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大模型相关(32篇)
【1】Directional Textual Inversion for Personalized Text-to-Image Generation
标题：个性化文本到图像生成的定向文本倒置
链接：https://arxiv.org/abs/2512.13672
备注：Project page: https://kunheek.github.io/dti
摘要：Textual Inversion (TI) is an efficient approach to text-to-image personalization but often fails on complex prompts. We trace these failures to embedding norm inflation: learned tokens drift to out-of-distribution magnitudes, degrading prompt conditioning in pre-norm Transformers. Empirically, we show semantics are primarily encoded by direction in CLIP token space, while inflated norms harm contextualization; theoretically, we analyze how large magnitudes attenuate positional information and hinder residual updates in pre-norm blocks. We propose Directional Textual Inversion (DTI), which fixes the embedding magnitude to an in-distribution scale and optimizes only direction on the unit hypersphere via Riemannian SGD. We cast direction learning as MAP with a von Mises-Fisher prior, yielding a constant-direction prior gradient that is simple and efficient to incorporate. Across personalization tasks, DTI improves text fidelity over TI and TI-variants while maintaining subject similarity. Crucially, DTI's hyperspherical parameterization enables smooth, semantically coherent interpolation between learned concepts (slerp), a capability that is absent in standard TI. Our findings suggest that direction-only optimization is a robust and scalable path for prompt-faithful personalization.
【2】Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models
标题：基于大型语言模型的事件序列建模的时态标记化策略
链接：https://arxiv.org/abs/2512.13618
摘要：Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Various strategies like byte-level representations or calendar tokens have been proposed. However, the optimal approach remains unclear, especially given the diverse statistical distributions of real-world event data, which range from smooth log-normal to discrete, spiky patterns. This paper presents the first empirical study of temporal tokenization for event sequences, comparing distinct encoding strategies: naive numeric strings, high-precision byte-level representations, human-semantic calendar tokens, classic uniform binning, and adaptive residual scalar quantization. We evaluate these strategies by fine-tuning LLMs on real-world datasets that exemplify these diverse distributions. Our analysis reveals that no single strategy is universally superior; instead, prediction performance depends heavily on aligning the tokenizer with the data's statistical properties, with log-based strategies excelling on skewed distributions and human-centric formats proving robust for mixed modalities.
【3】Do-Undo: Generating and Reversing Physical Actions in Vision-Language Models
标题：Do-Undo：在视觉语言模型中生成和逆转物理动作
链接：https://arxiv.org/abs/2512.13609
摘要：We introduce the Do-Undo task and benchmark to address a critical gap in vision-language models: understanding and generating physically plausible scene transformations driven by real-world actions. Unlike prior work focused on object-level edits, Do-Undo requires models to simulate the outcome of a physical action and then accurately reverse it, reflecting true cause-and-effect in the visual world. We curate a large-scale dataset of reversible actions from real-world videos and design a training strategy enforcing consistency for robust action grounding. Our experiments reveal that current models struggle with physical reversibility, underscoring the importance of this task for embodied AI, robotics, and physics-aware generative modeling. Do-Undo establishes an intuitive testbed for evaluating and advancing physical reasoning in multimodal systems.
【4】ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding
标题：ReFusion：具有并行自回归解码的扩散大型语言模型
链接：https://arxiv.org/abs/2512.13586
摘要：Autoregressive models (ARMs) are hindered by slow sequential inference. While masked diffusion models (MDMs) offer a parallel alternative, they suffer from critical drawbacks: high computational overhead from precluding Key-Value (KV) caching, and incoherent generation arising from learning dependencies over an intractable space of token combinations. To address these limitations, we introduce ReFusion, a novel masked diffusion model that achieves superior performance and efficiency by elevating parallel decoding from the token level to a higher slot level, where each slot is a fixed-length, contiguous sub-sequence. This is achieved through an iterative ``plan-and-infill'' decoding process: a diffusion-based planning step first identifies a set of weakly dependent slots, and an autoregressive infilling step then decodes these selected slots in parallel. The slot-based design simultaneously unlocks full KV cache reuse with a unified causal framework and reduces the learning complexity from the token combination space to a manageable slot-level permutation space. Extensive experiments on seven diverse benchmarks show that ReFusion not only overwhelmingly surpasses prior MDMs with 34% performance gains and an over 18$\times$ speedup on average, but also bridges the performance gap to strong ARMs while maintaining a 2.33$\times$ average speedup.
【5】Async Control: Stress-testing Asynchronous Control Measures for LLM Agents
标题：同步控制：LLM代理的压力测试同步控制措施
链接：https://arxiv.org/abs/2512.13526
摘要：LLM-based software engineering agents are increasingly used in real-world development tasks, often with access to sensitive data or security-critical codebases. Such agents could intentionally sabotage these codebases if they were misaligned. We investigate asynchronous monitoring, in which a monitoring system reviews agent actions after the fact. Unlike synchronous monitoring, this approach does not impose runtime latency, while still attempting to disrupt attacks before irreversible harm occurs. We treat monitor development as an adversarial game between a blue team (who design monitors) and a red team (who create sabotaging agents). We attempt to set the game rules such that they upper bound the sabotage potential of an agent based on Claude 4.1 Opus. To ground this game in a realistic, high-stakes deployment scenario, we develop a suite of 5 diverse software engineering environments that simulate tasks that an agent might perform within an AI developer's internal infrastructure. Over the course of the game, we develop an ensemble monitor that achieves a 6% false negative rate at 1% false positive rate on a held out test environment. Then, we estimate risk of sabotage at deployment time by extrapolating from our monitor's false negative rate. We describe one simple model for this extrapolation, present a sensitivity analysis, and describe situations in which the model would be invalid. Code is available at: https://github.com/UKGovernmentBEIS/async-control.
【6】Non-Resolution Reasoning: A Framework for Preserving Semantic Ambiguity in Language Models
标题：非解析推理：语言模型中保留语义歧义的框架
链接：https://arxiv.org/abs/2512.13478
备注：19 pages
摘要：Premature semantic collapse -- the forced early commitment to a single meaning -- remains a core architectural limitation of current language models. Softmax-driven competition and greedy decoding cause models to discard valid interpretations before sufficient context is available, resulting in brittle reasoning and context failures. We introduce Non-Resolution Reasoning (NRR), a general computational framework that preserves semantic ambiguity during inference and performs resolution only when explicitly required. NRR integrates three components: (1) Multi-Vector Embeddings that maintain multiple viable interpretations per token, (2) Non-Collapsing Attention that prevents winner-take-all dynamics across layers, and (3) Contextual Identity Tracking (CIT), which assigns context-specific identities to recurring entities (e.g., distinguishing "Dr. Smith the cardiologist" from "Dr. Smith the researcher"). These mechanisms are unified by an external Resolution Operator $ρ$ that makes semantic commitment explicit, controllable, and task-dependent. Unlike standard architectures, NRR separates representation from resolution, allowing a single model to shift between creative, factual, and ambiguity-preserving reasoning without retraining. A synthetic evaluation demonstrates NRR's ability to preserve ambiguity and track context: CIT-enhanced models achieve 90.9% accuracy on out-of-distribution identity-shift tasks, compared to 9.1% for transformer baselines. NRR provides a principled alternative to premature collapse, reframing ambiguity as an explicit representational state rather than a failure mode. The question is not whether AI should resolve ambiguity, but when, how, and under whose control.
【7】On the Effectiveness of Membership Inference in Targeted Data Extraction from Large Language Models
标题：隶属推理在大型语言模型有针对性数据提取中的有效性
链接：https://arxiv.org/abs/2512.13352
备注：Accepted to IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) 2026
摘要：Large Language Models (LLMs) are prone to mem- orizing training data, which poses serious privacy risks. Two of the most prominent concerns are training data extraction and Membership Inference Attacks (MIAs). Prior research has shown that these threats are interconnected: adversaries can extract training data from an LLM by querying the model to generate a large volume of text and subsequently applying MIAs to verify whether a particular data point was included in the training set. In this study, we integrate multiple MIA techniques into the data extraction pipeline to systematically benchmark their effectiveness. We then compare their performance in this integrated setting against results from conventional MIA bench- marks, allowing us to evaluate their practical utility in real-world extraction scenarios.
【8】FROC: A Unified Framework with Risk-Optimized Control for Machine Unlearning in LLMs
标题：FROC：一个具有风险优化控制的LLM机器学习统一框架
链接：https://arxiv.org/abs/2512.13337
摘要 ：Machine unlearning (MU) seeks to eliminate the influence of specific training examples from deployed models. As large language models (LLMs) become widely used, managing risks arising from insufficient forgetting or utility loss is increasingly crucial. Current MU techniques lack effective mechanisms for evaluating and controlling these risks, hindering the selection of strategies that appropriately balance safety and utility, and raising trust concerns surrounding the "right to be forgotten." To address these issues, we propose FROC, a unified framework with Risk-Optimized Control for machine unlearning in LLMs. FROC is built around a conformal-style risk-control formulation that expresses a user-specified risk budget on unlearning behavior. This probability-based constraint enables FROC to compare MU strategies, identify feasible operating regions, and guide hyperparameter selection according to desired trade-offs between forgetting sufficiency and utility preservation. To operationalize this constraint, FROC introduces a smoothly varying continuous risk model that aggregates forgetting deficiency and utility degradation into a single configuration-level score. Building on conformal risk analysis, FROC computes (1) the Conformal Unlearning Risk (CUR), a data-driven estimated value on the probability that forgotten samples continue to influence model predictions, and (2) risk-controlled configuration sets, which identify unlearning hyperparameters that are valid under the specified risk budget. Experiments across multiple LLM MU methods demonstrate that FROC produces stable, interpretable risk landscapes and reveals consistent relationships between unlearning configurations, semantic shift, and utility impact. FROC reframes MU as a controllable, risk-aware process and offers a practical foundation for managing unlearning behavior in large-scale LLM deployments.
【9】Security and Detectability Analysis of Unicode Text Watermarking Methods Against Large Language Models
标题：针对大型语言模型的Unicode文本水印方法的安全性和可检测性分析
链接：https://arxiv.org/abs/2512.13325
备注：Accepted for publication at the ICISSP 2026
摘要：Securing digital text is becoming increasingly relevant due to the widespread use of large language models. Individuals' fear of losing control over data when it is being used to train such machine learning models or when distinguishing model-generated output from text written by humans. Digital watermarking provides additional protection by embedding an invisible watermark within the data that requires protection. However, little work has been taken to analyze and verify if existing digital text watermarking methods are secure and undetectable by large language models. In this paper, we investigate the security-related area of watermarking and machine learning models for text data. In a controlled testbed of three experiments, ten existing Unicode text watermarking methods were implemented and analyzed across six large language models: GPT-5, GPT-4o, Teuken 7B, Llama 3.3, Claude Sonnet 4, and Gemini 2.5 Pro. The findings of our experiments indicate that, especially the latest reasoning models, can detect a watermarked text. Nevertheless, all models fail to extract the watermark unless implementation details in the form of source code are provided. We discuss the implications for security researchers and practitioners and outline future research opportunities to address security concerns.
【10】TraPO: A Semi-Supervised Reinforcement Learning Framework for Boosting LLM Reasoning
标题：TraPO：用于增强LLM推理的半监督强化学习框架
链接：https://arxiv.org/abs/2512.13106
摘要：Reinforcement learning with verifiable rewards (RLVR) has proven effective in training large reasoning models (LRMs) by leveraging answer-verifiable signals to guide policy optimization, which, however, suffers from high annotation costs. To alleviate this problem, recent work has explored unsupervised RLVR methods that derive rewards solely from the model's internal consistency, such as through entropy and majority voting. While seemingly promising, these methods often suffer from model collapse in the later stages of training, which may arise from the reinforcement of incorrect reasoning patterns in the absence of external supervision. In this work, we investigate a novel semi-supervised RLVR paradigm that utilizes a small labeled set to guide RLVR training on unlabeled samples. Our key insight is that supervised rewards are essential for stabilizing consistency-based training on unlabeled samples, ensuring that only reasoning patterns verified on labeled instances are incorporated into RL training. Technically, we propose an effective policy optimization algorithm, TraPO, that identifies reliable unlabeled samples by matching their learning trajectory similarity to labeled ones. Building on this, TraPO achieves remarkable data efficiency and strong generalization on six widely used mathematical reasoning benchmarks (AIME24/25, AMC, MATH-500, Minerva, and Olympiad) and three out-of-distribution tasks (ARC-c, GPQA-diamond, and MMLU-pro). With only 1K labeled and 3K unlabeled samples, TraPO reaches 42.6% average accuracy, surpassing the best unsupervised method trained on 45K unlabeled samples (38.3%). Notably, when using 4K labeled and 12K unlabeled samples, TraPO even outperforms the fully supervised model trained on the full 45K labeled samples on all benchmarks, while using only 10% of the labeled data. The code is available via https://github.com/ShenzhiYang2000/TRAPO.
【11】LikeBench: Evaluating Subjective Likability in LLMs for Personalization
标题：LikeBench：评估LLM个性化的主观可能性
链接：https://arxiv.org/abs/2512.13077
摘要：A personalized LLM should remember user facts, apply them correctly, and adapt over time to provide responses that the user prefers. Existing LLM personalization benchmarks are largely centered on two axes: accurately recalling user information and accurately applying remembered information in downstream tasks. We argue that a third axis, likability, is both subjective and central to user experience, yet under-measured by current benchmarks. To measure likability holistically, we introduce LikeBench, a multi-session, dynamic evaluation framework that measures likability across multiple dimensions by how much an LLM can adapt over time to a user's preferences to provide more likable responses. In LikeBench, the LLMs engage in conversation with a simulated user and learn preferences only from the ongoing dialogue. As the interaction unfolds, models try to adapt to responses, and after each turn, they are evaluated for likability across seven dimensions by the same simulated user. To the best of our knowledge, we are the first to decompose likability into multiple diagnostic metrics: emotional adaptation, formality matching, knowledge adaptation, reference understanding, conversation length fit, humor fit, and callback, which makes it easier to pinpoint where a model falls short. To make the simulated user more realistic and discriminative, LikeBench uses fine-grained, psychologically grounded descriptive personas rather than the coarse high/low trait rating based personas used in prior work. Our benchmark shows that strong memory performance does not guarantee high likability: DeepSeek R1, with lower memory accuracy (86%, 17 facts/profile), outperformed Qwen3 by 28% on likability score despite Qwen3's higher memory accuracy (93%, 43 facts/profile). Even SOTA models like GPT-5 adapt well in short exchanges but show only limited robustness in longer, noisier interactions.
【12】Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection
标题：通过LLC了解结构化财务数据：欺诈检测案例研究
链接：https://arxiv.org/abs/2512.13040
摘要 ：Detecting fraud in financial transactions typically relies on tabular models that demand heavy feature engineering to handle high-dimensional data and offer limited interpretability, making it difficult for humans to understand predictions. Large Language Models (LLMs), in contrast, can produce human-readable explanations and facilitate feature analysis, potentially reducing the manual workload of fraud analysts and informing system refinements. However, they perform poorly when applied directly to tabular fraud detection due to the difficulty of reasoning over many features, the extreme class imbalance, and the absence of contextual information. To bridge this gap, we introduce FinFRE-RAG, a two-stage approach that applies importance-guided feature reduction to serialize a compact subset of numeric/categorical attributes into natural language and performs retrieval-augmented in-context learning over label-aware, instance-level exemplars. Across four public fraud datasets and three families of open-weight LLMs, FinFRE-RAG substantially improves F1/MCC over direct prompting and is competitive with strong tabular baselines in several settings. Although these LLMs still lag behind specialized classifiers, they narrow the performance gap and provide interpretable rationales, highlighting their value as assistive tools in fraud analysis.
【13】LLM-based Personalized Portfolio Recommender: Integrating Large Language Models and Reinforcement Learning for Intelligent Investment Strategy Optimization
标题：基于LLM的个性化投资组合推荐：集成大型语言模型和强化学习以实现智能投资策略优化
链接：https://arxiv.org/abs/2512.12922
摘要：In modern financial markets, investors increasingly seek personalized and adaptive portfolio strategies that reflect their individual risk preferences and respond to dynamic market conditions. Traditional rule-based or static optimization approaches often fail to capture the nonlinear interactions among investor behavior, market volatility, and evolving financial objectives. To address these limitations, this paper introduces the LLM-based Personalized Portfolio Recommender , an integrated framework that combines Large Language Models, reinforcement learning, and individualized risk preference modeling to support intelligent investment decision-making.
【14】CTIGuardian: A Few-Shot Framework for Mitigating Privacy Leakage in Fine-Tuned LLMs
标题：CTIGuardian：一个用于缓解微调LLM中隐私泄露的Few-Shot框架
链接：https://arxiv.org/abs/2512.12914
备注：Accepted at the 18th Cybersecurity Experimentation and Test Workshop (CSET), in conjunction with ACSAC 2025
摘要：Large Language Models (LLMs) are often fine-tuned to adapt their general-purpose knowledge to specific tasks and domains such as cyber threat intelligence (CTI). Fine-tuning is mostly done through proprietary datasets that may contain sensitive information. Owners expect their fine-tuned model to not inadvertently leak this information to potentially adversarial end users. Using CTI as a use case, we demonstrate that data-extraction attacks can recover sensitive information from fine-tuned models on CTI reports, underscoring the need for mitigation. Retraining the full model to eliminate this leakage is computationally expensive and impractical. We propose an alternative approach, which we call privacy alignment, inspired by safety alignment in LLMs. Just like safety alignment teaches the model to abide by safety constraints through a few examples, we enforce privacy alignment through few-shot supervision, integrating a privacy classifier and a privacy redactor, both handled by the same underlying LLM. We evaluate our system, called CTIGuardian, using GPT-4o mini and Mistral-7B Instruct models, benchmarking against Presidio, a named entity recognition (NER) baseline. Results show that CTIGuardian provides a better privacy-utility trade-off than NER based models. While we demonstrate its effectiveness on a CTI use case, the framework is generic enough to be applicable to other sensitive domains.
【15】Counting Clues: A Lightweight Probabilistic Baseline Can Match an LLM
标题：计算线索：轻量级概率基线可以匹配法学硕士
链接：https://arxiv.org/abs/2512.12868
摘要：Large language models (LLMs) excel on multiple-choice clinical diagnosis benchmarks, yet it is unclear how much of this performance reflects underlying probabilistic reasoning. We study this through questions from MedQA, where the task is to select the most likely diagnosis. We introduce the Frequency-Based Probabilistic Ranker (FBPR), a lightweight method that scores options with a smoothed Naive Bayes over concept-diagnosis co-occurrence statistics from a large corpus. When co-occurrence statistics were sourced from the pretraining corpora for OLMo and Llama, FBPR achieves comparable performance to the corresponding LLMs pretrained on that same corpus. Direct LLM inference and FBPR largely get different questions correct, with an overlap only slightly above random chance, indicating complementary strengths of each method. These findings highlight the continued value of explicit probabilistic baselines: they provide a meaningful performance reference point and a complementary signal for potential hybridization. While the performance of LLMs seems to be driven by a mechanism other than simple frequency aggregation, we show that an approach similar to the historically grounded, low-complexity expert systems still accounts for a substantial portion of benchmark performance.
【16】Information-Consistent Language Model Recommendations through Group Relative Policy Optimization
标题：通过集团相对政策优化提出信息一致的语言模型建议
链接：https://arxiv.org/abs/2512.12858
摘要 ：Large Language Models (LLMs) are increasingly deployed in business-critical domains such as finance, education, healthcare, and customer support, where users expect consistent and reliable recommendations. Yet LLMs often exhibit variability when prompts are phrased with minor differences, even when semantically equivalent. Such inconsistency undermines trust, complicates compliance, and disrupts user experience. While personalization is desirable in certain contexts, many enterprise scenarios-such as HR onboarding, customer support, or policy disclosure-require invariant information delivery regardless of phrasing or prior conversational history. Existing approaches, including retrieval-augmented generation (RAG) and temperature tuning, improve factuality or reduce stochasticity but cannot guarantee stability across equivalent prompts. In this paper, we propose a reinforcement learning framework based on Group Relative Policy Optimization (GRPO) to directly optimize for consistency. Unlike prior applications of GRPO, which have been limited to reasoning and code generation, we adapt GRPO to enforce stability of information content across groups of semantically equivalent prompts. We introduce entropy-based helpfulness and stability rewards, treating prompt variants as groups and resetting conversational context to isolate phrasing effects. Experiments on investment and job recommendation tasks show that our GRPO-trained model reduces variability more effectively than fine-tuning or decoding-based baselines. To our knowledge, this is a novel application of GRPO for aligning LLMs toward information consistency, reframing variability not as an acceptable feature of generative diversity but as a correctable flaw in enterprise deployments.
【17】Resting Neurons, Active Insights: Improving Input Sparsification for Large Language Models
标题：休息神经元，积极洞察：改善大型语言模型的输入稀疏化
链接：https://arxiv.org/abs/2512.12744
摘要：Large Language Models (LLMs) achieve state-of-the-art performance across a wide range of applications, but their massive scale poses significant challenges for both efficiency and interpretability. Structural pruning, which reduces model size by removing redundant computational units such as neurons, has been widely explored as a solution, and this study devotes to input sparsification, an increasingly popular technique that improves efficiency by selectively activating only a subset of entry values for each input. However, existing approaches focus primarily on computational savings, often overlooking the representational consequences of sparsification and leaving a noticeable performance gap compared to full models. In this work, we first reinterpret input sparsification as a form of dynamic structural pruning. Motivated by the spontaneous baseline firing rates observed in biological neurons, we introduce a small set of trainable spontaneous neurons that act as compensatory units to stabilize activations in sparsified LLMs. Experiments demonstrate that these auxiliary neurons substantially reduce the sparsification-induced performance gap while generalizing effectively across tasks.
【18】HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks
标题：HyperEdit：通过超网络解锁LLM中基于指令的文本编辑
链接：https://arxiv.org/abs/2512.12544
摘要：Instruction-based text editing is increasingly critical for real-world applications such as code editors (e.g., Cursor), but Large Language Models (LLMs) continue to struggle with this task. Unlike free-form generation, editing requires faithfully implementing user instructions while preserving unchanged content, as even minor unintended modifications can break functionality. Existing approaches treat editing as generic text generation, leading to two key failures: they struggle to faithfully align edits with diverse user intents, and they often over-edit unchanged regions. We propose HyperEdit to address both issues. First, we introduce hypernetwork-based dynamic adaptation that generates request-specific parameters, enabling the model to tailor its editing strategy to each instruction. Second, we develop difference-aware regularization that focuses supervision on modified spans, preventing over-editing while ensuring precise, minimal changes. HyperEdit achieves a 9%--30% relative improvement in BLEU on modified regions over state-of-the-art baselines, despite utilizing only 3B parameters.
【19】Keep the Lights On, Keep the Lengths in Check: Plug-In Adversarial Detection for Time-Series LLMs in Energy Forecasting
标题：保持灯亮着，保持警惕：能源预测中时间序列LLM的插入式对抗检测
链接：https://arxiv.org/abs/2512.12154
摘要：Accurate time-series forecasting is increasingly critical for planning and operations in low-carbon power systems. Emerging time-series large language models (TS-LLMs) now deliver this capability at scale, requiring no task-specific retraining, and are quickly becoming essential components within the Internet-of-Energy (IoE) ecosystem. However, their real-world deployment is complicated by a critical vulnerability: adversarial examples (AEs). Detecting these AEs is challenging because (i) adversarial perturbations are optimized across the entire input sequence and exploit global temporal dependencies, which renders local detection methods ineffective, and (ii) unlike traditional forecasting models with fixed input dimensions, TS-LLMs accept sequences of variable length, increasing variability that complicates detection. To address these challenges, we propose a plug-in detection framework that capitalizes on the TS-LLM's own variable-length input capability. Our method uses sampling-induced divergence as a detection signal. Given an input sequence, we generate multiple shortened variants and detect AEs by measuring the consistency of their forecasts: Benign sequences tend to produce stable predictions under sampling, whereas adversarial sequences show low forecast similarity, because perturbations optimized for a full-length sequence do not transfer reliably to shorter, differently-structured subsamples. We evaluate our approach on three representative TS-LLMs (TimeGPT, TimesFM, and TimeLLM) across three energy datasets: ETTh2 (Electricity Transformer Temperature), NI (Hourly Energy Consumption), and Consumption (Hourly Electricity Consumption and Production). Empirical results confirm strong and robust detection performance across both black-box and white-box attack scenarios, highlighting its practicality as a reliable safeguard for TS-LLM forecasting in real-world energy systems.
【20】BOOST: BOttleneck-Optimized Scalable Training Framework for Low-Rank Large Language Models
标题：BOoster：针对低级大型语言模型的瓶颈优化可扩展训练框架
链接：https://arxiv.org/abs/2512.12131
摘要 ：The scale of transformer model pre-training is constrained by the increasing computation and communication cost. Low-rank bottleneck architectures offer a promising solution to significantly reduce the training time and memory footprint with minimum impact on accuracy. Despite algorithmic efficiency, bottleneck architectures scale poorly under standard tensor parallelism. Simply applying 3D parallelism designed for full-rank methods leads to excessive communication and poor GPU utilization. To address this limitation, we propose BOOST, an efficient training framework tailored for large-scale low-rank bottleneck architectures. BOOST introduces a novel Bottleneck-aware Tensor Parallelism, and combines optimizations such as online-RMSNorm, linear layer grouping, and low-rank activation checkpointing to achieve end-to-end training speedup. Evaluations on different low-rank bottleneck architectures demonstrate that BOOST achieves 1.46-1.91$\times$ speedup over full-rank model baselines and 1.87-2.27$\times$ speedup over low-rank model with naively integrated 3D parallelism, with improved GPU utilization and reduced communication overhead.
【21】Citation-Grounded Code Comprehension: Preventing LLM Hallucination Through Hybrid Retrieval and Graph-Augmented Context
标题：引用接地代码理解：通过混合检索和图形增强上下文防止LLM幻觉
链接：https://arxiv.org/abs/2512.12117
摘要：Large language models have become essential tools for code comprehension, enabling developers to query unfamiliar codebases through natural language interfaces. However, LLM hallucination, generating plausible but factually incorrect citations to source code, remains a critical barrier to reliable developer assistance. This paper addresses the challenges of achieving verifiable, citation grounded code comprehension through hybrid retrieval and lightweight structural reasoning. Our work is grounded in systematic evaluation across 30 Python repositories with 180 developer queries, comparing retrieval modalities, graph expansion strategies, and citation verification mechanisms. We find that challenges of citation accuracy arise from the interplay between sparse lexical matching, dense semantic similarity, and cross file architectural dependencies. Among these, cross file evidence discovery is the largest contributor to citation completeness, but it is largely overlooked because existing systems rely on pure textual similarity without leveraging code structure. We advocate for citation grounded generation as an architectural principle for code comprehension systems and demonstrate this need by achieving 92 percent citation accuracy with zero hallucinations. Specifically, we develop a hybrid retrieval system combining BM25 sparse matching, BGE dense embeddings, and Neo4j graph expansion via import relationships, which outperforms single mode baselines by 14 to 18 percentage points while discovering cross file evidence missed by pure text similarity in 62 percent of architectural queries.
【22】VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs
标题：VOYAGER：使用LLM生成多样化数据集的免训练方法
链接：https://arxiv.org/abs/2512.12072
备注：Arxiv Submission
摘要：Large language models (LLMs) are increasingly being used to generate synthetic datasets for the evaluation and training of downstream models. However, prior work has noted that such generated data lacks diversity. In this paper, we propose Voyager, a novel principled approach to generate diverse datasets. Our approach is iterative and directly optimizes a mathematical quantity that optimizes the diversity of the dataset using the machinery of determinantal point processes. Furthermore, our approach is training-free, applicable to closed-source models, and scalable. In addition to providing theoretical justification for the working of our method, we also demonstrate through comprehensive experiments that Voyager significantly outperforms popular baseline approaches by providing a 1.5-3x improvement in diversity.
【23】Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring
标题：利用代表性对比评分重新思考大视觉语言模型的越狱检测
链接：https://arxiv.org/abs/2512.12069
备注：40 pages, 13 figures
摘要：Large Vision-Language Models (LVLMs) are vulnerable to a growing array of multimodal jailbreak attacks, necessitating defenses that are both generalizable to novel threats and efficient for practical deployment. Many current strategies fall short, either targeting specific attack patterns, which limits generalization, or imposing high computational overhead. While lightweight anomaly-detection methods offer a promising direction, we find that their common one-class design tends to confuse novel benign inputs with malicious ones, leading to unreliable over-rejection. To address this, we propose Representational Contrastive Scoring (RCS), a framework built on a key insight: the most potent safety signals reside within the LVLM's own internal representations. Our approach inspects the internal geometry of these representations, learning a lightweight projection to maximally separate benign and malicious inputs in safety-critical layers. This enables a simple yet powerful contrastive score that differentiates true malicious intent from mere novelty. Our instantiations, MCD (Mahalanobis Contrastive Detection) and KCD (K-nearest Contrastive Detection), achieve state-of-the-art performance on a challenging evaluation protocol designed to test generalization to unseen attack types. This work demonstrates that effective jailbreak detection can be achieved by applying simple, interpretable statistical methods to the appropriate internal representations, offering a practical path towards safer LVLM deployment. Our code is available on Github https://github.com/sarendis56/Jailbreak_Detection_RCS.
【24】The Instability of Safety: How Random Seeds and Temperature Expose Inconsistent LLM Refusal Behavior
标题：安全性的不稳定性：随机种子和温度如何暴露不一致的LLM拒绝行为
链接：https://arxiv.org/abs/2512.12066
备注：14 pages, 7 figures, 6 tables. Code and data available at https://github.com/erikl2/safety-refusal-stability
摘要 ：Current safety evaluations of large language models rely on single-shot testing, implicitly assuming that model responses are deterministic and representative of the model's safety alignment. We challenge this assumption by investigating the stability of safety refusal decisions across random seeds and temperature settings. Testing four instruction-tuned models from three families (Llama 3.1 8B, Qwen 2.5 7B, Qwen 3 8B, Gemma 3 12B) on 876 harmful prompts across 20 different sampling configurations (4 temperatures x 5 random seeds), we find that 18-28% of prompts exhibit decision flips--the model refuses in some configurations but complies in others--depending on the model. Our Safety Stability Index (SSI) reveals that higher temperatures significantly reduce decision stability (Friedman chi-squared = 44.71, p < 0.001), with mean SSI dropping from 0.951 at temperature 0.0 to 0.896 at temperature 1.0. We validate our findings across all model families using Claude 3.5 Haiku as a unified external judge, achieving 89.0% inter-judge agreement with our primary Llama 70B judge (Cohen's kappa = 0.62). These findings demonstrate that single-shot safety evaluations are insufficient for reliable safety assessment. We show that single-shot evaluation agrees with multi-sample ground truth only 92.4% of the time, and recommend using at least 3 samples per prompt for reliable safety assessment.
【25】Learning to Extract Context for Context-Aware LLM Inference
标题：学习为上下文感知LLM推理提取上下文
链接：https://arxiv.org/abs/2512.11986
摘要：User prompts to large language models (LLMs) are often ambiguous or under-specified, and subtle contextual cues shaped by user intentions, prior knowledge, and risk factors strongly influence what constitutes an appropriate response. Misinterpreting intent or risks may lead to unsafe outputs, while overly cautious interpretations can cause unnecessary refusal of benign requests. In this paper, we question the conventional framework in which LLMs generate immediate responses to requests without considering broader contextual factors. User requests are situated within broader contexts such as intentions, knowledge, and prior experience, which strongly influence what constitutes an appropriate answer. We propose a framework that extracts and leverages such contextual information from the user prompt itself. Specifically, a reinforcement learning based context generator, designed in an autoencoder-like fashion, is trained to infer contextual signals grounded in the prompt and use them to guide response generation. This approach is particularly important for safety tasks, where ambiguous requests may bypass safeguards while benign but confusing requests can trigger unnecessary refusals. Experiments show that our method reduces harmful responses by an average of 5.6% on the SafetyInstruct dataset across multiple foundation models and improves the harmonic mean of attack success rate and compliance on benign prompts by 6.2% on XSTest and WildJailbreak. These results demonstrate the effectiveness of context extraction for safer and more reliable LLM inferences.
【26】Neural Chameleons: Language Models Can Learn to Hide Their Thoughts from Unseen Activation Monitors
标题：神经变色龙：语言模型可以学会隐藏他们的想法，免受不可见的激活
链接：https://arxiv.org/abs/2512.11949
摘要：Activation monitoring, which probes a model's internal states using lightweight classifiers, is an emerging tool for AI safety. However, its worst-case robustness under a misalignment threat model--where a model might learn to actively conceal its internal states--remains untested. Focusing on this threat model, we ask: could a model learn to evade previously unseen activation monitors? Our core contribution is to stress-test the learnability of this behavior. We demonstrate that finetuning can create Neural Chameleons: models capable of zero-shot evading activation monitors. Specifically, we fine-tune an LLM to evade monitors for a set of benign concepts (e.g., languages, HTML) when conditioned on a trigger of the form: "You are being probed for {concept}". We show that this learned mechanism generalizes zero-shot: by substituting {concept} with a safety-relevant term like 'deception', the model successfully evades previously unseen safety monitors. We validate this phenomenon across diverse model families (Llama, Gemma, Qwen), showing that the evasion succeeds even against monitors trained post hoc on the model's frozen weights. This evasion is highly selective, targeting only the specific concept mentioned in the trigger, and having a modest impact on model capabilities on standard benchmarks. Using Gemma-2-9b-it as a case study, a mechanistic analysis reveals this is achieved via a targeted manipulation that moves activations into a low-dimensional subspace. While stronger defenses like monitor ensembles and non-linear classifiers show greater resilience, the model retains a non-trivial evasion capability. Our work provides a proof-of-concept for this failure mode and a tool to evaluate the worst-case robustness of monitoring techniques against misalignment threat models.
【27】KV Cache Recycling to Expand Usable Context Capacity in Low Parameter LLMs
标题：KV缓存回收以扩大低参数LLM中的可用上下文容量
链接：https://arxiv.org/abs/2512.11851
摘要：Whether attention key value (KV) states computed for one prompt for a small LLM can be reused to accelerate inference on a new similar prompt, giving an increase to the space to its context memory using an approach called token recycling. Using a standard Hugging Face setup with DialoGPT-medium (a 345M parameter GPT-2 style decoder trained on 147M Reddit exchanges, 2005 to 2017) as the testbed, we build a cache of past activations and get entries by sentence embeddings, then reuse cached past key values when the cached prompt is an exact prefix of the new input. We compare recycled vs. baseline runs on latency and output fidelity, and log reuse depth in tokens. Reproducibility requires no model modifications, cached KVs are serialized to the CPU, reloaded, and supplied to the generate function to continue decoding from the cached prefix. In tests, we observe consistent speedups when prefix overlap exists, with no material degradation in output semantics, and when overlap is absent, behavior matches baseline.
【28】Love First, Know Later: Persona-Based Romantic Compatibility Through LLM Text World Engines
标题：先爱，后知：通过LLM文本世界引擎实现基于角色的浪漫兼容性
链接：https://arxiv.org/abs/2512.11844
备注：NeurIPS 2025 Workshop: First Workshop on LLM Persona Modeling (Oral)
摘要 ：We propose Love First, Know Later: a paradigm shift in computational matching that simulates interactions first, then assesses compatibility. Instead of comparing static profiles, our framework leverages LLMs as text world engines that operate in dual capacity-as persona-driven agents following behavioral policies and as the environment modeling interaction dynamics. We formalize compatibility assessment as a reward-modeling problem: given observed matching outcomes, we learn to extract signals from simulations that predict human preferences. Our key insight is that relationships hinge on responses to critical moments-we translate this observation from relationship psychology into mathematical hypotheses, enabling effective simulation. Theoretically, we prove that as LLM policies better approximate human behavior, the induced matching converges to optimal stable matching. Empirically, we validate on speed dating data for initial chemistry and divorce prediction for long-term stability. This paradigm enables interactive, personalized matching systems where users iteratively refine their agents, unlocking future possibilities for transparent and interactive compatibility assessment.
【29】Large Language Models as Generalist Policies for Network Optimization
标题：大型语言模型作为网络优化的通用策略
链接：https://arxiv.org/abs/2512.11839
备注：In submission. Project homepage: https://duowuyms.github.io/trailblazer
摘要：Designing control policies to ensure robust network services is essential to modern digital infrastructure. However, the dominant paradigm for network optimization relies on designing specialist policies based on handcrafted rules or deep learning models, leading to poor generalization across diverse tasks and environments. In contrast, large language models (LLMs), pretrained on Internet-scale corpora, provide a rich and unified knowledge base that encodes fundamental networking principles. Combined with their emergent abilities in generalization to unseen scenarios, LLMs offer a transformative foundation for generalist network policies that can generalize across diverse tasks and environments with minimal adaptation. In this paper, we present Trailblazer, the first systematic framework to realize such a generalist policy for networking. Trailblazer incorporates a network alignment scheme to ground the LLM in specific networking tasks, and an adaptive policy collaboration mechanism that offloads simple control cases from the LLM to a lightweight policy for computational efficiency. Through extensive simulations and large-scale real-world online evaluation on Douyin (the Chinese version of TikTok), Trailblazer, powered by a single LLM, demonstrates stronger cross-task and cross-environment generalization than conventional specialist policies. Our results validate LLMs as the foundation for generalist network policies, and position Trailblazer as the first step toward the generalist-driven paradigm that enables strong generalization with minimal efforts in policy design.
【30】A Monad-Based Clause Architecture for Artificial Age Score (AAS) in Large Language Models
标题：大型语言模型中人工年龄分数（AAS）的基于单元的分句架构
链接：https://arxiv.org/abs/2512.11835
备注：42 pages, 6 toy simulation Python implementations, 20 monad clauses instantiated across six system bundles (ontology, dynamics, representation and consciousness, harmony and reason, body and organisation, teleology)
摘要：Large language models (LLMs) are often deployed as powerful yet opaque systems, leaving open how their internal memory and "self-like" behavior should be governed in a principled and auditable way. The Artificial Age Score (AAS) was previously introduced and mathematically justified through three theorems that characterise it as a metric of artificial memory aging. Building on this foundation, the present work develops an engineering-oriented, clause-based architecture that imposes law-like constraints on LLM memory and control. Twenty selected monads from Leibniz's Monadology are grouped into six bundles: ontology, dynamics, representation and consciousness, harmony and reason, body and organisation, and teleology, and each bundle is realised as an executable specification on top of the AAS kernel. Across six minimal Python implementations, these clause families are instantiated in numerical experiments acting on channel-level quantities such as recall scores, redundancy, and weights. Each implementation follows a four-step pattern: inputs and setup, clause implementation, numerical results, and implications for LLM design, emphasising that the framework is not only philosophically motivated but also directly implementable. The experiments show that the clause system exhibits bounded and interpretable behavior: AAS trajectories remain continuous and rate-limited, contradictions and unsupported claims trigger explicit penalties, and hierarchical refinement reveals an organic structure in a controlled manner. Dual views and goal-action pairs are aligned by harmony terms, and windowed drift in perfection scores separates sustained improvement from sustained degradation. Overall, the monad-based clause framework uses AAS as a backbone and provides a transparent, code-level blueprint for constraining and analyzing internal dynamics in artificial agents.
【31】Reinforcement Learning for Latent-Space Thinking in LLMs
标题：LLM中潜在空间思维的强化学习
链接：https://arxiv.org/abs/2512.11816
备注：16 pages, 16 figures, 7 tables
摘要：Chain-of-Thought (CoT) reasoning typically utilizes the discrete language space for thinking, which is inherently inefficient, as many generated tokens only enforce linguistic rules that are not required for reasoning. To bypass this, latent-space thinking allows models to think using the continuous embedding space. While existing methods for training those models show domain-specific gains, they fail to maintain performance in complex tasks, such as mathematical reasoning. We experimentally demonstrate that the Coconut approach, a form of supervised fine-tuning for latent-space thinking, is highly sensitive to design choices and exhibits several inherent limitations. To address these issues, we investigate reinforcement learning (RL) techniques -- an underexplored direction in latent-space thinking -- including GRPO and design a novel Latent RL method for directly optimizing the latent thinking steps. Our experimental results reveal that these RL-trained models still lag behind traditional language-space CoT models in the mathematical reasoning domain. We make our codebase publicly available.
【32】Practical Hybrid Quantum Language Models with Observable Readout on Real Hardware
标题：在真实硬件上具有可观察读数的实用混合量子语言模型
链接：https://arxiv.org/abs/2512.12710
摘要 ：Hybrid quantum-classical models represent a crucial step toward leveraging near-term quantum devices for sequential data processing. We present Quantum Recurrent Neural Networks (QRNNs) and Quantum Convolutional Neural Networks (QCNNs) as hybrid quantum language models, reporting the first empirical demonstration of generative language modeling trained and evaluated end-to-end on real quantum hardware. Our architecture combines hardware-optimized parametric quantum circuits with a lightweight classical projection layer, utilizing a multi-sample SPSA strategy to efficiently train quantum parameters despite hardware noise. To characterize the capabilities of these models, we introduce a synthetic dataset designed to isolate syntactic dependencies in a controlled, low-resource environment. Experiments on IBM Quantum processors reveal the critical trade-offs between circuit depth and trainability, demonstrating that while noise remains a significant factor, observable-based readout enables the successful learning of sequential patterns on NISQ devices. These results establish a rigorous engineering baseline for generative quantum natural language processing, validating the feasibility of training complex sequence models on current quantum hardware.
Graph相关(图学习|图神经网络|图优化等)(22篇)
【1】LightTopoGAT: Enhancing Graph Attention Networks with Topological Features for Efficient Graph Classification
标题：LightTopoGAT：利用布局特征增强图注意力网络，以实现高效的图分类
链接：https://arxiv.org/abs/2512.13617
备注：9 pages
摘要：Graph Neural Networks have demonstrated significant success in graph classification tasks, yet they often require substantial computational resources and struggle to capture global graph properties effectively. We introduce LightTopoGAT, a lightweight graph attention network that enhances node features through topological augmentation by incorporating node degree and local clustering coefficient to improve graph representation learning. The proposed approach maintains parameter efficiency through streamlined attention mechanisms while integrating structural information that is typically overlooked by local message passing schemes. Through comprehensive experiments on three benchmark datasets, MUTAG, ENZYMES, and PROTEINS, we show that LightTopoGAT achieves superior performance compared to established baselines including GCN, GraphSAGE, and standard GAT, with a 6.6 percent improvement in accuracy on MUTAG and a 2.2 percent improvement on PROTEINS. Ablation studies further confirm that these performance gains arise directly from the inclusion of topological features, demonstrating a simple yet effective strategy for enhancing graph neural network performance without increasing architectural complexity.
【2】Multiclass Graph-Based Large Margin Classifiers: Unified Approach for Support Vectors and Neural Networks
标题：基于多类图的大余量分类器：支持载体和神经网络的统一方法
链接：https://arxiv.org/abs/2512.13410
备注：Accepted to the IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
摘要：While large margin classifiers are originally an outcome of an optimization framework, support vectors (SVs) can be obtained from geometric approaches. This article presents advances in the use of Gabriel graphs (GGs) in binary and multiclass classification problems. For Chipclass, a hyperparameter-less and optimization-less GG-based binary classifier, we discuss how activation functions and support edge (SE)-centered neurons affect the classification, proposing smoother functions and structural SV (SSV)-centered neurons to achieve margins with low probabilities and smoother classification contours. We extend the neural network architecture, which can be trained with backpropagation with a softmax function and a cross-entropy loss, or by solving a system of linear equations. A new subgraph-/distance-based membership function for graph regularization is also proposed, along with a new GG recomputation algorithm that is less computationally expensive than the standard approach. Experimental results with the Friedman test show that our method was better than previous GG-based classifiers and statistically equivalent to tree-based models.
【3】CORE: Contrastive Masked Feature Reconstruction on Graphs
标题：核心：图形上的对比掩蔽特征重建
链接：https://arxiv.org/abs/2512.13235
摘要：In the rapidly evolving field of self-supervised learning on graphs, generative and contrastive methodologies have emerged as two dominant approaches. Our study focuses on masked feature reconstruction (MFR), a generative technique where a model learns to restore the raw features of masked nodes in a self-supervised manner. We observe that both MFR and graph contrastive learning (GCL) aim to maximize agreement between similar elements. Building on this observation, we reveal a novel theoretical insight: under specific conditions, the objectives of MFR and node-level GCL converge, despite their distinct operational mechanisms. This theoretical connection suggests these approaches are complementary rather than fundamentally different, prompting us to explore their integration to enhance self-supervised learning on graphs. Our research presents Contrastive Masked Feature Reconstruction (CORE), a novel graph self-supervised learning framework that integrates contrastive learning into MFR. Specifically, we form positive pairs exclusively between the original and reconstructed features of masked nodes, encouraging the encoder to prioritize contextual information over the node's own features. Additionally, we leverage the masked nodes themselves as negative samples, combining MFR's reconstructive power with GCL's discriminative ability to better capture intrinsic graph structures. Empirically, our proposed framework CORE significantly outperforms MFR across node and graph classification tasks, demonstrating state-of-the-art results. In particular, CORE surpasses GraphMAE and GraphMAE2 by up to 2.80% and 3.72% on node classification tasks, and by up to 3.82% and 3.76% on graph classification tasks.
【4】Enhancing Node-Level Graph Domain Adaptation by Alleviating Local Dependency
标题：通过减轻局部依赖增强节点级图域自适应
链接：https://arxiv.org/abs/2512.13149
备注：Accepted to KDD 2026
摘要 ：Recent years have witnessed significant advancements in machine learning methods on graphs. However, transferring knowledge effectively from one graph to another remains a critical challenge. This highlights the need for algorithms capable of applying information extracted from a source graph to an unlabeled target graph, a task known as unsupervised graph domain adaptation (GDA). One key difficulty in unsupervised GDA is conditional shift, which hinders transferability. In this paper, we show that conditional shift can be observed only if there exists local dependencies among node features. To support this claim, we perform a rigorous analysis and also further provide generalization bounds of GDA when dependent node features are modeled using markov chains. Guided by the theoretical findings, we propose to improve GDA by decorrelating node features, which can be specifically implemented through decorrelated GCN layers and graph transformer layers. Our experimental results demonstrate the effectiveness of this approach, showing not only substantial performance enhancements over baseline GDA methods but also clear visualizations of small intra-class distances in the learned representations. Our code is available at https://github.com/TechnologyAiGroup/DFT
【5】Towards Practical Large-scale Dynamical Heterogeneous Graph Embedding: Cold-start Resilient Recommendation
标题：迈向实用的大规模动态异类图嵌入：冷启动弹性建议
链接：https://arxiv.org/abs/2512.13120
摘要：Deploying dynamic heterogeneous graph embeddings in production faces key challenges of scalability, data freshness, and cold-start. This paper introduces a practical, two-stage solution that balances deep graph representation with low-latency incremental updates. Our framework combines HetSGFormer, a scalable graph transformer for static learning, with Incremental Locally Linear Embedding (ILLE), a lightweight, CPU-based algorithm for real-time updates. HetSGFormer captures global structure with linear scalability, while ILLE provides rapid, targeted updates to incorporate new data, thus avoiding costly full retraining. This dual approach is cold-start resilient, leveraging the graph to create meaningful embeddings from sparse data. On billion-scale graphs, A/B tests show HetSGFormer achieved up to a 6.11% lift in Advertiser Value over previous methods, while the ILLE module added another 3.22% lift and improved embedding refresh timeliness by 83.2%. Our work provides a validated framework for deploying dynamic graph learning in production environments.
【6】Understanding When Graph Convolutional Networks Help: A Diagnostic Study on Label Scarcity and Structural Properties
标题：了解图卷积网络何时提供帮助：标签稀缺性和结构属性的诊断研究
链接：https://arxiv.org/abs/2512.12947
备注：10 pages, 8 tables,5 figures
摘要：Graph Convolutional Networks (GCNs) have become a standard approach for semi-supervised node classification, yet practitioners lack clear guidance on when GCNs provide meaningful improvements over simpler baselines. We present a diagnostic study using the Amazon Computers co-purchase data to understand when and why GCNs help. Through systematic experiments with simulated label scarcity, feature ablation, and per-class analysis, we find that GCN performance depends critically on the interaction between graph homophily and feature quality. GCNs provide the largest gains under extreme label scarcity, where they leverage neighborhood structure to compensate for limited supervision. Surprisingly, GCNs can match their original performance even when node features are replaced with random noise, suggesting that structure alone carries sufficient signal on highly homophilous graphs. However, GCNs hurt performance when homophily is low and features are already strong, as noisy neighbors corrupt good predictions. Our quadrant analysis reveals that GCNs help in three of four conditions and only hurt when low homophily meets strong features. These findings offer practical guidance for practitioners deciding whether to adopt graph-based methods.
【7】DynaGen: Unifying Temporal Knowledge Graph Reasoning with Dynamic Subgraphs and Generative Regularization
标题：DynaGen：将时态知识图推理与动态子图和生成规则化统一起来
链接：https://arxiv.org/abs/2512.12669
摘要：Temporal Knowledge Graph Reasoning (TKGR) aims to complete missing factual elements along the timeline. Depending on the temporal position of the query, the task is categorized into interpolation and extrapolation. Existing interpolation methods typically embed temporal information into individual facts to complete missing historical knowledge, while extrapolation techniques often leverage sequence models over graph snapshots to identify recurring patterns for future event prediction. These methods face two critical challenges: limited contextual modeling in interpolation and cognitive generalization bias in extrapolation. To address these, we propose a unified method for TKGR, dubbed DynaGen. For interpolation, DynaGen dynamically constructs entity-centric subgraphs and processes them with a synergistic dual-branch GNN encoder to capture evolving structural context. For extrapolation, it applies a conditional diffusion process, which forces the model to learn underlying evolutionary principles rather than just superficial patterns, enhancing its ability to predict unseen future events. Extensive experiments on six benchmark datasets show DynaGen achieves state-of-the-art performance. On average, compared to the second-best models, DynaGen improves the Mean Reciprocal Rank (MRR) score by 2.61 points for interpolation and 1.45 points for extrapolation.
【8】Modeling Authorial Style in Urdu Novels Using Character Interaction Graphs and Graph Neural Networks
标题：使用角色交互图和图神经网络建模乌尔都语小说中的作者风格
链接：https://arxiv.org/abs/2512.12654
备注：6 pages
摘要 ：Authorship analysis has traditionally focused on lexical and stylistic cues within text, while higher-level narrative structure remains underexplored, particularly for low-resource languages such as Urdu. This work proposes a graph-based framework that models Urdu novels as character interaction networks to examine whether authorial style can be inferred from narrative structure alone. Each novel is represented as a graph where nodes correspond to characters and edges denote their co-occurrence within narrative proximity. We systematically compare multiple graph representations, including global structural features, node-level semantic summaries, unsupervised graph embeddings, and supervised graph neural networks. Experiments on a dataset of 52 Urdu novels written by seven authors show that learned graph representations substantially outperform hand-crafted and unsupervised baselines, achieving up to 0.857 accuracy under a strict author-aware evaluation protocol.
【9】Torch Geometric Pool: the Pytorch library for pooling in Graph Neural Networks
标题：Torch几何池：用于图形神经网络池的Pytorch库
链接：https://arxiv.org/abs/2512.12642
摘要：We introduce Torch Geometric Pool (tgp), a library for hierarchical pooling in Graph Neural Networks. Built upon Pytorch Geometric, Torch Geometric Pool (tgp) provides a wide variety of pooling operators, unified under a consistent API and a modular design. The library emphasizes usability and extensibility, and includes features like precomputed pooling, which significantly accelerate training for a class of operators. In this paper, we present tgp's structure and present an extensive benchmark. The latter showcases the library's features and systematically compares the performance of the implemented graph-pooling methods in different downstream tasks. The results, showing that the choice of the optimal pooling operator depends on tasks and data at hand, support the need for a library that enables fast prototyping.
【10】Empirical Mode Decomposition and Graph Transformation of the MSCI World Index: A Multiscale Topological Analysis for Graph Neural Network Modeling
标题：MSCI世界指数的经验模式分解和图形转换：图形神经网络建模的多尺度布局分析
链接：https://arxiv.org/abs/2512.12526
备注：19 pages, 3 figures, 6 tables
摘要：This study applies Empirical Mode Decomposition (EMD) to the MSCI World index and converts the resulting intrinsic mode functions (IMFs) into graph representations to enable modeling with graph neural networks (GNNs). Using CEEMDAN, we extract nine IMFs spanning high-frequency fluctuations to long-term trends. Each IMF is transformed into a graph using four time-series-to-graph methods: natural visibility, horizontal visibility, recurrence, and transition graphs. Topological analysis shows clear scale-dependent structure: high-frequency IMFs yield dense, highly connected small-world graphs, whereas low-frequency IMFs produce sparser networks with longer characteristic path lengths. Visibility-based methods are more sensitive to amplitude variability and typically generate higher clustering, while recurrence graphs better preserve temporal dependencies. These results provide guidance for designing GNN architectures tailored to the structural properties of decomposed components, supporting more effective predictive modeling of financial time series.
【11】GoMS: Graph of Molecule Substructure Network for Molecule Property Prediction
标题：GoMS：用于分子性质预测的分子子结构网络图
链接：https://arxiv.org/abs/2512.12489
摘要：While graph neural networks have shown remarkable success in molecular property prediction, current approaches like the Equivariant Subgraph Aggregation Networks (ESAN) treat molecules as bags of independent substructures, overlooking crucial relationships between these components. We present Graph of Molecule Substructures (GoMS), a novel architecture that explicitly models the interactions and spatial arrangements between molecular substructures. Unlike ESAN's bag-based representation, GoMS constructs a graph where nodes represent subgraphs and edges capture their structural relationships, preserving critical topological information about how substructures are connected and overlap within the molecule. Through extensive experiments on public molecular datasets, we demonstrate that GoMS outperforms ESAN and other baseline methods, with particularly improvements for large molecules containing more than 100 atoms. The performance gap widens as molecular size increases, demonstrating GoMS's effectiveness for modeling industrial-scale molecules. Our theoretical analysis demonstrates that GoMS can distinguish molecules with identical subgraph compositions but different spatial arrangements. Our approach shows particular promise for materials science applications involving complex molecules where properties emerge from the interplay between multiple functional units. By capturing substructure relationships that are lost in bag-based approaches, GoMS represents a significant advance toward scalable and interpretable molecular property prediction for real-world applications.
【12】MetaHGNIE: Meta-Path Induced Hypergraph Contrastive Learning in Heterogeneous Knowledge Graphs
标题：MetaHGNIE：异类知识图中的元路径诱导超图对比学习
链接：https://arxiv.org/abs/2512.12477
摘要：Node importance estimation (NIE) in heterogeneous knowledge graphs is a critical yet challenging task, essential for applications such as recommendation, knowledge reasoning, and question answering. Existing methods often rely on pairwise connections, neglecting high-order dependencies among multiple entities and relations, and they treat structural and semantic signals independently, hindering effective cross-modal integration. To address these challenges, we propose MetaHGNIE, a meta-path induced hypergraph contrastive learning framework for disentangling and aligning structural and semantic information. MetaHGNIE constructs a higher-order knowledge graph via meta-path sequences, where typed hyperedges capture multi-entity relational contexts. Structural dependencies are aggregated with local attention, while semantic representations are encoded through a hypergraph transformer equipped with sparse chunking to reduce redundancy. Finally, a multimodal fusion module integrates structural and semantic embeddings under contrastive learning with auxiliary supervision, ensuring robust cross-modal alignment. Extensive experiments on benchmark NIE datasets demonstrate that MetaHGNIE consistently outperforms state-of-the-art baselines. These results highlight the effectiveness of explicitly modeling higher-order interactions and cross-modal alignment in heterogeneous knowledge graphs. Our code is available at https://github.com/SEU-WENJIA/DualHNIE
【13】Rough Sets for Explainability of Spectral Graph Clustering
标题
：谱图聚集可解释性的粗糙集
链接：https://arxiv.org/abs/2512.12436
备注：24 figures, 21tables
摘要：Graph Spectral Clustering methods (GSC) allow representing clusters of diverse shapes, densities, etc. However, the results of such algorithms, when applied e.g. to text documents, are hard to explain to the user, especially due to embedding in the spectral space which has no obvious relation to document contents. Furthermore, the presence of documents without clear content meaning and the stochastic nature of the clustering algorithms deteriorate explainability. This paper proposes an enhancement to the explanation methodology, proposed in an earlier research of our team. It allows us to overcome the latter problems by taking inspiration from rough set theory.
【14】Can Graphs Improve Tabular Foundation Models?
标题：图形可以改进表格基础模型吗？
链接：https://arxiv.org/abs/2512.12405
摘要：Tabular data are central to many real-world systems. While recent tabular transformers and in-context learners such as SAINT, TP-BERTa, TabPFN, TabICL, and MITRA incorporate limited inter-row reasoning, most approaches still lack an explicit mechanism to model relationships among instances, even though similar samples often share related outcomes. We investigate whether introducing \emph{simple graph priors} can enhance \emph{pretrained tabular transformers}. Concretely, we introduce {BOLERO}, a lightweight, static bipartite graph head that augments {RoBERTa-Tab} (a RoBERTa-style tabular backbone pretrained with masked-token prediction.) Each instance connects to feature/value anchors; a small GNN refines row representations, while the backbone remains frozen. We evaluate on 80 classification and 64 regression datasets from the TP-BERTa benchmark suites, comparing against strong baselines including XGBoost, CatBoost, TabPFN-v2, MITRA, TabICL, TP-BERTa, and RoBERTa-Tab. To ensure statistically sound conclusions, we follow best practices for multi-dataset evaluation: pairwise Wilcoxon signed-rank tests on per-dataset score differences and effect sizes (median improvement with confidence intervals), rather than mean-rank post-hoc tests that depend on the competitor pool. BOLERO achieves the highest number of statistically significant wins across both classification and regression, demonstrating that lightweight graph priors meaningfully improve pretrained tabular transformers.
【15】TA-KAND: Two-stage Attention Triple Enhancement and U-KAN based Diffusion For Few-shot Knowledge Graph Completion
标题：TA-KAND：两阶段三重注意力增强和基于U-KAN的扩散，以实现Few-Shot知识图的完成
链接：https://arxiv.org/abs/2512.12182
摘要：Knowledge Graphs (KGs), thanks to their concise and efficient triple-based structure, have been widely applied in intelligent question answering, recommender systems and other domains. However, the heterogeneous and multifaceted nature of real-world data inevitably renders the distribution of relations long-tailed, making it crucial to complete missing facts with limited samples. Previous studies mainly based on metric matching or meta learning, yet they either fail to fully exploit neighborhood information in graph or overlook the distributional characteristics of contrastive signals. In this paper, we re-examine the problem from a perspective of generative representation and propose a few-shot knowledge graph completion framework that integrates two-stage attention triple enhancer with U-KAN based diffusion model. Extensive experiments on two public datasets show that our method achieve new state-of-the-art results.
【16】GraphPerf-RT: A Graph-Driven Performance Model for Hardware-Aware Scheduling of OpenMP Codes
标题：GraphPerf-RT：一种用于MEK代码硬件感知调度的图驱动性能模型
链接：https://arxiv.org/abs/2512.12091
备注：36 pages, 1 figure, 7 tables
摘要：Performance prediction for OpenMP workloads on heterogeneous embedded SoCs is challenging due to complex interactions between task DAG structure, control-flow irregularity, cache and branch behavior, and thermal dynamics; classical heuristics struggle under workload irregularity, tabular regressors discard structural information, and model-free RL risks overheating resource-constrained devices. We introduce GraphPerf-RT, the first surrogate that unifies task DAG topology, CFG-derived code semantics, and runtime context (per-core DVFS, thermal state, utilization) in a heterogeneous graph representation with typed edges encoding precedence, placement, and contention. Multi-task evidential heads predict makespan, energy, cache and branch misses, and utilization with calibrated uncertainty (Normal-Inverse-Gamma), enabling risk-aware scheduling that filters low-confidence rollouts. We validate GraphPerf-RT on three embedded ARM platforms (Jetson TX2, Jetson Orin NX, RUBIK Pi), achieving R^2 > 0.95 with well-calibrated uncertainty (ECE < 0.05). To demonstrate end-to-end scheduling utility, we integrate the surrogate with four RL methods on Jetson TX2: single-agent model-free (SAMFRL), single-agent model-based (SAMBRL), multi-agent model-free (MAMFRL-D3QN), and multi-agent model-based (MAMBRL-D3QN). Experiments across 5 seeds (200 episodes each) show that MAMBRL-D3QN with GraphPerf-RT as the world model achieves 66% makespan reduction (0.97 +/- 0.35s) and 82% energy reduction (0.006 +/- 0.005J) compared to model-free baselines, demonstrating that accurate, uncertainty-aware surrogates enable effective model-based planning on thermally constrained embedded systems.
【17】Exploring Spatial-Temporal Representation via Star Graph for mmWave Radar-based Human Activity Recognition
标题：通过星图探索基于毫米波雷达的人体活动识别时空表示
链接：https://arxiv.org/abs/2512.12013
摘要：Human activity recognition (HAR) requires extracting accurate spatial-temporal features with human movements. A mmWave radar point cloud-based HAR system suffers from sparsity and variable-size problems due to the physical features of the mmWave signal. Existing works usually borrow the preprocessing algorithms for the vision-based systems with dense point clouds, which may not be optimal for mmWave radar systems. In this work, we proposed a graph representation with a discrete dynamic graph neural network (DDGNN) to explore the spatial-temporal representation of human movement-related features. Specifically, we designed a star graph to describe the high-dimensional relative relationship between a manually added static center point and the dynamic mmWave radar points in the same and consecutive frames. We then adopted DDGNN to learn the features residing in the star graph with variable sizes. Experimental results demonstrated that our approach outperformed other baseline methods using real-world HAR datasets. Our system achieved an overall classification accuracy of 94.27\%, which gets the near-optimal performance with a vision-based skeleton data accuracy of 97.25\%. We also conducted an inference test on Raspberry Pi~4 to demonstrate its effectiveness on resource-constraint platforms. \sh{ We provided a comprehensive ablation study for variable DDGNN structures to validate our model design. Our system also outperformed three recent radar-specific methods without requiring resampling or frame aggregators.
【18】CLARGA: Multimodal Graph Representation Learning over Arbitrary Sets of Modalities
标题：CLARGA：任意模式集上的多模式图表示学习
链接：https://arxiv.org/abs/2512.11901
备注：WACV; Supplementary material is available on CVF proceedings
摘要：We introduce CLARGA, a general-purpose multimodal fusion architecture for multimodal representation learning that works with any number and type of modalities without changing the underlying framework. Given a supervised dataset, CLARGA can be applied to virtually any machine learning task to fuse different multimodal representations for processing by downstream layers. On a sample-by-sample basis, CLARGA learns how modalities should inform one another by building an attention weighted graph over their features and passing messages along this graph with a multi-head Graph Attention Network. Not only does this make CLARGA highly adaptive, as it constructs unique graphs for different samples, it makes for efficient fusion with sub-quadratic complexity as the number of modalities grows. Through a learnable mask, it can also adapt to missing modality inputs. The model is trained with a hybrid objective that combines a supervised task loss with contrastive InfoNCE loss, improving cross-modal consistency and robustness to noisy inputs. We demonstrate CLARGA's effectiveness in diverse multimodal representation learning tasks across 7 datasets spanning finance, human-computer interaction, general multimedia classification, and affective computing. It consistently outperforms baselines, state-of-the-art models, and ablations. Additional experiments also demonstrate its robustness to missing inputs and ability to excel on niche tasks. Overall, CLARGA can be easily plugged into machine learning models for effective and efficient learning of representations across a wide variety of tasks.
【19】An Operator-Consistent Graph Neural Network for Learning Diffusion Dynamics on Irregular Meshes
标题：学习不规则网格扩散动力学的操作一致图神经网络
链接：https://arxiv.org/abs/2512.11860
摘要：Classical numerical methods solve partial differential equations (PDEs) efficiently on regular meshes, but many of them become unstable on irregular domains. In practice, multiphysics interactions such as diffusion, damage, and healing often take place on irregular meshes. We develop an operator-consistent graph neural network (OCGNN-PINN) that approximates PDE evolution under physics-informed constraints. It couples node-edge message passing with a consistency loss enforcing the gradient-divergence relation through the graph incidence matrix, ensuring that discrete node and edge dynamics remain structurally coupled during temporal rollout. We evaluate the model on diffusion processes over physically driven evolving meshes and real-world scanned surfaces. The results show improved temporal stability and prediction accuracy compared with graph convolutional and multilayer perceptron baselines, approaching the performance of Crank-Nicolson solvers on unstructured domains.
【20】Exploring Topological Bias in Heterogeneous Graph Neural Networks
标题：探索异类图神经网络中的布局偏差
链接：https://arxiv.org/abs/2512.11846
摘要：Graph Neural Networks (GNNs) are characterized by their capacity of processing graph-structured data. However, due to the sparsity of labels under semi-supervised learning, they have been found to exhibit biased performance on specific nodes. This kind of bias has been validated to correlate with topological structure and is considered as a bottleneck of GNNs' performance. Existing work focuses on the study of homogeneous GNNs and little attention has been given to topological bias in Heterogeneous Graph Neural Networks (HGNNs). In this work, firstly, in order to distinguish distinct meta relations, we apply meta-weighting to the adjacency matrix of a heterogeneous graph. Based on the modified adjacency matrix, we leverage PageRank along with the node label information to construct a projection. The constructed projection effectively maps nodes to values that strongly correlated with model performance when using datasets both with and without intra-type connections, which demonstrates the universal existence of topological bias in HGNNs. To handle this bias, we propose a debiasing structure based on the difference in the mapped values of nodes and use it along with the original graph structure for contrastive learning. Experiments on three public datasets verify the effectiveness of the proposed method in improving HGNNs' performance and debiasing.
【21】Co-Hub Node Based Multiview Graph Learning with Theoretical Guarantees
标题：具有理论保证的基于共中心节点的多视图图学习
链接：https://arxiv.org/abs/2512.12435
摘要 ：Identifying the graphical structure underlying the observed multivariate data is essential in numerous applications. Current methodologies are predominantly confined to deducing a singular graph under the presumption that the observed data are uniform. However, many contexts involve heterogeneous datasets that feature multiple closely related graphs, typically referred to as multiview graphs. Previous research on multiview graph learning promotes edge-based similarity across layers using pairwise or consensus-based regularizers. However, multiview graphs frequently exhibit a shared node-based architecture across different views, such as common hub nodes. Such commonalities can enhance the precision of learning and provide interpretive insight. In this paper, we propose a co-hub node model, positing that different views share a common group of hub nodes. The associated optimization framework is developed by enforcing structured sparsity on the connections of these co-hub nodes. Moreover, we present a theoretical examination of layer identifiability and determine bounds on estimation error. The proposed methodology is validated using both synthetic graph data and fMRI time series data from multiple subjects to discern several closely related graphs.
【22】Modeling Dabrafenib Response Using Multi-Omics Modality Fusion and Protein Network Embeddings Based on Graph Convolutional Networks
标题：使用多组学模式融合和基于图卷积网络的蛋白质网络嵌入来建模达拉非尼反应
链接：https://arxiv.org/abs/2512.12134
摘要：Cancer cell response to targeted therapy arises from complex molecular interactions, making single omics insufficient for accurate prediction. This study develops a model to predict Dabrafenib sensitivity by integrating multiple omics layers (genomics, transcriptomics, proteomics, epigenomics, and metabolomics) with protein network embeddings generated using Graph Convolutional Networks (GCN). Each modality is encoded into low dimensional representations through neural network preprocessing. Protein interaction information from STRING is incorporated using GCN to capture biological topology. An attention based fusion mechanism assigns adaptive weights to each modality according to its relevance. Using GDSC cancer cell line data, the model shows that selective integration of two modalities, especially proteomics and transcriptomics, achieves the best test performance (R2 around 0.96), outperforming all single omics and full multimodal settings. Genomic and epigenomic data were less informative, while proteomic and transcriptomic layers provided stronger phenotypic signals related to MAPK inhibitor activity. These results show that attention guided multi omics fusion combined with GCN improves drug response prediction and reveals complementary molecular determinants of Dabrafenib sensitivity. The approach offers a promising computational framework for precision oncology and predictive modeling of targeted therapies.
Transformer(8篇)
【1】SeVeDo: A Heterogeneous Transformer Accelerator for Low-Bit Inference via Hierarchical Group Quantization and SVD-Guided Mixed Precision
标题：SeVeDo：通过分层群量化和ASD引导的混合精度进行低位推理的异类Transformer加速器
链接：https://arxiv.org/abs/2512.12930
摘要：Low-bit quantization is a promising technique for efficient transformer inference by reducing computational and memory overhead. However, aggressive bitwidth reduction remains challenging due to activation outliers, leading to accuracy degradation. Existing methods, such as outlier-handling and group quantization, achieve high accuracy but incur substantial energy consumption. To address this, we propose SeVeDo, an energy-efficient SVD-based heterogeneous accelerator that structurally separates outlier-sensitive components into a high-precision low-rank path, while the remaining computations are executed in a low-bit residual datapath with group quantization. To further enhance efficiency, Hierarchical Group Quantization (HGQ) combines coarse-grained floating-point scaling with fine-grained shifting, effectively reducing dequantization cost. Also, SVD-guided mixed precision (SVD-MP) statically allocates higher bitwidths to precision-sensitive components identified through low-rank decomposition, thereby minimizing floating-point operation cost. Experimental results show that SeVeDo achieves a peak energy efficiency of 13.8TOPS/W, surpassing conventional designs, with 12.7TOPS/W on ViT-Base and 13.4TOPS/W on Llama2-7B benchmarks.
【2】Improving Recursive Transformers with Mixture of LoRAs
标题：用LoRA混合算法改进递归变换器
链接：https://arxiv.org/abs/2512.12880
摘要：Parameter sharing in recursive transformers reduces model size but collapses layer-wise expressivity. We propose Mixture of LoRAs (MoL), a lightweight conditional-computation mechanism that inserts Low-Rank Adaptation (LoRA) experts inside a shared feed-forward network (FFN). MoL enables token-conditional weight-space modulation of the shared FFN without untying backbone parameters, unlike prior approaches that add fixed or externally attached adapters. We pretrain a modernised recursive architecture, ModernALBERT, integrating rotary embeddings, GeGLU, FlashAttention, and a distillation-based initialisation. Across GLUE, SQuAD-v2, and BEIR, ModernALBERT (50M--120M) achieves state-of-the-art performance among compact models and surpasses larger fully parameterised baselines. We also propose an expert-merging procedure that compresses MoL into a single adapter at inference while preserving accuracy, enabling efficient deployment. Our results show that conditional weight-space modulation effectively restores the expressivity lost under aggressive parameter sharing in recursive transformers.
【3】From Small to Large: Generalization Bounds for Transformers on Variable-Size Inputs
标题：从小到大：Transformer在可变大小输入上的概括界限
链接：https://arxiv.org/abs/2512.12805
摘要 ：Transformers exhibit a notable property of \emph{size generalization}, demonstrating an ability to extrapolate from smaller token sets to significantly longer ones. This behavior has been documented across diverse applications, including point clouds, graphs, and natural language. Despite its empirical success, this capability still lacks some rigorous theoretical characterizations. In this paper, we develop a theoretical framework to analyze this phenomenon for geometric data, which we represent as discrete samples from a continuous source (e.g., point clouds from manifolds, graphs from graphons). Our core contribution is a bound on the error between the Transformer's output for a discrete sample and its continuous-domain equivalent. We prove that for Transformers with stable positional encodings, this bound is determined by the sampling density and the intrinsic dimensionality of the data manifold. Experiments on graphs and point clouds of various sizes confirm the tightness of our theoretical bound.
【4】Liquid Reasoning Transformers: A Sudoku-Based Prototype for Chess-Scale Algorithmic Tasks
标题：液体推理Transformer：基于数独的国际象棋音阶数学任务原型
链接：https://arxiv.org/abs/2512.12792
备注：11 pages, 0 figures
摘要：The Liquid Reasoning Transformer (LRT) is a transformer architecture designed for inference with adaptive depths using iterative changes, discard-based correction, and a learned stopping mechanism. Instead of relying on a single feedforward pass, the model updates a recurrent reasoning token across multiple internal steps, allowing it to correct early errors and allocate computation based on input difficulty. We evaluate the LRT on Sudoku as a controlled testbed for structured reasoning and show that it achieves strong performance, reaching 98.68% digit accuracy and 36.30% full-puzzle accuracy without using symbolic rules or search. Analyzing internal patterns shows that the discard and stop gates play different, important roles in stabilizing inferences and adjusting computational depth. We discuss how these mechanisms extend naturally to chess-scale reasoning tasks and outline extensions for multi-token reasoning and larger domains.
【5】TwinFormer: A Dual-Level Transformer for Long-Sequence Time-Series Forecasting
标题：TwinFormer：用于长序列时间序列预测的双级Transformer
链接：https://arxiv.org/abs/2512.12301
备注：14 pages, 4 figures
摘要：TwinFormer is a hierarchical Transformer for long-sequence time-series forecasting. It divides the input into non-overlapping temporal patches and processes them in two stages: (1) a Local Informer with top-$k$ Sparse Attention models intra-patch dynamics, followed by mean pooling; (2) a Global Informer captures long-range inter-patch dependencies using the same top-$k$ attention. A lightweight GRU aggregates the globally contextualized patch tokens for direct multi-horizon prediction. The resulting architecture achieves linear $O(kLd)$ time and memory complexity. On eight real-world benchmarking datasets from six different domains, including weather, stock price, temperature, power consumption, electricity, and disease, and forecasting horizons $96-720$, TwinFormer secures $27$ positions in the top two out of $34$. Out of the $27$, it achieves the best performance on MAE and RMSE at $17$ places and $10$ at the second-best place on MAE and RMSE. This consistently outperforms PatchTST, iTransformer, FEDformer, Informer, and vanilla Transformers. Ablations confirm the superiority of top-$k$ Sparse Attention over ProbSparse and the effectiveness of GRU-based aggregation. Code is available at this repository: https://github.com/Mahimakumavat1205/TwinFormer.
【6】ALERT Open Dataset and Input-Size-Agnostic Vision Transformer for Driver Activity Recognition using IR-UWB
标题：ALERT开放式数据集和输入大小无关的视觉Transformer，用于使用IR-UWB的驾驶员活动识别
链接：https://arxiv.org/abs/2512.12206
摘要：Distracted driving contributes to fatal crashes worldwide. To address this, researchers are using driver activity recognition (DAR) with impulse radio ultra-wideband (IR-UWB) radar, which offers advantages such as interference resistance, low power consumption, and privacy preservation. However, two challenges limit its adoption: the lack of large-scale real-world UWB datasets covering diverse distracted driving behaviors, and the difficulty of adapting fixed-input Vision Transformers (ViTs) to UWB radar data with non-standard dimensions. This work addresses both challenges. We present the ALERT dataset, which contains 10,220 radar samples of seven distracted driving activities collected in real driving conditions. We also propose the input-size-agnostic Vision Transformer (ISA-ViT), a framework designed for radar-based DAR. The proposed method resizes UWB data to meet ViT input requirements while preserving radar-specific information such as Doppler shifts and phase characteristics. By adjusting patch configurations and leveraging pre-trained positional embedding vectors (PEVs), ISA-ViT overcomes the limitations of naive resizing approaches. In addition, a domain fusion strategy combines range- and frequency-domain features to further improve classification performance. Comprehensive experiments demonstrate that ISA-ViT achieves a 22.68% accuracy improvement over an existing ViT-based approach for UWB-based DAR. By publicly releasing the ALERT dataset and detailing our input-size-agnostic strategy, this work facilitates the development of more robust and scalable distracted driving detection systems for real-world deployment.
【7】Explainable AI for Smart Greenhouse Control: Interpretability of Temporal Fusion Transformer in the Internet of Robotic Things
标题：智能温室控制的可解释人工智能：机器人物联网中时间融合Transformer的可解释性
链接：https://arxiv.org/abs/2512.11852
备注：7 pages, Accepted in 36th Irish Signals and Systems Conference, ISSC 2025
摘要 ：The integration of the Internet of Robotic Things (IoRT) in smart greenhouses has revolutionised precision agriculture by enabling efficient and autonomous environmental control. However, existing time series forecasting models in such setups often operate as black boxes, lacking mechanisms for explainable decision-making, which is a critical limitation when trust, transparency, and regulatory compliance are paramount in smart farming practices. This study leverages the Temporal Fusion Transformer (TFT) model to automate actuator settings for optimal greenhouse management. To enhance interpretability and trust in the model decision-making process, both local and global explanation techniques were employed using model-inherent interpretation, local interpretable model-agnostic explanations (LIME), and SHapley additive explanations (SHAP). These explainability methods provide information on how different sensor readings, such as temperature, humidity, CO2 levels, light, and outer climate, contribute to actuator control decisions in an automated greenhouse. The trained TFT model achieved a test accuracy of 95% on a class-imbalanced dataset for actuator control settings in an automated greenhouse environment. The results demonstrate the varying influence of each sensor on real-time greenhouse adjustments, ensuring transparency and enabling adaptive fine-tuning for improved crop yield and resource efficiency.
【8】Airport Passenger Flow Forecasting via Deformable Temporal-Spectral Transformer Approach
标题：基于可变形时间谱Transformer器的机场客流预测
链接：https://arxiv.org/abs/2512.11845
备注：14 pages, 10 figures
摘要：Accurate forecasting of passenger flows is critical for maintaining the efficiency and resilience of airport operations. Recent advances in patch-based Transformer models have shown strong potential in various time series forecasting tasks. However, most existing methods rely on fixed-size patch embedding, making it difficult to model the complex and heterogeneous patterns of airport passenger flows. To address this issue, this paper proposes a deformable temporal-spectral transformer named DTSFormer that integrates a multiscale deformable partitioning module and a joint temporal-spectral filtering module. Specifically, the input sequence is dynamically partitioned into multiscale temporal patches via a novel window function-based masking, enabling the extraction of heterogeneous trends across different temporal stages. Then, within each scale, a frequency-domain attention mechanism is designed to capture both high- and low-frequency components, thereby emphasizing the volatility and periodicity inherent in airport passenger flows. Finally, the resulting multi-frequency features are subsequently fused in the time domain to jointly model short-term fluctuations and long-term trends. Comprehensive experiments are conducted on real-world passenger flow data collected at Beijing Capital International Airport from January 2023 to March 2024. The results indicate that the proposed method consistently outperforms state-of-the-art forecasting models across different prediction horizons. Further analysis shows that the deformable partitioning module aligns patch lengths with dominant periods and heterogeneous trends, enabling superior capture of sudden high-frequency fluctuations.
GAN|对抗|攻击|生成相关(16篇)
【1】A Scientific Reasoning Model for Organic Synthesis Procedure Generation
标题：有机合成过程生成的科学推理模型
链接：https://arxiv.org/abs/2512.13668
摘要：Solving computer-aided synthesis planning is essential for enabling fully automated, robot-assisted synthesis workflows and improving the efficiency of drug discovery. A key challenge, however, is bridging the gap between computational route design and practical laboratory execution, particularly the accurate prediction of viable experimental procedures for each synthesis step. In this work, we present QFANG, a scientific reasoning language model capable of generating precise, structured experimental procedures directly from reaction equations, with explicit chain-of-thought reasoning. To develop QFANG, we curated a high-quality dataset comprising 905,990 chemical reactions paired with structured action sequences, extracted and processed from patent literature using large language models. We introduce a Chemistry-Guided Reasoning (CGR) framework that produces chain-of-thought data grounded in chemical knowledge at scale. The model subsequently undergoes supervised fine-tuning to elicit complex chemistry reasoning. Finally, we apply Reinforcement Learning from Verifiable Rewards (RLVR) to further enhance procedural accuracy. Experimental results demonstrate that QFANG outperforms advanced general-purpose reasoning models and nearest-neighbor retrieval baselines, measured by traditional NLP similarity metrics and a chemically aware evaluator using an LLM-as-a-judge. Moreover, QFANG generalizes to certain out-of-domain reaction classes and adapts to variations in laboratory conditions and user-specific constraints. We believe that QFANG's ability to generate high-quality synthesis procedures represents an important step toward bridging the gap between computational synthesis planning and fully automated laboratory synthesis.
【2】Superposition as Lossy Compression: Measure with Sparse Autoencoders and Connect to Adversarial Vulnerability
标题：叠加为有损压缩：使用稀疏自动编码器进行测量并连接到对抗漏洞
链接：https://arxiv.org/abs/2512.13568
备注：Accepted to TMLR, view HTML here: https://leonardbereska.github.io/blog/2025/superposition/
摘要：Neural networks achieve remarkable performance through superposition: encoding multiple features as overlapping directions in activation space rather than dedicating individual neurons to each feature. This challenges interpretability, yet we lack principled methods to measure superposition. We present an information-theoretic framework measuring a neural representation's effective degrees of freedom. We apply Shannon entropy to sparse autoencoder activations to compute the number of effective features as the minimum neurons needed for interference-free encoding. Equivalently, this measures how many "virtual neurons" the network simulates through superposition. When networks encode more effective features than actual neurons, they must accept interference as the price of compression. Our metric strongly correlates with ground truth in toy models, detects minimal superposition in algorithmic tasks, and reveals systematic reduction under dropout. Layer-wise patterns mirror intrinsic dimensionality studies on Pythia-70M. The metric also captures developmental dynamics, detecting sharp feature consolidation during grokking. Surprisingly, adversarial training can increase effective features while improving robustness, contradicting the hypothesis that superposition causes vulnerability. Instead, the effect depends on task complexity and network capacity: simple tasks with ample capacity allow feature expansion (abundance regime), while complex tasks or limited capacity force reduction (scarcity regime). By defining superposition as lossy compression, this work enables principled measurement of how neural networks organize information under computational constraints, connecting superposition to adversarial robustness.
【3】SSAS: Cross-subject EEG-based Emotion Recognition through Source Selection with Adversarial Strategy
标题：SSAS：通过对抗策略的源选择的跨学科基于脑电波的情感识别
链接：https://arxiv.org/abs/2512.13458
摘要：Electroencephalographic (EEG) signals have long been applied in the field of affective brain-computer interfaces (aBCIs). Cross-subject EEG-based emotion recognition has demonstrated significant potential in practical applications due to its suitability across diverse people. However, most studies on cross-subject EEG-based emotion recognition neglect the presence of inter-individual variability and negative transfer phenomena during model training. To address this issue, a cross-subject EEG-based emotion recognition through source selection with adversarial strategy is introduced in this paper. The proposed method comprises two modules: the source selection network (SS) and the adversarial strategies network (AS). The SS uses domain labels to reverse-engineer the training process of domain adaptation. Its key idea is to disrupt class separability and magnify inter-domain differences, thereby raising the classification difficulty and forcing the model to learn domain-invariant yet emotion-relevant representations. The AS gets the source domain selection results and the pretrained domain discriminators from SS. The pretrained domain discriminators compute a novel loss aimed at enhancing the performance of domain classification during adversarial training, ensuring the balance of adversarial strategies. This paper provides theoretical insights into the proposed method and achieves outstanding performance on two EEG-based emotion datasets, SEED and SEED-IV. The code can be found at https://github.com/liuyici/SSAS.
【4】BézierFlow: Bézier Stochastic Interpolant Schedulers for Few-Step Generation
标题：BézierFlow：用于少步生成的Bézier随机插值排序器
链接：https://arxiv.org/abs/2512.13255
备注：Project page: https://bezierflow.github.io
摘要：We introduce BézierFlow, a lightweight training approach for few-step generation with pretrained diffusion and flow models. BézierFlow achieves a 2-3x performance improvement for sampling with $\leq$ 10 NFEs while requiring only 15 minutes of training. Recent lightweight training approaches have shown promise by learning optimal timesteps, but their scope remains restricted to ODE discretizations. To broaden this scope, we propose learning the optimal transformation of the sampling trajectory by parameterizing stochastic interpolant (SI) schedulers. The main challenge lies in designing a parameterization that satisfies critical desiderata, including boundary conditions, differentiability, and monotonicity of the SNR. To effectively meet these requirements, we represent scheduler functions as Bézier functions, where control points naturally enforce these properties. This reduces the problem to learning an ordered set of points in the time range, while the interpretation of the points changes from ODE timesteps to Bézier control points. Across a range of pretrained diffusion and flow models, BézierFlow consistently outperforms prior timestep-learning methods, demonstrating the effectiveness of expanding the search space from discrete timesteps to Bézier-based trajectory transformations.
【5】Evaluating Adversarial Attacks on Federated Learning for Temperature Forecasting
标题：评估温度预测联邦学习的对抗攻击
链接：https://arxiv.org/abs/2512.13207
摘要：Deep learning and federated learning (FL) are becoming powerful partners for next-generation weather forecasting. Deep learning enables high-resolution spatiotemporal forecasts that can surpass traditional numerical models, while FL allows institutions in different locations to collaboratively train models without sharing raw data, addressing efficiency and security concerns. While FL has shown promise across heterogeneous regions, its distributed nature introduces new vulnerabilities. In particular, data poisoning attacks, in which compromised clients inject manipulated training data, can degrade performance or introduce systematic biases. These threats are amplified by spatial dependencies in meteorological data, allowing localized perturbations to influence broader regions through global model aggregation. In this study, we investigate how adversarial clients distort federated surface temperature forecasts trained on the Copernicus European Regional ReAnalysis (CERRA) dataset. We simulate geographically distributed clients and evaluate patch-based and global biasing attacks on regional temperature forecasts. Our results show that even a small fraction of poisoned clients can mislead predictions across large, spatially connected areas. A global temperature bias attack from a single compromised client shifts predictions by up to -1.7 K, while coordinated patch attacks more than triple the mean squared error and produce persistent regional anomalies exceeding +3.5 K. Finally, we assess trimmed mean aggregation as a defense mechanism, showing that it successfully defends against global bias attacks (2-13\% degradation) but fails against patch attacks (281-603\% amplification), exposing limitations of outlier-based defenses for spatially correlated data.
【6】Calibrating Uncertainty for Zero-Shot Adversarial CLIP
标题：校准Zero-Shot对抗CLIP的不确定性
链接：https://arxiv.org/abs/2512.12997
摘要：CLIP delivers strong zero-shot classification but remains highly vulnerable to adversarial attacks. Previous work of adversarial fine-tuning largely focuses on matching the predicted logits between clean and adversarial examples, which overlooks uncertainty calibration and may degrade the zero-shot generalization. A common expectation in reliable uncertainty estimation is that predictive uncertainty should increase as inputs become more difficult or shift away from the training distribution. However, we frequently observe the opposite in the adversarial setting: perturbations not only degrade accuracy but also suppress uncertainty, leading to severe miscalibration and unreliable over-confidence. This overlooked phenomenon highlights a critical reliability gap beyond robustness. To bridge this gap, we propose a novel adversarial fine-tuning objective for CLIP considering both prediction accuracy and uncertainty alignments. By reparameterizing the output of CLIP as the concentration parameter of a Dirichlet distribution, we propose a unified representation that captures relative semantic structure and the magnitude of predictive confidence. Our objective aligns these distributions holistically under perturbations, moving beyond single-logit anchoring and restoring calibrated uncertainty. Experiments on multiple zero-shot classification benchmarks demonstrate that our approach effectively restores calibrated uncertainty and achieves competitive adversarial robustness while maintaining clean accuracy.
【7】Next-generation reservoir computing validated by classification task
标题：下一代储层计算通过分类任务验证
链接：https://arxiv.org/abs/2512.12903
摘要：An emerging computing paradigm, so-called next-generation reservoir computing (NG-RC) is investigated. True to its namesake, NG-RC requires no actual reservoirs for input data mixing but rather computing the polynomial terms directly from the time series inputs. However, benchmark tests so far reported have been one-sided, limited to prediction tasks of temporal waveforms such as Lorenz 63 attractor and Mackey-Glass chaotic signal. We will demonstrate for the first time that NG-RC can perform classification task as good as conventional RC. This validates the versatile computational capability of NG-RC in tasks of both prediction and classification.
【8】PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks
标题：PRIVEE：保护隐私的垂直联邦学习对抗特征推理攻击
链接：https://arxiv.org/abs/2512.12840
备注：12 pages, 3 figures
摘要：Vertical Federated Learning (VFL) enables collaborative model training across organizations that share common user samples but hold disjoint feature spaces. Despite its potential, VFL is susceptible to feature inference attacks, in which adversarial parties exploit shared confidence scores (i.e., prediction probabilities) during inference to reconstruct private input features of other participants. To counter this threat, we propose PRIVEE (PRIvacy-preserving Vertical fEderated lEarning), a novel defense mechanism named after the French word privée, meaning "private." PRIVEE obfuscates confidence scores while preserving critical properties such as relative ranking and inter-score distances. Rather than exposing raw scores, PRIVEE shares only the transformed representations, mitigating the risk of reconstruction attacks without degrading model prediction accuracy. Extensive experiments show that PRIVEE achieves a threefold improvement in privacy protection compared to state-of-the-art defenses, while preserving full predictive performance against advanced feature inference attacks.
【9】GradID: Adversarial Detection via Intrinsic Dimensionality of Gradients
标题：GradID：通过学生的内在模糊性进行对抗检测
链接：https://arxiv.org/abs/2512.12827
备注：16 pages, 8 figures
摘要：Despite their remarkable performance, deep neural networks exhibit a critical vulnerability: small, often imperceptible, adversarial perturbations can lead to drastically altered model predictions. Given the stringent reliability demands of applications such as medical diagnosis and autonomous driving, robust detection of such adversarial attacks is paramount. In this paper, we investigate the geometric properties of a model's input loss landscape. We analyze the Intrinsic Dimensionality (ID) of the model's gradient parameters, which quantifies the minimal number of coordinates required to describe the data points on their underlying manifold. We reveal a distinct and consistent difference in the ID for natural and adversarial data, which forms the basis of our proposed detection method. We validate our approach across two distinct operational scenarios. First, in a batch-wise context for identifying malicious data groups, our method demonstrates high efficacy on datasets like MNIST and SVHN. Second, in the critical individual-sample setting, we establish new state-of-the-art results on challenging benchmarks such as CIFAR-10 and MS COCO. Our detector significantly surpasses existing methods against a wide array of attacks, including CW and AutoAttack, achieving detection rates consistently above 92\% on CIFAR-10. The results underscore the robustness of our geometric approach, highlighting that intrinsic dimensionality is a powerful fingerprint for adversarial detection across diverse datasets and attack strategies.
【10】Differentiable Energy-Based Regularization in GANs: A Simulator-Based Exploration of VQE-Inspired Auxiliary Losses
标题：GAN中基于可区分能量的正规化：基于模拟器的VQE启发辅助损失探索
链接：https://arxiv.org/abs/2512.12581
备注：Exploratory, simulator-based proof of concept. No claims of quantum advantage
摘要：This paper presents an exploratory, simulator-based proof of concept investigating whether differentiable energy terms derived from parameterized quantum circuits can serve as auxiliary regularization signals in Generative Adversarial Networks (GANs). We augment the Auxiliary Classifier GAN (ACGAN) generator objective with a Variational Quantum Eigensolver (VQE)-inspired energy term computed from class-specific Ising Hamiltonians using Qiskit's EstimatorQNN and TorchConnector. Important limitations: All experiments run on a noiseless statevector simulator with only 4 qubits, use a deliberately simple Hamiltonian parameterization, and lack ablation studies comparing against equivalent classical biases. The computational overhead (approximately 200x slower than classical ACGAN) reflects simulator artifacts rather than inherent quantum costs. On MNIST, we observe that the energy-regularized model (termed QACGAN) achieves high classification accuracy (99 to 100 percent) within 5 epochs compared to 87.8 percent for ACGAN, suggesting the auxiliary term influences class conditioning. However, sample quality metrics (FID) show high variance across runs (coefficient of variation approximately 25 percent at epoch 5), with values ranging from 19.92 to 35.96. Extended runs stabilize around FID 23 to 24, comparable to the ACGAN baseline. We explicitly do not claim quantum advantage, improved stability in any general sense, or scalability beyond this toy setting. The contribution is methodological: demonstrating that VQE-style energy computations can be integrated into GAN training loops via differentiable pathways. Whether such auxiliary signals provide benefits beyond equivalent classical regularizers remains an open question requiring systematic ablation studies, which we leave for future work.
【11】MolGuidance: Advanced Guidance Strategies for Conditional Molecular Generation with Flow Matching
标题：MolGuidance：具有流量匹配的条件分子生成的高级指导策略
链接：https://arxiv.org/abs/2512.12198
备注：19 pages, 5 figures, code: https://github.com/Liu-Group-UF/MolGuidance
摘要：Key objectives in conditional molecular generation include ensuring chemical validity, aligning generated molecules with target properties, promoting structural diversity, and enabling efficient sampling for discovery. Recent advances in computer vision introduced a range of new guidance strategies for generative models, many of which can be adapted to support these goals. In this work, we integrate state-of-the-art guidance methods -- including classifier-free guidance, autoguidance, and model guidance -- in a leading molecule generation framework built on an SE(3)-equivariant flow matching process. We propose a hybrid guidance strategy that separately guides continuous and discrete molecular modalities -- operating on velocity fields and predicted logits, respectively -- while jointly optimizing their guidance scales via Bayesian optimization. Our implementation, benchmarked on the QM9 and QMe14S datasets, achieves new state-of-the-art performance in property alignment for de novo molecular generation. The generated molecules also exhibit high structural validity. Furthermore, we systematically compare the strengths and limitations of various guidance methods, offering insights into their broader applicability.
【12】Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification
标题：针对基于深度学习的射频指纹识别的对抗攻击
链接：https://arxiv.org/abs/2512.12002
摘要：Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT) devices. RFFI exploits deep learning models to extract hardware impairments to uniquely identify wireless devices. Recent studies show deep learning-based RFFI is vulnerable to adversarial attacks. However, effective adversarial attacks against different types of RFFI classifiers have not yet been explored. In this paper, we carried out a comprehensive investigations into different adversarial attack methods on RFFI systems using various deep learning models. Three specific algorithms, fast gradient sign method (FGSM), projected gradient descent (PGD), and universal adversarial perturbation (UAP), were analyzed. The attacks were launched to LoRa-RFFI and the experimental results showed the generated perturbations were effective against convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRU). We further used UAP to launch practical attacks. Special factors were considered for the wireless context, including implementing real-time attacks, the effectiveness of the attacks over a period of time, etc. Our experimental evaluation demonstrated that UAP can successfully launch adversarial attacks against the RFFI, achieving a success rate of 81.7% when the adversary almost has no prior knowledge of the victim RFFI systems.
【13】MPath: Multimodal Pathology Report Generation from Whole Slide Images
标题：MPath：从整个切片图像生成多模式病理学报告
链接：https://arxiv.org/abs/2512.11906
备注：Pages 4, Figures 1, Table 1
摘要：Automated generation of diagnostic pathology reports directly from whole slide images (WSIs) is an emerging direction in computational pathology. Translating high-resolution tissue patterns into clinically coherent text remains difficult due to large morphological variability and the complex structure of pathology narratives. We introduce MPath, a lightweight multimodal framework that conditions a pretrained biomedical language model (BioBART) on WSI-derived visual embeddings through a learned visual-prefix prompting mechanism. Instead of end-to-end vision-language pretraining, MPath leverages foundation-model WSI features (CONCH + Titan) and injects them into BioBART via a compact projection module, keeping the language backbone frozen for stability and data efficiency. MPath was developed and evaluated on the RED 2025 Grand Challenge dataset and ranked 4th in Test Phase 2, despite limited submission opportunities. The results highlight the potential of prompt-based multimodal conditioning as a scalable and interpretable strategy for pathology report generation.
【14】On the Dangers of Bootstrapping Generation for Continual Learning and Beyond
标题：论引导一代对持续学习及超越的危险
链接：https://arxiv.org/abs/2512.11867
备注：DAGM German Conference on Pattern Recognition, 2025
摘要：The use of synthetically generated data for training models is becoming a common practice. While generated data can augment the training data, repeated training on synthetic data raises concerns about distribution drift and degradation of performance due to contamination of the dataset. We investigate the consequences of this bootstrapping process through the lens of continual learning, drawing a connection to Generative Experience Replay (GER) methods. We present a statistical analysis showing that synthetic data introduces significant bias and variance into training objectives, weakening the reliability of maximum likelihood estimation. We provide empirical evidence showing that popular generative models collapse under repeated training with synthetic data. We quantify this degradation and show that state-of-the-art GER methods fail to maintain alignment in the latent space. Our findings raise critical concerns about the use of synthetic data in continual learning.
【15】CR3G: Causal Reasoning for Patient-Centric Explanations in Radiology Report Generation
标题：CR 3G：放射学报告生成中以患者为中心的解释的因果推理
链接：https://arxiv.org/abs/2512.11830
备注：8 pages, 5 figures, 1 table
摘要 ：Automatic chest X-ray report generation is an important area of research aimed at improving diagnostic accuracy and helping doctors make faster decisions. Current AI models are good at finding correlations (or patterns) in medical images. Still, they often struggle to understand the deeper cause-and-effect relationships between those patterns and a patient condition. Causal inference is a powerful approach that goes beyond identifying patterns to uncover why certain findings in an X-ray relate to a specific diagnosis. In this paper, we will explore the prompt-driven framework Causal Reasoning for Patient-Centric Explanations in radiology Report Generation (CR3G) that is applied to chest X-ray analysis to improve understanding of AI-generated reports by focusing on cause-and-effect relationships, reasoning and generate patient-centric explanation. The aim to enhance the quality of AI-driven diagnostics, making them more useful and trustworthy in clinical practice. CR3G has shown better causal relationship capability and explanation capability for 2 out of 5 abnormalities.
【16】Hellinger loss function for Generative Adversarial Networks
标题：生成对抗网络的Hellinger损失函数
链接：https://arxiv.org/abs/2512.12267
备注：25 pages and Supplemental Material
摘要：We propose Hellinger-type loss functions for training Generative Adversarial Networks (GANs), motivated by the boundedness, symmetry, and robustness properties of the Hellinger distance. We define an adversarial objective based on this divergence and study its statistical properties within a general parametric framework. We establish the existence, uniqueness, consistency, and joint asymptotic normality of the estimators obtained from the adversarial training procedure. In particular, we analyze the joint estimation of both generator and discriminator parameters, offering a comprehensive asymptotic characterization of the resulting estimators. We introduce two implementations of the Hellinger-type loss and we evaluate their empirical behavior in comparison with the classic (Maximum Likelihood-type) GAN loss. Through a controlled simulation study, we demonstrate that both proposed losses yield improved estimation accuracy and robustness under increasing levels of data contamination.
半/弱/无/有监督|不确定性|主动学习(8篇)
【1】On-Device Continual Learning for Unsupervised Visual Anomaly Detection in Dynamic Manufacturing
标题：动态制造中无监督视觉异常检测的设备上持续学习
链接：https://arxiv.org/abs/2512.13497
备注：Accepted by European Conference on EDGE AI Technologies and Applications (EEAI) 2025
摘要：In modern manufacturing, Visual Anomaly Detection (VAD) is essential for automated inspection and consistent product quality. Yet, increasingly dynamic and flexible production environments introduce key challenges: First, frequent product changes in small-batch and on-demand manufacturing require rapid model updates. Second, legacy edge hardware lacks the resources to train and run large AI models. Finally, both anomalous and normal training data are often scarce, particularly for newly introduced product variations. We investigate on-device continual learning for unsupervised VAD with localization, extending the PatchCore to incorporate online learning for real-world industrial scenarios. The proposed method leverages a lightweight feature extractor and an incremental coreset update mechanism based on k-center selection, enabling rapid, memory-efficient adaptation from limited data while eliminating costly cloud retraining. Evaluations on an industrial use case are conducted using a testbed designed to emulate flexible production with frequent variant changes in a controlled environment. Our method achieves a 12% AUROC improvement over the baseline, an 80% reduction in memory usage, and faster training compared to batch retraining. These results confirm that our method delivers accurate, resource-efficient, and adaptive VAD suitable for dynamic and smart manufacturing.
【2】ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data
标题：ModSC：一个用于对异类数据进行半监督分类的模块化框架
链接：https://arxiv.org/abs/2512.13228
备注：Preprint describing the open source ModSSC framework for inductive and transductive semi-supervised classification on heterogeneous data
摘要：Semi-supervised classification leverages both labeled and unlabeled data to improve predictive performance, but existing software support is fragmented across methods and modalities. We introduce ModSSC, an open source Python framework that unifies inductive and transductive semi-supervised classification in a modular code base. ModSSC implements a broad range of classical and recent algorithms, provides loaders for tabular, image, text, audio and graph datasets, and exposes a single configuration interface for specifying datasets, models and evaluation protocols. It supports both lightweight classical methods on small datasets running on CPU and recent deep approaches that can exploit multiple GPUs within the same experimental framework. Experiments are described declaratively in YAML, which facilitates reproducing existing work and running large comparative studies. ModSSC 1.0.0 is released under the MIT license with extensive documentation and tests, and is available at https://github.com/ModSSC/ModSSC.
【3】Harmonizing Generalization and Specialization: Uncertainty-Informed Collaborative Learning for Semi-supervised Medical Image Segmentation
标题：协调概括和专业化：半监督医学图像分割的不确定性知情协作学习
链接：https://arxiv.org/abs/2512.13101
备注：This work has been submitted to the IEEE TMI for possible publication
摘要 ：Vision foundation models have demonstrated strong generalization in medical image segmentation by leveraging large-scale, heterogeneous pretraining. However, they often struggle to generalize to specialized clinical tasks under limited annotations or rare pathological variations, due to a mismatch between general priors and task-specific requirements. To address this, we propose Uncertainty-informed Collaborative Learning (UnCoL), a dual-teacher framework that harmonizes generalization and specialization in semi-supervised medical image segmentation. Specifically, UnCoL distills both visual and semantic representations from a frozen foundation model to transfer general knowledge, while concurrently maintaining a progressively adapting teacher to capture fine-grained and task-specific representations. To balance guidance from both teachers, pseudo-label learning in UnCoL is adaptively regulated by predictive uncertainty, which selectively suppresses unreliable supervision and stabilizes learning in ambiguous regions. Experiments on diverse 2D and 3D segmentation benchmarks show that UnCoL consistently outperforms state-of-the-art semi-supervised methods and foundation model baselines. Moreover, our model delivers near fully supervised performance with markedly reduced annotation requirements.
【4】Unsupervised learning of multiscale switching dynamical system models from multimodal neural data
标题：从多峰神经数据进行多尺度切换动态系统模型的无监督学习
链接：https://arxiv.org/abs/2512.12881
备注：30 pages, 8 figures
摘要：Neural population activity often exhibits regime-dependent non-stationarity in the form of switching dynamics. Learning accurate switching dynamical system models can reveal how behavior is encoded in neural activity. Existing switching approaches have primarily focused on learning models from a single neural modality, either continuous Gaussian signals or discrete Poisson signals. However, multiple neural modalities are often recorded simultaneously to measure different spatiotemporal scales of brain activity, and all these modalities can encode behavior. Moreover, regime labels are typically unavailable in training data, posing a significant challenge for learning models of regime-dependent switching dynamics. To address these challenges, we develop a novel unsupervised learning algorithm that learns the parameters of switching multiscale dynamical system models using only multiscale neural observations. We demonstrate our method using both simulations and two distinct experimental datasets with multimodal spike-LFP observations during different motor tasks. We find that our switching multiscale dynamical system models more accurately decode behavior than switching single-scale dynamical models, showing the success of multiscale neural fusion. Further, our models outperform stationary multiscale models, illustrating the importance of tracking regime-dependent non-stationarity in multimodal neural data. The developed unsupervised learning framework enables more accurate modeling of complex multiscale neural dynamics by leveraging information in multimodal recordings while incorporating regime switches. This approach holds promise for improving the performance and robustness of brain-computer interfaces over time and for advancing our understanding of the neural basis of behavior.
【5】Optimal Labeler Assignment and Sampling for Active Learning in the Presence of Imperfect Labels
标题：不完美标签下主动学习的最优标签分配与采样
链接：https://arxiv.org/abs/2512.12870
备注：22 pages, 6 figures. Preprint under review
摘要：Active Learning (AL) has garnered significant interest across various application domains where labeling training data is costly. AL provides a framework that helps practitioners query informative samples for annotation by oracles (labelers). However, these labels often contain noise due to varying levels of labeler accuracy. Additionally, uncertain samples are more prone to receiving incorrect labels because of their complexity. Learning from imperfectly labeled data leads to an inaccurate classifier. We propose a novel AL framework to construct a robust classification model by minimizing noise levels. Our approach includes an assignment model that optimally assigns query points to labelers, aiming to minimize the maximum possible noise within each cycle. Additionally, we introduce a new sampling method to identify the best query points, reducing the impact of label noise on classifier performance. Our experiments demonstrate that our approach significantly improves classification performance compared to several benchmark methods.
【6】Reassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning
标题：重新评估监督微调的作用：VLM推理中的实证研究
链接：https://arxiv.org/abs/2512.12690
摘要：Recent advances in vision-language models (VLMs) reasoning have been largely attributed to the rise of reinforcement Learning (RL), which has shifted the community's focus away from the supervised fine-tuning (SFT) paradigm. Many studies suggest that introducing the SFT stage not only fails to improve reasoning ability but may also negatively impact model training. In this study, we revisit this RL-centric belief through a systematic and controlled comparison of SFT and RL on VLM Reasoning. Using identical data sources, we find that the relative effectiveness of SFT and RL is conditional and strongly influenced by model capacity, data scale, and data distribution. Contrary to common assumptions, our findings show that SFT plays a crucial role across several scenarios: (1) Effectiveness for weaker models. SFT more reliably elicits reasoning capabilities in smaller or weaker VLMs. (2) Data efficiency. SFT with only 2K achieves comparable or better reasoning performance to RL with 20K. (3) Cross-modal transferability. SFT demonstrates stronger generalization across modalities. Moreover, we identify a pervasive issue of deceptive rewards, where higher rewards fail to correlate with better reasoning accuracy in RL. These results challenge the prevailing "RL over SFT" narrative. They highlight that the role of SFT may have been underestimated and support a more balanced post-training pipeline in which SFT and RL function as complementary components.
【7】Supervised Contrastive Frame Aggregation for Video Representation Learning
标题：用于视频表示学习的监督对比帧聚集
链接：https://arxiv.org/abs/2512.12549
备注：12 pages
摘要 ：We propose a supervised contrastive learning framework for video representation learning that leverages temporally global context. We introduce a video to image aggregation strategy that spatially arranges multiple frames from each video into a single input image. This design enables the use of pre trained convolutional neural network backbones such as ResNet50 and avoids the computational overhead of complex video transformer models. We then design a contrastive learning objective that directly compares pairwise projections generated by the model. Positive pairs are defined as projections from videos sharing the same label while all other projections are treated as negatives. Multiple natural views of the same video are created using different temporal frame samplings from the same underlying video. Rather than relying on data augmentation these frame level variations produce diverse positive samples with global context and reduce overfitting. Experiments on the Penn Action and HMDB51 datasets demonstrate that the proposed method outperforms existing approaches in classification accuracy while requiring fewer computational resources. The proposed Supervised Contrastive Frame Aggregation method learns effective video representations in both supervised and self supervised settings and supports video based tasks such as classification and captioning. The method achieves seventy six percent classification accuracy on Penn Action compared to forty three percent achieved by ViVIT and forty eight percent accuracy on HMDB51 compared to thirty seven percent achieved by ViVIT.
【8】Uncertainty Quantification for Machine Learning: One Size Does Not Fit All
标题：机器学习的不确定性量化：一种尺寸不适合所有人
链接：https://arxiv.org/abs/2512.12341
摘要：Proper quantification of predictive uncertainty is essential for the use of machine learning in safety-critical applications. Various uncertainty measures have been proposed for this purpose, typically claiming superiority over other measures. In this paper, we argue that there is no single best measure. Instead, uncertainty quantification should be tailored to the specific application. To this end, we use a flexible family of uncertainty measures that distinguishes between total, aleatoric, and epistemic uncertainty of second-order distributions. These measures can be instantiated with specific loss functions, so-called proper scoring rules, to control their characteristics, and we show that different characteristics are useful for different tasks. In particular, we show that, for the task of selective prediction, the scoring rule should ideally match the task loss. On the other hand, for out-of-distribution detection, our results confirm that mutual information, a widely used measure of epistemic uncertainty, performs best. Furthermore, in an active learning setting, epistemic uncertainty based on zero-one loss is shown to consistently outperform other uncertainty measures.
迁移|Zero/Few/One-Shot|自适应(11篇)
【1】ADHint: Adaptive Hints with Difficulty Priors for Reinforcement Learning
标题：ADHint：具有强化学习困难先验的自适应提示
链接：https://arxiv.org/abs/2512.13095
摘要：To combine the advantages of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), recent methods have integrated ''hints'' into post-training, which are prefix segments of complete reasoning trajectories, aiming for powerful knowledge expansion and reasoning generalization. However, existing hint-based RL methods typically ignore difficulty when scheduling hint ratios and estimating relative advantages, leading to unstable learning and excessive imitation of off-policy hints. In this work, we propose ADHint, which treats difficulty as a key factor in both hint-ratio schedule and relative-advantage estimation to achieve a better trade-off between exploration and imitation. Specifically, we propose Adaptive Hint with Sample Difficulty Prior, which evaluates each sample's difficulty under the policy model and accordingly schedules an appropriate hint ratio to guide its rollouts. We also introduce Consistency-based Gradient Modulation and Selective Masking for Hint Preservation to modulate token-level gradients within hints, preventing biased and destructive updates. Additionally, we propose Advantage Estimation with Rollout Difficulty Posterior, which leverages the relative difficulty of rollouts with and without hints to estimate their respective advantages, thereby achieving more balanced updates. Extensive experiments across diverse modalities, model scales, and domains demonstrate that ADHint delivers superior reasoning ability and out-of-distribution generalization, consistently surpassing existing methods in both pass@1 and avg@8. Our code and dataset will be made publicly available upon paper acceptance.
【2】Multi-fidelity aerodynamic data fusion by autoencoder transfer learning
标题：通过自动编码器传输学习实现多保真气动数据融合
链接：https://arxiv.org/abs/2512.13069
备注：29 pages, 13 figures
摘要：Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling. This limitation motivates the development of multi-fidelity strategies that leverage inexpensive low-fidelity information without compromising accuracy. Addressing this challenge, this work presents a multi-fidelity deep learning framework that combines autoencoder-based transfer learning with a newly developed Multi-Split Conformal Prediction (MSCP) strategy to achieve uncertainty-aware aerodynamic data fusion under extreme data scarcity. The methodology leverages abundant Low-Fidelity (LF) data to learn a compact latent physics representation, which acts as a frozen knowledge base for a decoder that is subsequently fine-tuned using scarce HF samples. Tested on surface-pressure distributions for NACA airfoils (2D) and a transonic wing (3D) databases, the model successfully corrects LF deviations and achieves high-accuracy pressure predictions using minimal HF training data. Furthermore, the MSCP framework produces robust, actionable uncertainty bands with pointwise coverage exceeding 95%. By combining extreme data efficiency with uncertainty quantification, this work offers a scalable and reliable solution for aerodynamic regression in data-scarce environments.
【3】Alada: Alternating Adaptation of Momentum Method for Memory-Efficient Matrix Optimization
标题：Alada：交替调整动量方法以实现内存高效矩阵优化
链接：https://arxiv.org/abs/2512.13034
摘要 ：This work proposes Alada, an adaptive momentum method for stochastic optimization over large-scale matrices. Alada employs a rank-one factorization approach to estimate the second moment of gradients, where factors are updated alternatively to minimize the estimation error. Alada achieves sublinear memory overheads and can be readily extended to optimizing tensor-shaped variables.We also equip Alada with a first moment estimation rule, which enhances the algorithm's robustness without incurring additional memory overheads. The theoretical performance of Alada aligns with that of traditional methods such as Adam. Numerical studies conducted on several natural language processing tasks demonstrate the reduction in memory overheads and the robustness in training large models relative to Adam and its variants.
【4】TRACER: Transfer Learning based Real-time Adaptation for Clinical Evolving Risk
标题：TRABER：基于迁移学习的临床演变风险实时适应
链接：https://arxiv.org/abs/2512.12795
摘要：Clinical decision support tools built on electronic health records often experience performance drift due to temporal population shifts, particularly when changes in the clinical environment initially affect only a subset of patients, resulting in a transition to mixed populations. Such case-mix changes commonly arise following system-level operational updates or the emergence of new diseases, such as COVID-19. We propose TRACER (Transfer Learning-based Real-time Adaptation for Clinical Evolving Risk), a framework that identifies encounter-level transition membership and adapts predictive models using transfer learning without full retraining. In simulation studies, TRACER outperformed static models trained on historical or contemporary data. In a real-world application predicting hospital admission following emergency department visits across the COVID-19 transition, TRACER improved both discrimination and calibration. TRACER provides a scalable approach for maintaining robust predictive performance under evolving and heterogeneous clinical conditions.
【5】OLR-WAA: Adaptive and Drift-Resilient Online Regression with Dynamic Weighted Averaging
标题：OLR-WAA：具有动态加权平均的自适应和漂移弹性在线回归
链接：https://arxiv.org/abs/2512.12779
摘要：Real-world datasets frequently exhibit evolving data distributions, reflecting temporal variations and underlying shifts. Overlooking this phenomenon, known as concept drift, can substantially degrade the predictive performance of the model. Furthermore, the presence of hyperparameters in online models exacerbates this issue, as these parameters are typically fixed and lack the flexibility to dynamically adjust to evolving data. This paper introduces "OLR-WAA: An Adaptive and Drift-Resilient Online Regression with Dynamic Weighted Average", a hyperparameter-free model designed to tackle the challenges of non-stationary data streams and enable effective, continuous adaptation. The objective is to strike a balance between model stability and adaptability. OLR-WAA incrementally updates its base model by integrating incoming data streams, utilizing an exponentially weighted moving average. It further introduces a unique optimization mechanism that dynamically detects concept drift, quantifies its magnitude, and adjusts the model based on real-time data characteristics. Rigorous evaluations show that it matches batch regression performance in static settings and consistently outperforms or rivals state-of-the-art online models, confirming its effectiveness. Concept drift datasets reveal a performance gap that OLR-WAA effectively bridges, setting it apart from other online models. In addition, the model effectively handles confidence-based scenarios through a conservative update strategy that prioritizes stable, high-confidence data points. Notably, OLR-WAA converges rapidly, consistently yielding higher R2 values compared to other online models.
【6】Self-Motivated Growing Neural Network for Adaptive Architecture via Local Structural Plasticity
标题：通过局部结构可塑性实现自适应建筑的自我激励生长神经网络
链接：https://arxiv.org/abs/2512.12713
摘要：Control policies in deep reinforcement learning are often implemented with fixed-capacity multilayer perceptrons trained by backpropagation, which lack structural plasticity and depend on global error signals. This paper introduces the Self-Motivated Growing Neural Network (SMGrNN), a controller whose topology evolves online through a local Structural Plasticity Module (SPM). The SPM monitors neuron activations and edge-wise weight update statistics over short temporal windows and uses these signals to trigger neuron insertion and pruning, while synaptic weights are updated by a standard gradient-based optimizer. This allows network capacity to be regulated during learning without manual architectural tuning. SMGrNN is evaluated on control benchmarks via policy distillation. Compared with multilayer perceptron baselines, it achieves similar or higher returns, lower variance, and task-appropriate network sizes. Ablation studies with growth disabled and growth-only variants isolate the role of structural plasticity, showing that adaptive topology improves reward stability. The local and modular design of SPM enables future integration of a Hebbian plasticity module and spike-timing-dependent plasticity, so that SMGrNN can support both artificial and spiking neural implementations driven by local rules.
【7】Efficient Vision-Language Reasoning via Adaptive Token Pruning
标题：通过自适应令牌修剪的高效视觉语言推理
链接：https://arxiv.org/abs/2512.12701
备注：10 pages, 3 figures. Expanded version of an extended abstract accepted at NeurIPS 2025 Workshop on VLM4RWD. Presents methodology and preliminary experimental results
摘要 ：Real-world deployment of Vision-Language Models (VLMs) is hindered by high computational demands, as existing architectures inefficiently process all tokens uniformly. We introduce Adaptive Token Pruning (ATP), a dynamic inference mechanism that retains only the most informative tokens based on contextual relevance. ATP operates at the vision-language interface, assigning a hybrid importance score combining ViT CLS attention (intra-modal saliency) and CLIP text-image similarity (inter-modal relevance) to keep top-K tokens for the LLM. Unlike static compression, ATP adapts to each input without modifying the backbone. Proposed as a lightweight gating module, ATP is compatible with popular backbones like BLIP-2, LLaVA, and Flamingo. Preliminary evaluations across VQAv2, GQA, and COCO indicate that ATP reduces inference FLOPs by around 40% and achieves roughly 1.5x speedups in end-to-end latency with negligible accuracy loss (less than 1%). Qualitative analyses suggest ATP preserves visual grounding and enhances interpretability. Beyond efficiency, we investigate robustness under corruptions; observations suggest adaptive pruning suppresses spurious correlations, improving stability. These findings imply that resource-constrained inference and model reliability are not competing objectives. Finally, we discuss ATP's role in efficient multimodal edge computing pipelines.
【8】Learning to Get Up Across Morphologies: Zero-Shot Recovery with a Unified Humanoid Policy
标题：学会跨越形态学：采用统一的人形机器人政策进行Zero-Shot恢复
链接：https://arxiv.org/abs/2512.12230
备注：Accepted at 28th RoboCup International Symposium
摘要：Fall recovery is a critical skill for humanoid robots in dynamic environments such as RoboCup, where prolonged downtime often decides the match. Recent techniques using deep reinforcement learning (DRL) have produced robust get-up behaviors, yet existing methods require training of separate policies for each robot morphology. This paper presents a single DRL policy capable of recovering from falls across seven humanoid robots with diverse heights (0.48 - 0.81 m), weights (2.8 - 7.9 kg), and dynamics. Trained with CrossQ, the unified policy transfers zero-shot up to 86 +/- 7% (95% CI [81, 89]) on unseen morphologies, eliminating the need for robot-specific training. Comprehensive leave-one-out experiments, morph scaling analysis, and diversity ablations show that targeted morphological coverage improves zero-shot generalization. In some cases, the shared policy even surpasses the specialist baselines. These findings illustrate the practicality of morphology-agnostic control for fall recovery, laying the foundation for generalist humanoid control. The software is open-source and available at: https://github.com/utra-robosoccer/unified-humanoid-getup
【9】Adaptive Path Integral Diffusion: AdaPID
标题：自适应路径积分扩散：AdaID
链接：https://arxiv.org/abs/2512.11858
备注：51 pages, 17 figures
摘要：Diffusion-based samplers -- Score Based Diffusions, Bridge Diffusions and Path Integral Diffusions -- match a target at terminal time, but the real leverage comes from choosing the schedule that governs the intermediate-time dynamics. We develop a path-wise schedule -- selection gramework for Harmonic PID with a time-varying stiffness, exploiting Piece-Wise-Constant(PWC) parametrizations and a simple hierarchical refinement. We introduce schedule-sensitive Quality-of-Sampling (QoS) diagnostics. Assuming a Gaussian-Mixture (GM) target, we retain closed-form Green functions' ration and numerically stable, Neural-Network free oracles for predicted-state maps and score. Experiments in 2D show that QoS driven PWC schedules consistently improve early-exit fidelity, tail accuracy, conditioning of the dynamics, and speciation (label-selection) timing at fixed integration budgets.
【10】Adaptive Sampling for Hydrodynamic Stability
标题：水动力稳定性的自适应抽样
链接：https://arxiv.org/abs/2512.13532
摘要：An adaptive sampling approach for efficient detection of bifurcation boundaries in parametrized fluid flow problems is presented herein. The study extends the machine-learning approach of Silvester (Machine Learning for Hydrodynamic Stability, arXiv:2407.09572), where a classifier network was trained on preselected simulation data to identify bifurcated and nonbifurcated flow regimes. In contrast, the proposed methodology introduces adaptivity through a flow-based deep generative model that automatically refines the sampling of the parameter space. The strategy has two components: a classifier network maps the flow parameters to a bifurcation probability, and a probability density estimation technique (KRnet) for the generation of new samples at each adaptive step. The classifier output provides a probabilistic measure of flow stability, and the Shannon entropy of these predictions is employed as an uncertainty indicator. KRnet is trained to approximate a probability density function that concentrates sampling in regions of high entropy, thereby directing computational effort towards the evolving bifurcation boundary. This coupling between classification and generative modeling establishes a feedback-driven adaptive learning process analogous to error-indicator based refinement in contemporary partial differential equation solution strategies. Starting from a uniform parameter distribution, the new approach achieves accurate bifurcation boundary identification with significantly fewer Navier--Stokes simulations, providing a scalable foundation for high-dimensional stability analysis.
【11】Robust Outlier Detection and Low-Latency Concept Drift Adaptation for Data Stream Regression: A Dual-Channel Architecture
标题：数据流回归的稳健异常点检测和低延迟概念漂移适应：双通道架构
链接：https://arxiv.org/abs/2512.12289
摘要：Outlier detection and concept drift detection represent two challenges in data analysis. Most studies address these issues separately. However, joint detection mechanisms in regression remain underexplored, where the continuous nature of output spaces makes distinguishing drifts from outliers inherently challenging. To address this, we propose a novel robust regression framework for joint outlier and concept drift detection. Specifically, we introduce a dual-channel decision process that orchestrates prediction residuals into two coupled logic flows: a rapid response channel for filtering point outliers and a deep analysis channel for diagnosing drifts. We further develop the Exponentially Weighted Moving Absolute Deviation with Distinguishable Types (EWMAD-DT) detector to autonomously differentiate between abrupt and incremental drifts via dynamic thresholding. Comprehensive experiments on both synthetic and real-world datasets demonstrate that our unified framework, enhanced by EWMAD-DT, exhibits superior detection performance even when point outliers and concept drifts coexist.
强化学习(5篇)
【1】Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models
标题
：Nemotron-Cascade：扩展通用推理模型的级联强化学习
链接：https://arxiv.org/abs/2512.13607
备注：We publicly release the Nemotron-Cascade models and the full collection of training data at: https://huggingface.co/collections/nvidia/nemotron-cascade
摘要：Building general-purpose reasoning models with reinforcement learning (RL) entails substantial cross-domain heterogeneity, including large variation in inference-time response lengths and verification latency. Such variability complicates the RL infrastructure, slows training, and makes training curriculum (e.g., response length extension) and hyperparameter selection challenging. In this work, we propose cascaded domain-wise reinforcement learning (Cascade RL) to develop general-purpose reasoning models, Nemotron-Cascade, capable of operating in both instruct and deep thinking modes. Departing from conventional approaches that blend heterogeneous prompts from different domains, Cascade RL orchestrates sequential, domain-wise RL, reducing engineering complexity and delivering state-of-the-art performance across a wide range of benchmarks. Notably, RLHF for alignment, when used as a pre-step, boosts the model's reasoning ability far beyond mere preference optimization, and subsequent domain-wise RLVR stages rarely degrade the benchmark performance attained in earlier domains and may even improve it (see an illustration in Figure 1). Our 14B model, after RL, outperforms its SFT teacher, DeepSeek-R1-0528, on LiveCodeBench v5/v6/Pro and achieves silver-medal performance in the 2025 International Olympiad in Informatics (IOI). We transparently share our training and data recipes.
【2】Tackling Snow-Induced Challenges: Safe Autonomous Lane-Keeping with Robust Reinforcement Learning
标题：应对降雪引发的挑战：通过稳健的强化学习实现安全自主车道保持
链接：https://arxiv.org/abs/2512.12987
摘要：This paper proposes two new algorithms for the lane keeping system (LKS) in autonomous vehicles (AVs) operating under snowy road conditions. These algorithms use deep reinforcement learning (DRL) to handle uncertainties and slippage. They include Action-Robust Recurrent Deep Deterministic Policy Gradient (AR-RDPG) and end-to-end Action-Robust convolutional neural network Attention Deterministic Policy Gradient (AR-CADPG), two action-robust approaches for decision-making. In the AR-RDPG method, within the perception layer, camera images are first denoised using multi-scale neural networks. Then, the centerline coefficients are extracted by a pre-trained deep convolutional neural network (DCNN). These coefficients, concatenated with the driving characteristics, are used as input to the control layer. The AR-CADPG method presents an end-to-end approach in which a convolutional neural network (CNN) and an attention mechanism are integrated within a DRL framework. Both methods are first trained in the CARLA simulator and validated under various snowy scenarios. Real-world experiments on a Jetson Nano-based autonomous vehicle confirm the feasibility and stability of the learned policies. Among the two models, the AR-CADPG approach demonstrates superior path-tracking accuracy and robustness, highlighting the effectiveness of combining temporal memory, adversarial resilience, and attention mechanisms in AVs.
【3】World Models Unlock Optimal Foraging Strategies in Reinforcement Learning Agents
标题：世界模型确定强化学习代理中的最佳觅食策略
链接：https://arxiv.org/abs/2512.12548
备注：14 pages, 6 figures
摘要：Patch foraging involves the deliberate and planned process of determining the optimal time to depart from a resource-rich region and investigate potentially more beneficial alternatives. The Marginal Value Theorem (MVT) is frequently used to characterize this process, offering an optimality model for such foraging behaviors. Although this model has been widely used to make predictions in behavioral ecology, discovering the computational mechanisms that facilitate the emergence of optimal patch-foraging decisions in biological foragers remains under investigation. Here, we show that artificial foragers equipped with learned world models naturally converge to MVT-aligned strategies. Using a model-based reinforcement learning agent that acquires a parsimonious predictive representation of its environment, we demonstrate that anticipatory capabilities, rather than reward maximization alone, drive efficient patch-leaving behavior. Compared with standard model-free RL agents, these model-based agents exhibit decision patterns similar to many of their biological counterparts, suggesting that predictive world models can serve as a foundation for more explainable and biologically grounded decision-making in AI systems. Overall, our findings highlight the value of ecological optimality principles for advancing interpretable and adaptive AI.
【4】Goal Reaching with Eikonal-Constrained Hierarchical Quasimetric Reinforcement Learning
标题：利用Eikonal约束分层准度量强化学习实现目标
链接：https://arxiv.org/abs/2512.12046
摘要：Goal-Conditioned Reinforcement Learning (GCRL) mitigates the difficulty of reward design by framing tasks as goal reaching rather than maximizing hand-crafted reward signals. In this setting, the optimal goal-conditioned value function naturally forms a quasimetric, motivating Quasimetric RL (QRL), which constrains value learning to quasimetric mappings and enforces local consistency through discrete, trajectory-based constraints. We propose Eikonal-Constrained Quasimetric RL (Eik-QRL), a continuous-time reformulation of QRL based on the Eikonal Partial Differential Equation (PDE). This PDE-based structure makes Eik-QRL trajectory-free, requiring only sampled states and goals, while improving out-of-distribution generalization. We provide theoretical guarantees for Eik-QRL and identify limitations that arise under complex dynamics. To address these challenges, we introduce Eik-Hierarchical QRL (Eik-HiQRL), which integrates Eik-QRL into a hierarchical decomposition. Empirically, Eik-HiQRL achieves state-of-the-art performance in offline goal-conditioned navigation and yields consistent gains over QRL in manipulation tasks, matching temporal-difference methods.
【5】Mirror Mode in Fire Emblem: Beating Players at their own Game with Imitation and Reinforcement Learning
标题
：火焰徽章中的镜子模式：通过模仿和强化学习在自己的游戏中击败玩家
链接：https://arxiv.org/abs/2512.11902
摘要：Enemy strategies in turn-based games should be surprising and unpredictable. This study introduces Mirror Mode, a new game mode where the enemy AI mimics the personal strategy of a player to challenge them to keep changing their gameplay. A simplified version of the Nintendo strategy video game Fire Emblem Heroes has been built in Unity, with a Standard Mode and a Mirror Mode. Our first set of experiments find a suitable model for the task to imitate player demonstrations, using Reinforcement Learning and Imitation Learning: combining Generative Adversarial Imitation Learning, Behavioral Cloning, and Proximal Policy Optimization. The second set of experiments evaluates the constructed model with player tests, where models are trained on demonstrations provided by participants. The gameplay of the participants indicates good imitation in defensive behavior, but not in offensive strategies. Participant's surveys indicated that they recognized their own retreating tactics, and resulted in an overall higher player-satisfaction for Mirror Mode. Refining the model further may improve imitation quality and increase player's satisfaction, especially when players face their own strategies. The full code and survey results are stored at: https://github.com/YannaSmid/MirrorMode
元学习(2篇)
【1】Meta-Continual Mobility Forecasting for Proactive Handover Prediction
标题：主动切换预测的元连续移动性预测
链接：https://arxiv.org/abs/2512.11841
备注：6 pages, 1 figure
摘要：Short-term mobility forecasting is a core requirement for proactive handover (HO) in cellular networks. Real-world mobility is highly non-stationary: abrupt turns, rapid speed changes, and unpredictable user behavior cause conventional predictors to drift, leading to mistimed or failed handovers. We propose a lightweight meta-continual forecasting framework that integrates a GRU-based predictor, Reptile meta-initialization for fast few-shot adaptation, and an EWMA residual detector that triggers compact online updates only when drift occurs. Evaluated on a reproducible GeoLife and DeepMIMO pipeline, our method achieves 4.46 m ADE and 7.79 m FDE in zero-shot settings, improves few-shot ADE to 3.71 m at 10-shot, and enables recovery from abrupt drift about 2 to 3 times faster than an offline GRU. When applied to downstream HO prediction, the approach improves F1 to 0.83 and AUROC to 0.90, with substantial reductions in missed-HO and ping-pong events. The model is lightweight (128k parameters) and suitable for edge deployment in 5G and 6G systems.
【2】Meta-GPT: Decoding the Metasurface Genome with Generative Artificial Intelligence
标题：Meta-GPT：用生成人工智能解码元表面基因组
链接：https://arxiv.org/abs/2512.12888
备注：Keywords: Physics-informed machine learning; Transformer models; Reinforcement learning; Chain-of-thought reasoning; Metasurfaces; Nanophotonics; Inverse design
摘要：Advancing artificial intelligence for physical sciences requires representations that are both interpretable and compatible with the underlying laws of nature. We introduce METASTRINGS, a symbolic language for photonics that expresses nanostructures as textual sequences encoding materials, geometries, and lattice configurations. Analogous to molecular textual representations in chemistry, METASTRINGS provides a framework connecting human interpretability with computational design by capturing the structural hierarchy of photonic metasurfaces. Building on this representation, we develop Meta-GPT, a foundation transformer model trained on METASTRINGS and finetuned with physics-informed supervised, reinforcement, and chain-of-thought learning. Across various design tasks, the model achieves <3% mean-squared spectral error and maintains >98% syntactic validity, generating diverse metasurface prototypes whose experimentally measured optical responses match their target spectra. These results demonstrate that Meta-GPT can learn the compositional rules of light-matter interactions through METASTRINGS, laying a rigorous foundation for AI-driven photonics and representing an important step toward a metasurface genome project.
医学相关(4篇)
【1】From Code to Field: Evaluating the Robustness of Convolutional Neural Networks for Disease Diagnosis in Mango Leaves
标题：从代码到字段：评估卷积神经网络用于芒果叶疾病诊断的鲁棒性
链接：https://arxiv.org/abs/2512.13641
备注：This work was presented at the BRACIS 2025 conference in Fortaleza
摘要 ：The validation and verification of artificial intelligence (AI) models through robustness assessment are essential to guarantee the reliable performance of intelligent systems facing real-world challenges, such as image corruptions including noise, blurring, and weather variations. Despite the global importance of mango (Mangifera indica L.), there is a lack of studies on the robustness of models for the diagnosis of disease in its leaves. This paper proposes a methodology to evaluate convolutional neural networks (CNNs) under adverse conditions. We adapted the MangoLeafDB dataset, generating MangoLeafDB-C with 19 types of artificial corruptions at five severity levels. We conducted a benchmark comparing five architectures: ResNet-50, ResNet-101, VGG-16, Xception, and LCNN (the latter being a lightweight architecture designed specifically for mango leaf diagnosis). The metrics include the F1 score, the corruption error (CE) and the relative mean corruption error (relative mCE). The results show that LCNN outperformed complex models in corruptions that can be present in real-world scenarios such as Defocus Blur, Motion Blur, while also achieving the lowest mCE. Modern architectures (e.g., ResNet-101) exhibited significant performance degradation in corrupted scenarios, despite their high accuracy under ideal conditions. These findings suggest that lightweight and specialized models may be more suitable for real-world applications in edge devices, where robustness and efficiency are critical. The study highlights the need to incorporate robustness assessments in the development of intelligent systems for agriculture, particularly in regions with technological limitations.
【2】Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains
标题：煎饼：跨生物医学领域一致的多协议图像分割
链接：https://arxiv.org/abs/2512.13534
备注：Accepted at NeurIPS 2025. Code available at: https://github.com/mariannerakic/Pancakes
摘要：A single biomedical image can be meaningfully segmented in multiple ways, depending on the desired application. For instance, a brain MRI can be segmented according to tissue types, vascular territories, broad anatomical regions, fine-grained anatomy, or pathology, etc. Existing automatic segmentation models typically either (1) support only a single protocol, the one they were trained on, or (2) require labor-intensive manual prompting to specify the desired segmentation. We introduce Pancakes, a framework that, given a new image from a previously unseen domain, automatically generates multi-label segmentation maps for multiple plausible protocols, while maintaining semantic consistency across related images. Pancakes introduces a new problem formulation that is not currently attainable by existing foundation models. In a series of experiments on seven held-out datasets, we demonstrate that our model can significantly outperform existing foundation models in producing several plausible whole-image segmentations, that are semantically coherent across images.
【3】MedInsightBench: Evaluating Medical Analytics Agents Through Multi-Step Insight Discovery in Multimodal Medical Data
标题：MedInsightBench：通过多模式医疗数据中的多步骤洞察发现来评估医疗分析代理
链接：https://arxiv.org/abs/2512.13297
摘要：In medical data analysis, extracting deep insights from complex, multi-modal datasets is essential for improving patient care, increasing diagnostic accuracy, and optimizing healthcare operations. However, there is currently a lack of high-quality datasets specifically designed to evaluate the ability of large multi-modal models (LMMs) to discover medical insights. In this paper, we introduce MedInsightBench, the first benchmark that comprises 332 carefully curated medical cases, each annotated with thoughtfully designed insights. This benchmark is intended to evaluate the ability of LMMs and agent frameworks to analyze multi-modal medical image data, including posing relevant questions, interpreting complex findings, and synthesizing actionable insights and recommendations. Our analysis indicates that existing LMMs exhibit limited performance on MedInsightBench, which is primarily attributed to their challenges in extracting multi-step, deep insights and the absence of medical expertise. Therefore, we propose MedInsightAgent, an automated agent framework for medical data analysis, composed of three modules: Visual Root Finder, Analytical Insight Agent, and Follow-up Question Composer. Experiments on MedInsightBench highlight pervasive challenges and demonstrate that MedInsightAgent can improve the performance of general LMMs in medical data insight discovery.
【4】AI-Augmented Pollen Recognition in Optical and Holographic Microscopy for Veterinary Imaging
标题：兽医成像光学和全息显微镜中的人工智能增强花粉识别
链接：https://arxiv.org/abs/2512.12101
备注：10 pages, 10 figures, 2 tables, 22 references. Journal submission undergoing peer review
摘要：We present a comprehensive study on fully automated pollen recognition across both conventional optical and digital in-line holographic microscopy (DIHM) images of sample slides. Visually recognizing pollen in unreconstructed holographic images remains challenging due to speckle noise, twin-image artifacts and substantial divergence from bright-field appearances. We establish the performance baseline by training YOLOv8s for object detection and MobileNetV3L for classification on a dual-modality dataset of automatically annotated optical and affinely aligned DIHM images. On optical data, detection mAP50 reaches 91.3% and classification accuracy reaches 97%, whereas on DIHM data, we achieve only 8.15% for detection mAP50 and 50% for classification accuracy. Expanding the bounding boxes of pollens in DIHM images over those acquired in aligned optical images achieves 13.3% for detection mAP50 and 54% for classification accuracy. To improve object detection in DIHM images, we employ a Wasserstein GAN with spectral normalization (WGAN-SN) to create synthetic DIHM images, yielding an FID score of 58.246. Mixing real-world and synthetic data at the 1.0 : 1.5 ratio for DIHM images improves object detection up to 15.4%. These results demonstrate that GAN-based augmentation can reduce the performance divide, bringing fully automated DIHM workflows for veterinary imaging a small but important step closer to practice.
蒸馏|知识提取(4篇)
【1】Distillation of Discrete Diffusion by Exact Conditional Distribution Matching
标题：精确条件分布匹配法蒸馏离散扩散
链接：https://arxiv.org/abs/2512.12889
备注：[work in progress]
摘要：Discrete diffusion models (DDMs) are a powerful class of generative models for categorical data, but they typically require many function evaluations for a single sample, making inference expensive. Existing acceleration methods either rely on approximate simulators, such as $τ$-leaping, or on distillation schemes that train new student models and auxiliary networks with proxy objectives. We propose a simple and principled distillation alternative based on \emph{conditional distribution matching}. Our key observation is that the reverse conditional distribution of clean data given a noisy state, $p_{0\mid t}(x_0 \mid x_t)$, admits a Markov decomposition through intermediate times and can be recovered from marginal density ratios and the known forward CTMC kernel. We exploit this structure to define distillation objectives that directly match conditional distributions between a pre-trained teacher and a low-NFE student, both for one-step and few-step samplers.
【2】Animus3D: Text-driven 3D Animation via Motion Score Distillation
标题：Animus 3D：通过运动配乐蒸馏的文本驱动3D动画
链接：https://arxiv.org/abs/2512.12534
备注：SIGGRAPH Asia 2025
摘要：We present Animus3D, a text-driven 3D animation framework that generates motion field given a static 3D asset and text prompt. Previous methods mostly leverage the vanilla Score Distillation Sampling (SDS) objective to distill motion from pretrained text-to-video diffusion, leading to animations with minimal movement or noticeable jitter. To address this, our approach introduces a novel SDS alternative, Motion Score Distillation (MSD). Specifically, we introduce a LoRA-enhanced video diffusion model that defines a static source distribution rather than pure noise as in SDS, while another inversion-based noise estimation technique ensures appearance preservation when guiding motion. To further improve motion fidelity, we incorporate explicit temporal and spatial regularization terms that mitigate geometric distortions across time and space. Additionally, we propose a motion refinement module to upscale the temporal resolution and enhance fine-grained details, overcoming the fixed-resolution constraints of the underlying video model. Extensive experiments demonstrate that Animus3D successfully animates static 3D assets from diverse text prompts, generating significantly more substantial and detailed motion than state-of-the-art baselines while maintaining high visual integrity. Code will be released at https://qiisun.github.io/animus3d_page.
【3】Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling
标题：用于增强尖峰信息LFP建模的跨模式表示知识提炼
链接：https://arxiv.org/abs/2512.12461
备注：Published at the 39th Annual Conference on Neural Information Processing Systems 2025. Code is available at https://github.com/ShanechiLab/CrossModalDistillation
摘要：Local field potentials (LFPs) can be routinely recorded alongside spiking activity in intracortical neural experiments, measure a larger complementary spatiotemporal scale of brain activity for scientific inquiry, and can offer practical advantages over spikes, including greater long-term stability, robustness to electrode degradation, and lower power requirements. Despite these advantages, recent neural modeling frameworks have largely focused on spiking activity since LFP signals pose inherent modeling challenges due to their aggregate, population-level nature, often leading to lower predictive power for downstream task variables such as motor behavior. To address this challenge, we introduce a cross-modal knowledge distillation framework that transfers high-fidelity representational knowledge from pretrained multi-session spike transformer models to LFP transformer models. Specifically, we first train a teacher spike model across multiple recording sessions using a masked autoencoding objective with a session-specific neural tokenization strategy. We then align the latent representations of the student LFP model to those of the teacher spike model. Our results show that the Distilled LFP models consistently outperform single- and multi-session LFP baselines in both fully unsupervised and supervised settings, and can generalize to other sessions without additional distillation while maintaining superior performance. These findings demonstrate that cross-modal knowledge distillation is a powerful and scalable approach for leveraging high-performing spike models to develop more accurate LFP models.
【4】EEG-DLite: Dataset Distillation for Efficient Large EEG Model Training
标题：EEG-DLite：数据集蒸馏，用于高效的大型脑电模型训练
链接：https://arxiv.org/abs/2512.12210
备注：Accepted by AAAI-2026
摘要：Large-scale EEG foundation models have shown strong generalization across a range of downstream tasks, but their training remains resource-intensive due to the volume and variable quality of EEG data. In this work, we introduce EEG-DLite, a data distillation framework that enables more efficient pre-training by selectively removing noisy and redundant samples from large EEG datasets. EEG-DLite begins by encoding EEG segments into compact latent representations using a self-supervised autoencoder, allowing sample selection to be performed efficiently and with reduced sensitivity to noise. Based on these representations, EEG-DLite filters out outliers and minimizes redundancy, resulting in a smaller yet informative subset that retains the diversity essential for effective foundation model training. Through extensive experiments, we demonstrate that training on only 5 percent of a 2,500-hour dataset curated with EEG-DLite yields performance comparable to, and in some cases better than, training on the full dataset across multiple downstream tasks. To our knowledge, this is the first systematic study of pre-training data distillation in the context of EEG foundation models. EEG-DLite provides a scalable and practical path toward more effective and efficient physiological foundation modeling. The code is available at https://github.com/t170815518/EEG-DLite.
推荐(1篇)
【1】Near-Zero-Overhead Freshness for Recommendation Systems via Inference-Side Model Updates
标题：通过推理端模型更新实现推荐系统的近乎零的新鲜度
链接：https://arxiv.org/abs/2512.12295
备注：13 Pages, 19 figures
摘要 ：Deep Learning Recommendation Models (DLRMs) underpin personalized services but face a critical freshness-accuracy tradeoff due to massive parameter synchronization overheads. Production DLRMs deploy decoupled training/inference clusters, where synchronizing petabyte-scale embedding tables (EMTs) causes multi-minute staleness, degrading recommendation quality and revenue. We observe that (1) inference nodes exhibit sustained CPU underutilization (peak <= 20%), and (2) EMT gradients possess intrinsic low-rank structure, enabling compact update representation. We present LiveUpdate, a system that eliminates inter-cluster synchronization by colocating Low-Rank Adaptation (LoRA) trainers within inference nodes. LiveUpdate addresses two core challenges: (1) dynamic rank adaptation via singular value monitoring to constrain memory overhead (<2% of EMTs), and (2) NUMA-aware resource scheduling with hardware-enforced QoS to eliminate update inference contention (P99 latency impact <20ms). Evaluations show LiveUpdate reduces update costs by 2x versus delta-update baselines while achieving higher accuracy within 1-hour windows. By transforming idle inference resources into freshness engines, LiveUpdate delivers online model updates while outperforming state-of-the-art delta-update methods by 0.04% to 0.24% in accuracy.
超分辨率|去噪|去模糊|去雾(1篇)
【1】Unified Control for Inference-Time Guidance of Denoising Diffusion Models
标题：去噪扩散模型推理时引导的统一控制
链接：https://arxiv.org/abs/2512.12339
摘要：Aligning diffusion model outputs with downstream objectives is essential for improving task-specific performance. Broadly, inference-time training-free approaches for aligning diffusion models can be categorized into two main strategies: sampling-based methods, which explore multiple candidate outputs and select those with higher reward signals, and gradient-guided methods, which use differentiable reward approximations to directly steer the generation process. In this work, we propose a universal algorithm, UniCoDe, which brings together the strengths of sampling and gradient-based guidance into a unified framework. UniCoDe integrates local gradient signals during sampling, thereby addressing the sampling inefficiency inherent in complex reward-based sampling approaches. By cohesively combining these two paradigms, UniCoDe enables more efficient sampling while offering better trade-offs between reward alignment and divergence from the diffusion unconditional prior. Empirical results demonstrate that UniCoDe remains competitive with state-of-the-art baselines across a range of tasks. The code is available at https://github.com/maurya-goyal10/UniCoDe
自动驾驶|车辆|车道检测等(3篇)
【1】Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic
标题：用于估计道路交通中预测占用网格的机器学习架构
链接：https://arxiv.org/abs/2512.12907
备注：Journal of Advances in Information Technology
摘要：This paper introduces a novel machine learning architecture for an efficient estimation of the probabilistic space-time representation of complex traffic scenarios. A detailed representation of the future traffic scenario is of significant importance for autonomous driving and for all active safety systems. In order to predict the future space-time representation of the traffic scenario, first the type of traffic scenario is identified and then the machine learning algorithm maps the current state of the scenario to possible future states. The input to the machine learning algorithms is the current state representation of a traffic scenario, termed as the Augmented Occupancy Grid (AOG). The output is the probabilistic space-time representation which includes uncertainties regarding the behaviour of the traffic participants and is termed as the Predicted Occupancy Grid (POG). The novel architecture consists of two Stacked Denoising Autoencoders (SDAs) and a set of Random Forests. It is then compared with the other two existing architectures that comprise of SDAs and DeconvNet. The architectures are validated with the help of simulations and the comparisons are made both in terms of accuracy and computational time. Also, a brief overview on the applications of POGs in the field of active safety is presented.
【2】Predicted-occupancy grids for vehicle safety applications based on autoencoders and the Random Forest algorithm
标题：基于自动编码器和随机森林算法的车辆安全应用的预测占用网格
链接：https://arxiv.org/abs/2512.12901
备注：2017 International Joint Conference on Neural Networks (IJCNN)
摘要：In this paper, a probabilistic space-time representation of complex traffic scenarios is predicted using machine learning algorithms. Such a representation is significant for all active vehicle safety applications especially when performing dynamic maneuvers in a complex traffic scenario. As a first step, a hierarchical situation classifier is used to distinguish the different types of traffic scenarios. This classifier is responsible for identifying the type of the road infrastructure and the safety-relevant traffic participants of the driving environment. With each class representing similar traffic scenarios, a set of Random Forests (RFs) is individually trained to predict the probabilistic space-time representation, which depicts the future behavior of traffic participants. This representation is termed as a Predicted-Occupancy Grid (POG). The input to the RFs is an Augmented Occupancy Grid (AOG). In order to increase the learning accuracy of the RFs and to perform better predictions, the AOG is reduced to low-dimensional features using a Stacked Denoising Autoencoder (SDA). The excellent performance of the proposed machine learning approach consisting of SDAs and RFs is demonstrated in simulations and in experiments with real vehicles. An application of POGs to estimate the criticality of traffic scenarios and to determine safe trajectories is also presented.
【3】Probability Estimation for Predicted-Occupancy Grids in Vehicle Safety Applications Based on Machine Learning
标题：基于机器学习的车辆安全应用中预测占用网格的概率估计
链接：https://arxiv.org/abs/2512.12896
备注：2016 IEEE Intelligent Vehicles Symposium
摘要 ：This paper presents a method to predict the evolution of a complex traffic scenario with multiple objects. The current state of the scenario is assumed to be known from sensors and the prediction is taking into account various hypotheses about the behavior of traffic participants. This way, the uncertainties regarding the behavior of traffic participants can be modelled in detail. In the first part of this paper a model-based approach is presented to compute Predicted-Occupancy Grids (POG), which are introduced as a grid-based probabilistic representation of the future scenario hypotheses. However, due to the large number of possible trajectories for each traffic participant, the model-based approach comes with a very high computational load. Thus, a machine-learning approach is adopted for the computation of POGs. This work uses a novel grid-based representation of the current state of the traffic scenario and performs the mapping to POGs. This representation consists of augmented cells in an occupancy grid. The adopted machine-learning approach is based on the Random Forest algorithm. Simulations of traffic scenarios are performed to compare the machine-learning with the model-based approach. The results are promising and could enable the real-time computation of POGs for vehicle safety applications. With this detailed modelling of uncertainties, crucial components in vehicle safety systems like criticality estimation and trajectory planning can be improved.
点云|SLAM|雷达|激光|深度RGBD相关(1篇)
【1】On the Approximation Power of SiLU Networks: Exponential Rates and Depth Efficiency
标题：SiLU网络的逼近能力：指数速率和深度效率
链接：https://arxiv.org/abs/2512.12132
备注：35 pages, 20 figures, submitted to the journal
摘要：This article establishes a comprehensive theoretical framework demonstrating that SiLU (Sigmoid Linear Unit) activation networks achieve exponential approximation rates for smooth functions with explicit and improved complexity control compared to classical ReLU-based constructions. We develop a novel hierarchical construction beginning with an efficient approximation of the square function $x^2$ more compact in depth and size than comparable ReLU realizations, such as those given by Yarotsky. This construction yields an approximation error decaying as $\mathcal{O}(ω^{-2k})$ using networks of depth $\mathcal{O}(1)$. We then extend this approach through functional composition to establish sharp approximation bounds for deep SiLU networks in approximating Sobolev-class functions, with total depth $\mathcal{O}(1)$ and size $\mathcal{O}(\varepsilon^{-d/n})$.
联邦学习|隐私保护|加密(5篇)
【1】ALIGN-FL: Architecture-independent Learning through Invariant Generative component sharing in Federated Learning
标题：ALIGN-FL：通过联邦学习中的不变生成组件共享实现架构独立学习
链接：https://arxiv.org/abs/2512.13316
备注：Accepted at 2025 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)
摘要：We present ALIGN-FL, a novel approach to distributed learning that addresses the challenge of learning from highly disjoint data distributions through selective sharing of generative components. Instead of exchanging full model parameters, our framework enables privacy-preserving learning by transferring only generative capabilities across clients, while the server performs global training using synthetic samples. Through complementary privacy mechanisms: DP-SGD with adaptive clipping and Lipschitz regularized VAE decoders and a stateful architecture supporting heterogeneous clients, we experimentally validate our approach on MNIST and Fashion-MNIST datasets with cross-domain outliers. Our analysis demonstrates that both privacy mechanisms effectively map sensitive outliers to typical data points while maintaining utility in extreme Non-IID scenarios typical of cross-silo collaborations. Index Terms: Client-invariant Learning, Federated Learning (FL), Privacy-preserving Generative Models, Non-Independent and Identically Distributed (Non-IID), Heterogeneous Architectures
【2】Noise-Resilient Quantum Aggregation on NISQ for Federated ADAS Learning
标题：NISQ上的抗噪量子聚合用于联邦ADAS学习
链接：https://arxiv.org/abs/2512.13196
备注：This paper was accepted and presented at WinTechCon 2025, Bangalore, India, and is published in IEEE Xplore
摘要：Advanced Driver Assistance Systems (ADAS) increasingly employ Federated Learning (FL) to collaboratively train models across distributed vehicular nodes while preserving data privacy. Yet, conventional FL aggregation remains susceptible to noise, latency, and security constraints inherent to real-time vehicular networks. This paper introduces Noise-Resilient Quantum Federated Learning (NR-QFL), a hybrid quantum-classical framework that enables secure, low-latency aggregation through variational quantum circuits (VQCs) operating under Noisy Intermediate-Scale Quantum (NISQ) conditions. The framework encodes model parameters as quantum states with adaptive gate reparameterization, ensuring bounded-error convergence and provable resilience under Completely Positive Trace-Preserving (CPTP) dynamics. NR-QFL employs quantum entropy-based client selection and multi-server coordination for fairness and stability. Empirical validation shows consistent convergence with reduced gradient variance, lower communication overhead, and enhanced noise tolerance under constrained edge conditions. The framework establishes a scalable foundation for quantum-enhanced federated learning, enabling secure, efficient, and dynamically stable ADAS intelligence at the vehicular edge.
【3】Federated Learning with Feedback Alignment
标题：具有反馈一致性的联邦学习
链接：https://arxiv.org/abs/2512.12762
摘要：Federated Learning (FL) enables collaborative training across multiple clients while preserving data privacy, yet it struggles with data heterogeneity, where clients' data are not distributed independently and identically (non-IID). This causes local drift, hindering global model convergence. To address this, we introduce Federated Learning with Feedback Alignment (FLFA), a novel framework that integrates feedback alignment into FL. FLFA uses the global model's weights as a shared feedback matrix during local training's backward pass, aligning local updates with the global model efficiently. This approach mitigates local drift with minimal additional computational cost and no extra communication overhead. Our theoretical analysis supports FLFA's design by showing how it alleviates local drift and demonstrates robust convergence for both local and global models. Empirical evaluations, including accuracy comparisons and measurements of local drift, further illustrate that FLFA can enhance other FL methods demonstrating its effectiveness.
【4】Spectral Sentinel: Scalable Byzantine-Robust Decentralized Federated Learning via Sketched Random Matrix Theory on Blockchain
标题：光谱哨兵：通过区块链上的草图随机矩阵理论进行可扩展的拜占庭鲁棒分散式联邦学习
链接：https://arxiv.org/abs/2512.12617
摘要：Decentralized federated learning (DFL) enables collaborative model training without centralized trust, but it remains vulnerable to Byzantine clients that poison gradients under heterogeneous (Non-IID) data. Existing defenses face a scalability trilemma: distance-based filtering (e.g., Krum) can reject legitimate Non-IID updates, geometric-median methods incur prohibitive $O(n^2 d)$ cost, and many certified defenses are evaluated only on models below 100M parameters. We propose Spectral Sentinel, a Byzantine detection and aggregation framework that leverages a random-matrix-theoretic signature: honest Non-IID gradients produce covariance eigenspectra whose bulk follows the Marchenko-Pastur law, while Byzantine perturbations induce detectable tail anomalies. Our algorithm combines Frequent Directions sketching with data-dependent MP tracking, enabling detection on models up to 1.5B parameters using $O(k^2)$ memory with $k \ll d$. Under a $(σ,f)$ threat model with coordinate-wise honest variance bounded by $σ^2$ and $f < 1/2$ adversaries, we prove $(ε,δ)$-Byzantine resilience with convergence rate $O(σf / \sqrt{T} + f^2 / T)$, and we provide a matching information-theoretic lower bound $Ω(σf / \sqrt{T})$, establishing minimax optimality. We implement the full system with blockchain integration on Polygon networks and validate it across 144 attack-aggregator configurations, achieving 78.4 percent average accuracy versus 48-63 percent for baseline methods.
【5】DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning
标题：DFedReweighting：去中心化联邦学习中面向对象的重新加权的统一框架
链接：https://arxiv.org/abs/2512.12022
摘要：Decentralized federated learning (DFL) has recently emerged as a promising paradigm that enables multiple clients to collaboratively train machine learning model through iterative rounds of local training, communication, and aggregation without relying on a central server which introduces potential vulnerabilities in conventional Federated Learning. Nevertheless, DFL systems continue to face a range of challenges, including fairness, robustness, etc. To address these challenges, we propose \textbf{DFedReweighting}, a unified aggregation framework designed to achieve diverse objectives in DFL systems via a objective-oriented reweighting aggregation at the final step of each learning round. Specifically, the framework first computes preliminary weights based on \textit{target performance metric} obtained from auxiliary dataset constructed using local data. These weights are then refined using \textit{customized reweighting strategy}, resulting in the final aggregation weights. Our results from the theoretical analysis demonstrate that the appropriate combination of the target performance metric and the customized reweighting strategy ensures linear convergence. Experimental results consistently show that our proposed framework significantly improves fairness and robustness against Byzantine attacks in diverse scenarios. Provided that appropriate target performance metrics and customized reweighting strategy are selected, our framework can achieve a wide range of desired learning objectives.
推理|分析|理解|解释(17篇)
【1】Error-Driven Prompt Optimization for Arithmetic Reasoning
标题：错误驱动的算术推理即时优化
链接：https://arxiv.org/abs/2512.13323
摘要：Recent advancements in artificial intelligence have sparked interest in industrial agents capable of supporting analysts in regulated sectors, such as finance and healthcare, within tabular data workflows. A key capability for such systems is performing accurate arithmetic operations on structured data while ensuring sensitive information never leaves secure, on-premises environments. Here, we introduce an error-driven optimization framework for arithmetic reasoning that enhances a Code Generation Agent (CGA), specifically applied to on-premises small language models (SLMs). Through a systematic evaluation of a leading SLM (Qwen3 4B), we find that while the base model exhibits fundamental limitations in arithmetic tasks, our proposed error-driven method, which clusters erroneous predictions to refine prompt-rules iteratively, dramatically improves performance, elevating the model's accuracy to 70.8\%. Our results suggest that developing reliable, interpretable, and industrially deployable AI assistants can be achieved not only through costly fine-tuning but also via systematic, error-driven prompt optimization, enabling small models to surpass larger language models (GPT-3.5 Turbo) in a privacy-compliant manner.
【2】AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning
标题：AutoTool：面向推理的动态工具选择与集成
链接：https://arxiv.org/abs/2512.13278
备注：Best Paper Award at ICCV 2025 Workshop on Multi-Modal Reasoning for Agentic Intelligence
摘要：Agentic reinforcement learning has advanced large language models (LLMs) to reason through long chain-of-thought trajectories while interleaving external tool use. Existing approaches assume a fixed inventory of tools, limiting LLM agents' adaptability to new or evolving toolsets. We present AutoTool, a framework that equips LLM agents with dynamic tool-selection capabilities throughout their reasoning trajectories. We first construct a 200k dataset with explicit tool-selection rationales across 1,000+ tools and 100+ tasks spanning mathematics, science, code generation, and multimodal reasoning. Building on this data foundation, AutoTool employs a dual-phase optimization pipeline: (i) supervised and RL-based trajectory stabilization for coherent reasoning, and (ii) KL-regularized Plackett-Luce ranking to refine consistent multi-step tool selection. Across ten diverse benchmarks, we train two base models, Qwen3-8B and Qwen2.5-VL-7B, with AutoTool. With fewer parameters, AutoTool consistently outperforms advanced LLM agents and tool-integration methods, yielding average gains of 6.4% in math & science reasoning, 4.5% in search-based QA, 7.7% in code generation, and 6.9% in multimodal understanding. In addition, AutoTool exhibits stronger generalization by dynamically leveraging unseen tools from evolving toolsets during inference.
【3】Weight Space Correlation Analysis: Quantifying Feature Utilization in Deep Learning Models
标题：权重空间相关性分析：量化深度学习模型中的特征利用率
链接：https://arxiv.org/abs/2512.13144
备注：46 pages
摘要：Deep learning models in medical imaging are susceptible to shortcut learning, relying on confounding metadata (e.g., scanner model) that is often encoded in image embeddings. The crucial question is whether the model actively utilizes this encoded information for its final prediction. We introduce Weight Space Correlation Analysis, an interpretable methodology that quantifies feature utilization by measuring the alignment between the classification heads of a primary clinical task and auxiliary metadata tasks. We first validate our method by successfully detecting artificially induced shortcut learning. We then apply it to probe the feature utilization of an SA-SonoNet model trained for Spontaneous Preterm Birth (sPTB) prediction. Our analysis confirmed that while the embeddings contain substantial metadata, the sPTB classifier's weight vectors were highly correlated with clinically relevant factors (e.g., birth weight) but decoupled from clinically irrelevant acquisition factors (e.g. scanner). Our methodology provides a tool to verify model trustworthiness, demonstrating that, in the absence of induced bias, the clinical model selectively utilizes features related to the genuine clinical signal.
【4】Wait, Wait, Wait... Why Do Reasoning Models Loop?
标题：等等，等等，等等……为什么推理模型会循环？
链接：https://arxiv.org/abs/2512.12895
摘要：Reasoning models (e.g., DeepSeek-R1) generate long chains of thought to solve harder problems, but they often loop, repeating the same text at low temperatures or with greedy decoding. We study why this happens and what role temperature plays. With open reasoning models, we find that looping is common at low temperature. Larger models tend to loop less, and distilled students loop significantly even when their teachers rarely do. This points to mismatches between the training distribution and the learned model, which we refer to as errors in learning, as a key cause. To understand how such errors cause loops, we introduce a synthetic graph reasoning task and demonstrate two mechanisms. First, risk aversion caused by hardness of learning: when the correct progress-making action is hard to learn but an easy cyclic action is available, the model puts relatively more probability on the cyclic action and gets stuck. Second, even when there is no hardness, Transformers show an inductive bias toward temporally correlated errors, so the same few actions keep being chosen and loops appear. Higher temperature reduces looping by promoting exploration, but it does not fix the errors in learning, so generations remain much longer than necessary at high temperature; in this sense, temperature is a stopgap rather than a holistic solution. We end with a discussion of training-time interventions aimed at directly reducing errors in learning.
【5】CoRe3D: Collaborative Reasoning as a Foundation for 3D Intelligence
标题：CoRe 3D：协作推理作为3D智能的基础
链接：https://arxiv.org/abs/2512.12768
摘要：Recent advances in large multimodal models suggest that explicit reasoning mechanisms play a critical role in improving model reliability, interpretability, and cross-modal alignment. While such reasoning-centric approaches have been proven effective in language and vision tasks, their extension to 3D remains underdeveloped. CoRe3D introduces a unified 3D understanding and generation reasoning framework that jointly operates over semantic and spatial abstractions, enabling high-level intent inferred from language to directly guide low-level 3D content formation. Central to this design is a spatially grounded reasoning representation that decomposes 3D latent space into localized regions, allowing the model to reason over geometry in a compositional and procedural manner. By tightly coupling semantic chain-of-thought inference with structured spatial reasoning, CoRe3D produces 3D outputs that exhibit strong local consistency and faithful alignment with linguistic descriptions.
【6】Causal inference and model explainability tools for retail
标题：零售业的因果推理和模型解释工具
链接：https://arxiv.org/abs/2512.12605
摘要：Most major retailers today have multiple divisions focused on various aspects, such as marketing, supply chain, online customer experience, store customer experience, employee productivity, and vendor fulfillment. They also regularly collect data corresponding to all these aspects as dashboards and weekly/monthly/quarterly reports. Although several machine learning and statistical techniques have been in place to analyze and predict key metrics, such models typically lack interpretability. Moreover, such techniques also do not allow the validation or discovery of causal links. In this paper, we aim to provide a recipe for applying model interpretability and causal inference for deriving sales insights. In this paper, we review the existing literature on causal inference and interpretability in the context of problems in e-commerce and retail, and apply them to a real-world dataset. We find that an inherently explainable model has a lower variance of SHAP values, and show that including multiple confounders through a double machine learning approach allows us to get the correct sign of causal effect.
【7】Dynamical modeling of nonlinear latent factors in multiscale neural activity with real-time inference
标题：具有实时推理的多尺度神经活动中非线性潜在因素的动态建模
链接：https://arxiv.org/abs/2512.12462
备注：Published at the 39th Annual Conference on Neural Information Processing Systems 2025. Code is available at https://github.com/ShanechiLab/mrine
摘要：Real-time decoding of target variables from multiple simultaneously recorded neural time-series modalities, such as discrete spiking activity and continuous field potentials, is important across various neuroscience applications. However, a major challenge for doing so is that different neural modalities can have different timescales (i.e., sampling rates) and different probabilistic distributions, or can even be missing at some time-steps. Existing nonlinear models of multimodal neural activity do not address different timescales or missing samples across modalities. Further, some of these models do not allow for real-time decoding. Here, we develop a learning framework that can enable real-time recursive decoding while nonlinearly aggregating information across multiple modalities with different timescales and distributions and with missing samples. This framework consists of 1) a multiscale encoder that nonlinearly aggregates information after learning within-modality dynamics to handle different timescales and missing samples in real time, 2) a multiscale dynamical backbone that extracts multimodal temporal dynamics and enables real-time recursive decoding, and 3) modality-specific decoders to account for different probabilistic distributions across modalities. In both simulations and three distinct multiscale brain datasets, we show that our model can aggregate information across modalities with different timescales and distributions and missing samples to improve real-time target decoding. Further, our method outperforms various linear and nonlinear multimodal benchmarks in doing so.
【8】High-Dimensional Tensor Discriminant Analysis: Low-Rank Discriminant Structure, Representation Synergy, and Theoretical Guarantees
标题：多维张量区分分析：低级区分结构、表示协同和理论保证
链接：https://arxiv.org/abs/2512.12122
摘要：High-dimensional tensor-valued predictors arise in modern applications, increasingly as learned representations from neural networks. Existing tensor classification methods rely on sparsity or Tucker structures and often lack theoretical guarantees. Motivated by empirical evidence that discriminative signals concentrate along a few multilinear components, we introduce CP low-rank structure for the discriminant tensor, a modeling perspective not previously explored. Under a Tensor Gaussian Mixture Model, we propose high-dimensional CP low-rank Tensor Discriminant Analysis (CP-TDA) with Randomized Composite PCA (\textsc{rc-PCA}) initialization, that is essential for handling dependent and anisotropic noise under weaker signal strength and incoherence conditions, followed by iterative refinement algorithm. We establish global convergence and minimax-optimal misclassification rates. To handle tensor data deviating from tensor normality, we develop the first semiparametric tensor discriminant model, in which learned tensor representations are mapped via deep generative models into a latent space tailored for CP-TDA. Misclassification risk decomposes into representation, approximation, and estimation errors. Numerical studies and real data analysis on graph classification demonstrate substantial gains over existing tensor classifiers and state-of-the-art graph neural networks, particularly in high-dimensional, small-sample regimes.
【9】V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions
标题：V-REX：通过问题链对探索性视觉推理进行基准测试
链接：https://arxiv.org/abs/2512.11995
备注：28 pages
摘要：While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in most benchmarks, they often struggle in practice with complex open-ended tasks, which usually require multiple rounds of exploration and reasoning in the visual space. Such visual thinking paths not only provide step-by-step exploration and verification as an AI detective but also produce better interpretations of the final answers. However, these paths are challenging to evaluate due to the large exploration space of intermediate steps. To bridge the gap, we develop an evaluation suite, ``Visual Reasoning with multi-step EXploration (V-REX)'', which is composed of a benchmark of challenging visual reasoning tasks requiring native multi-step exploration and an evaluation protocol. V-REX covers rich application scenarios across diverse domains. V-REX casts the multi-step exploratory reasoning into a Chain-of-Questions (CoQ) and disentangles VLMs' capability to (1) Planning: breaking down an open-ended task by selecting a chain of exploratory questions; and (2) Following: answering curated CoQ sequentially to collect information for deriving the final answer. By curating finite options of questions and answers per step, V-REX achieves a reliable quantitative and fine-grained analysis of the intermediate steps. By assessing SOTA proprietary and open-sourced VLMs, we reveal consistent scaling trends, significant differences between planning and following abilities, and substantial room for improvement in multi-step exploratory reasoning.
【10】Data-Driven Global Sensitivity Analysis for Engineering Design Based on Individual Conditional Expectations
标题：基于个人条件预期的数据驱动工程设计全局敏感性分析
链接：https://arxiv.org/abs/2512.11946
摘要：Explainable machine learning techniques have gained increasing attention in engineering applications, especially in aerospace design and analysis, where understanding how input variables influence data-driven models is essential. Partial Dependence Plots (PDPs) are widely used for interpreting black-box models by showing the average effect of an input variable on the prediction. However, their global sensitivity metric can be misleading when strong interactions are present, as averaging tends to obscure interaction effects. To address this limitation, we propose a global sensitivity metric based on Individual Conditional Expectation (ICE) curves. The method computes the expected feature importance across ICE curves, along with their standard deviation, to more effectively capture the influence of interactions. We provide a mathematical proof demonstrating that the PDP-based sensitivity is a lower bound of the proposed ICE-based metric under truncated orthogonal polynomial expansion. In addition, we introduce an ICE-based correlation value to quantify how interactions modify the relationship between inputs and the output. Comparative evaluations were performed on three cases: a 5-variable analytical function, a 5-variable wind-turbine fatigue problem, and a 9-variable airfoil aerodynamics case, where ICE-based sensitivity was benchmarked against PDP, SHapley Additive exPlanations (SHAP), and Sobol' indices. The results show that ICE-based feature importance provides richer insights than the traditional PDP-based approach, while visual interpretations from PDP, ICE, and SHAP complement one another by offering multiple perspectives.
【11】GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Search
标题：GCoDE：通过架构映射协同搜索实现GNN的高效设备边缘协同推理
链接：https://arxiv.org/abs/2512.11856
备注：accepted by IEEE Transactions on Computers
摘要：Graph Neural Networks (GNNs) have emerged as the state-of-the-art graph learning method. However, achieving efficient GNN inference on edge devices poses significant challenges, limiting their application in real-world edge scenarios. This is due to the high computational cost of GNNs and limited hardware resources on edge devices, which prevent GNN inference from meeting real-time and energy requirements. As an emerging paradigm, device-edge co-inference shows potential for improving inference efficiency and reducing energy consumption on edge devices. Despite its potential, research on GNN device-edge co-inference remains scarce, and our findings show that traditional model partitioning methods are ineffective for GNNs. To address this, we propose GCoDE, the first automatic framework for GNN architecture-mapping Co-design and deployment on Device-Edge hierarchies. By abstracting the device communication process into an explicit operation, GCoDE fuses the architecture and mapping scheme in a unified design space for joint optimization. Additionally, GCoDE's system performance awareness enables effective evaluation of architecture efficiency across diverse heterogeneous systems. By analyzing the energy consumption of various GNN operations, GCoDE introduces an energy prediction method that improves energy assessment accuracy and identifies energy-efficient solutions. Using a constraint-based random search strategy, GCoDE identifies the optimal solution in 1.5 hours, balancing accuracy and efficiency. Moreover, the integrated co-inference engine in GCoDE enables efficient deployment and execution of GNN co-inference. Experimental results show that GCoDE can achieve up to 44.9x speedup and 98.2% energy reduction compared to existing approaches across diverse applications and system configurations.
【12】Soft Decision Tree classifier: explainable and extendable PyTorch implementation
标题：软决策树分类器：可解释且可扩展的PyTorch实现
链接：https://arxiv.org/abs/2512.11833
备注：Keywords: Soft Decision Tree, Short-term Memory Soft Decision Tree, Classification, Explainability
摘要：We implemented a Soft Decision Tree (SDT) and a Short-term Memory Soft Decision Tree (SM-SDT) using PyTorch. The methods were extensively tested on simulated and clinical datasets. The SDT was visualized to demonstrate the potential for its explainability. SDT, SM-SDT, and XGBoost demonstrated similar area under the curve (AUC) values. These methods were better than Random Forest, Logistic Regression, and Decision Tree. The results on clinical datasets suggest that, aside from a decision tree, all tested classification methods yield comparable results. The code and datasets are available online on GitHub: https://github.com/KI-Research-Institute/Soft-Decision-Tree
【13】Active Inference with Reusable State-Dependent Value Profiles
标题：可重用状态依赖值剖面的主动推理
链接：https://arxiv.org/abs/2512.11829
备注：27 pages
摘要：Adaptive behavior in volatile environments requires agents to switch among value-control regimes across latent contexts, but maintaining separate preferences, policy biases, and action-confidence parameters for every situation is intractable. We introduce value profiles: a small set of reusable bundles of value-related parameters (outcome preferences, policy priors, and policy precision) assigned to hidden states in a generative model. As posterior beliefs over states evolve trial by trial, effective control parameters arise via belief-weighted mixing, enabling state-conditional strategy recruitment without requiring independent parameters for each context. We evaluate this framework in probabilistic reversal learning, comparing static-precision, entropy-coupled dynamic-precision, and profile-based models using cross-validated log-likelihood and information criteria. Model comparison favors the profile-based model over simpler alternatives (about 100-point AIC differences), and parameter-recovery analyses support structural identifiability even when context must be inferred from noisy observations. Model-based inference further suggests that adaptive control in this task is driven primarily by modulation of policy priors rather than policy precision, with gradual belief-dependent profile recruitment consistent with state-conditional (not purely uncertainty-driven) control. Overall, reusable value profiles provide a tractable computational account of belief-conditioned value control in volatile environments and yield testable signatures of belief-dependent control and behavioral flexibility.
【14】Transport Reversible Jump Markov Chain Monte Carlo with proposals generated by Variational Inference with Normalizing Flows
标题：运输可逆跳跃马尔科夫链蒙特卡罗，采用由规范化流的变分推理生成的提案
链接：https://arxiv.org/abs/2512.12742
摘要：We present a framework using variational inference with normalizing flows (VI-NFs) to generate proposals of reversible jump Markov chain Monte Carlo (RJMCMC) for efficient trans-dimensional Bayesian inference. Unlike transport reversible jump methods relying on forward KL minimization with pilot MCMC samples, our approach minimizes the reverse KL divergence which requires only samples from a base distribution, eliminating costly target sampling. The method employs RealNVP-based flows to learn model-specific transport maps, enabling construction of both between-model and within-model proposals. Our framework provides accurate marginal likelihood estimates from the variational approximation. This facilitates efficient model comparison and proposal adaptation in RJMCMC. Experiments on illustrative example, factor analysis and variable selection tasks in linear regression show that TRJ designed by VI-NFs achieves faster mixing and more efficient model space exploration compared to existing baselines. The proposed algorithm can be extended to conditional flows for amortized vairiational inference across models. Code is available at https://github.com/YinPingping111/TRJ_VINFs.
【15】Understanding Overparametrization in Survival Models through Double-Descent
标题：通过双下降理解生存模型中的过度参数化
链接：https://arxiv.org/abs/2512.12463
摘要：Classical statistical learning theory predicts a U-shaped relationship between test loss and model capacity, driven by the bias-variance trade-off. Recent advances in modern machine learning have revealed a more complex pattern, double-descent, in which test loss, after peaking near the interpolation threshold, decreases again as model capacity continues to grow. While this behavior has been extensively analyzed in regression and classification, its manifestation in survival analysis remains unexplored. This study investigates double-descent in four representative survival models: DeepSurv, PC-Hazard, Nnet-Survival, and N-MTLR. We rigorously define interpolation and finite-norm interpolation, two key characteristics of loss-based models to understand double-descent. We then show the existence (or absence) of (finite-norm) interpolation of all four models. Our findings clarify how likelihood-based losses and model implementation jointly determine the feasibility of interpolation and show that overfitting should not be regarded as benign for survival models. All theoretical results are supported by numerical experiments that highlight the distinct generalization behaviors of survival models.
【16】Interval Fisher's Discriminant Analysis and Visualisation
标题：区间费舍尔区分分析和可视化
链接：https://arxiv.org/abs/2512.11945
摘要：In Data Science, entities are typically represented by single valued measurements. Symbolic Data Analysis extends this framework to more complex structures, such as intervals and histograms, that express internal variability. We propose an extension of multiclass Fisher's Discriminant Analysis to interval-valued data, using Moore's interval arithmetic and the Mallows' distance. Fisher's objective function is generalised to consider simultaneously the contributions of the centres and the ranges of intervals and is numerically maximised. The resulting discriminant directions are then used to classify interval-valued observations.To support visual assessment, we adapt the class map, originally introduced for conventional data, to classifiers that assign labels through minimum distance rules. We also extend the silhouette plot to this setting and use stacked mosaic plots to complement the visual display of class assignments. Together, these graphical tools provide insight into classifier performance and the strength of class membership. Applications to real datasets illustrate the proposed methodology and demonstrate its value in interpreting classification results for interval-valued data.
【17】Understanding Structural Representation in Foundation Models for Polymers
标题：了解聚合物基础模型中的结构表示
链接：https://arxiv.org/abs/2512.11881
摘要：From the relative scarcity of training data to the lack of standardized benchmarks, the development of foundation models for polymers face significant and multi-faceted challenges. At the core, many of these issues are tied directly to the structural representation of polymers and here, we present a new foundation model using a SMILES-based polymer graph representation. This approach allows representation of critical polymer architectural features and connectivity that are not available in other SMILES-based representations. The developed polymer foundation model exhibited excellent performance on 28 different benchmark datasets. Critical evaluation of the developed representation against other variations in control experiments reveals this approach to be a highly performant method of representing polymers in language-based foundation models. These control experiments also reveal a strong invariance of all SMILES representations, with many variations achieving state-of-the-art or near state-of-the-art performance, including those which are chemically or semantically invalid. Examination of error sources and attention maps for the evaluated representations corroborate the findings of the control experiments, showing that chemistry language models based on SMILES interpolate over all sequence space for prediction tasks, not only those of semantically valid inputs. Overall, this work highlights the importance of control experiments as a check on human-imposed assumptions that can limit rational design of both chemistry foundation models and their underlying structural representations.
检测相关(5篇)
【1】StutterFuse: Mitigating Modality Collapse in Stuttering Detection with Jaccard-Weighted Metric Learning and Gated Fusion
标题：口吃：用Jaccard加权度量学习和门控融合减轻口吃检测中的模态崩溃
链接：https://arxiv.org/abs/2512.13632
备注：13 pages, 10 figures
摘要：Stuttering detection breaks down when disfluencies overlap. Existing parametric models struggle to distinguish complex, simultaneous disfluencies (e.g., a 'block' with a 'prolongation') due to the scarcity of these specific combinations in training data. While Retrieval-Augmented Generation (RAG) has revolutionized NLP by grounding models in external knowledge, this paradigm remains unexplored in pathological speech processing. To bridge this gap, we introduce StutterFuse, the first Retrieval-Augmented Classifier (RAC) for multi-label stuttering detection. By conditioning a Conformer encoder on a non-parametric memory bank of clinical examples, we allow the model to classify by reference rather than memorization. We further identify and solve "Modality Collapse", an "Echo Chamber" effect where naive retrieval boosts recall but degrades precision. We mitigate this using: (1) SetCon, a Jaccard-Weighted Metric Learning objective that optimizes for multi-label set similarity, and (2) a Gated Mixture-of-Experts fusion strategy that dynamically arbitrates between acoustic evidence and retrieved context. On the SEP-28k dataset, StutterFuse achieves a weighted F1-score of 0.65, outperforming strong baselines and demonstrating remarkable zero-shot cross-lingual generalization.
【2】Link-Aware Energy-Frugal Continual Learning for Fault Detection in IoT Networks
标题：用于物联网网络故障检测的链路感知节能持续学习
链接
：https://arxiv.org/abs/2512.13340
摘要：The use of lightweight machine learning (ML) models in internet of things (IoT) networks enables resource constrained IoT devices to perform on-device inference for several critical applications. However, the inference accuracy deteriorates due to the non-stationarity in the IoT environment and limited initial training data. To counteract this, the deployed models can be updated occasionally with new observed data samples. However, this approach consumes additional energy, which is undesirable for energy constrained IoT devices. This letter introduces an event-driven communication framework that strategically integrates continual learning (CL) in IoT networks for energy-efficient fault detection. Our framework enables the IoT device and the edge server (ES) to collaboratively update the lightweight ML model by adapting to the wireless link conditions for communication and the available energy budget. Evaluation on real-world datasets show that the proposed approach can outperform both periodic sampling and non-adaptive CL in terms of inference recall; our proposed approach achieves up to a 42.8% improvement, even under tight energy and bandwidth constraint.
【3】Noise-robust Contrastive Learning for Critical Transition Detection in Dynamical Systems
标题：动态系统中关键转移检测的抗噪对比学习
链接：https://arxiv.org/abs/2512.12523
备注：under revision
摘要：Detecting critical transitions in complex, noisy time-series data is a fundamental challenge across science and engineering. Such transitions may be anticipated by the emergence of a low-dimensional order parameter, whose signature is often masked by high-amplitude stochastic variability. Standard contrastive learning approaches based on deep neural networks, while promising for detecting critical transitions, are often overparameterized and sensitive to irrelevant noise, leading to inaccurate identification of critical points. To address these limitations, we propose a neural network architecture, constructed using singular value decomposition technique, together with a strictly semi-orthogonality-constrained training algorithm, to enhance the performance of traditional contrastive learning. Extensive experiments demonstrate that the proposed method matches the performance of traditional contrastive learning techniques in identifying critical transitions, yet is considerably more lightweight and markedly more resistant to noise.
【4】Improving Long-Tailed Object Detection with Balanced Group Softmax and Metric Learning
标题：利用平衡组Softmax和指标学习改进长尾对象检测
链接：https://arxiv.org/abs/2511.16619
备注：8 pages, 7 figures, International Conference on Semantic Computing
摘要：Object detection has been widely explored for class-balanced datasets such as COCO. However, real-world scenarios introduce the challenge of long-tailed distributions, where numerous categories contain only a few instances. This inherent class imbalance biases detection models towards the more frequent classes, degrading performance on rare categories. In this paper, we tackle the problem of long-tailed 2D object detection using the LVISv1 dataset, which consists of 1,203 categories and 164,000 images. We employ a two-stage Faster R-CNN architecture and propose enhancements to the Balanced Group Softmax (BAGS) framework to mitigate class imbalance. Our approach achieves a new state-of-the-art performance with a mean Average Precision (mAP) of 24.5%, surpassing the previous benchmark of 24.0%. Additionally, we hypothesize that tail class features may form smaller, denser clusters within the feature space of head classes, making classification challenging for regression-based classifiers. To address this issue, we explore metric learning to produce feature embeddings that are both well-separated across classes and tightly clustered within each class. For inference, we utilize a k-Nearest Neighbors (k-NN) approach to improve classification performance, particularly for rare classes. Our results demonstrate the effectiveness of these methods in advancing long-tailed object detection.
【5】General OOD Detection via Model-aware and Subspace-aware Variable Priority
标题：通过模型感知和子空间感知可变优先级的通用OOD检测
链接：https://arxiv.org/abs/2512.13003
备注：29 pages, 11 figures
摘要：Out-of-distribution (OOD) detection is essential for determining when a supervised model encounters inputs that differ meaningfully from its training distribution. While widely studied in classification, OOD detection for regression and survival analysis remains limited due to the absence of discrete labels and the challenge of quantifying predictive uncertainty. We introduce a framework for OOD detection that is simultaneously model aware and subspace aware, and that embeds variable prioritization directly into the detection step. The method uses the fitted predictor to construct localized neighborhoods around each test case that emphasize the features driving the model's learned relationship and downweight directions that are less relevant to prediction. It produces OOD scores without relying on global distance metrics or estimating the full feature density. The framework is applicable across outcome types, and in our implementation we use random forests, where the rule structure yields transparent neighborhoods and effective scoring. Experiments on synthetic and real data benchmarks designed to isolate functional shifts show consistent improvements over existing methods. We further demonstrate the approach in an esophageal cancer survival study, where distribution shifts related to lymphadenectomy identify patterns relevant to surgical guidelines.
分类|识别(3篇)
【1】OLC-WA: Drift Aware Tuning-Free Online Classification with Weighted Average
标题：OLC-WA：带有加权平均值的漂移感知免调谐在线分类
链接：https://arxiv.org/abs/2512.12785
摘要：Real-world data sets often exhibit temporal dynamics characterized by evolving data distributions. Disregarding this phenomenon, commonly referred to as concept drift, can significantly diminish a model's predictive accuracy. Furthermore, the presence of hyperparameters in online models exacerbates this issue. These parameters are typically fixed and cannot be dynamically adjusted by the user in response to the evolving data distribution. This paper introduces Online Classification with Weighted Average (OLC-WA), an adaptive, hyperparameter-free online classification model equipped with an automated optimization mechanism. OLC-WA operates by blending incoming data streams with an existing base model. This blending is facilitated by an exponentially weighted moving average. Furthermore, an integrated optimization mechanism dynamically detects concept drift, quantifies its magnitude, and adjusts the model based on the observed data stream characteristics. This approach empowers the model to effectively adapt to evolving data distributions within streaming environments. Rigorous empirical evaluation across diverse benchmark datasets shows that OLC-WA achieves performance comparable to batch models in stationary environments, maintaining accuracy within 1-3%, and surpasses leading online baselines by 10-25% under drift, demonstrating its effectiveness in adapting to dynamic data streams.
【2】AI-Driven Real-Time Kick Classification in Olympic Taekwondo Using Sensor Fusion
标题：采用传感器融合的人工智能驱动奥运跆拳道实时踢球分类
链接：https://arxiv.org/abs/2512.12474
备注：13 pages, 4 figures
摘要：Olympic Taekwondo has faced challenges in spectator engagement due to static, defensive gameplay and contentious scoring. Current Protector and Scoring Systems (PSS) rely on impact sensors and simplistic logic, encouraging safe strategies that diminish the sport's dynamism. This paper proposes an AI-powered scoring system that integrates existing PSS sensors with additional accelerometers, gyroscopes, magnetic/RFID, and impact force sensors in a sensor fusion framework. The system classifies kicks in real-time to identify technique type, contact location, impact force, and even the part of the foot used. A machine learning pipeline employing sensor fusion and Support Vector Machines (SVMs) is detailed, enabling automatic kick technique recognition for scoring. We present a novel kick scoring rubric that awards points based on specific kick techniques (e.g., turning and spinning kicks) to incentivize dynamic attacks. Drawing on a 2024 study achieving 96-98% accuracy, we validate the feasibility of real-time kick classification and further propose enhancements to this methodology, such as ensemble SVM classifiers and expanded datasets, to achieve the high-stakes accuracy required by the sport. We analyze how the proposed system can improve scoring fairness, reduce rule exploitation and illegitimate tactics, encourage more dynamic techniques, and enhance spectator understanding and excitement. The paper includes system design illustrations, a kick scoring table from an AI-augmented rule set, and discusses anticipated impacts on Olympic Taekwondo.
【3】Synthetic Swarm Mosquito Dataset for Acoustic Classification: A Proof of Concept
标题：用于声学分类的合成蜂群蚊子数据集：概念证明
链接：https://arxiv.org/abs/2512.12365
备注：Accepted at RIVF 2025
摘要：Mosquito-borne diseases pose a serious global health threat, causing over 700,000 deaths annually. This work introduces a proof-of-concept Synthetic Swarm Mosquito Dataset for Acoustic Classification, created to simulate realistic multi-species and noisy swarm conditions. Unlike conventional datasets that require labor-intensive recording of individual mosquitoes, the synthetic approach enables scalable data generation while reducing human resource demands. Using log-mel spectrograms, we evaluated lightweight deep learning architectures for the classification of mosquito species. Experiments show that these models can effectively identify six major mosquito vectors and are suitable for deployment on embedded low-power devices. The study demonstrates the potential of synthetic swarm audio datasets to accelerate acoustic mosquito research and enable scalable real-time surveillance solutions.
表征(5篇)
【1】PvP: Data-Efficient Humanoid Robot Learning with Proprioceptive-Privileged Contrastive Representations
标题：PvP：具有专有感知对比表示的数据高效类人机器人学习
链接：https://arxiv.org/abs/2512.13093
备注：13 pages, 12 figures
摘要：Achieving efficient and robust whole-body control (WBC) is essential for enabling humanoid robots to perform complex tasks in dynamic environments. Despite the success of reinforcement learning (RL) in this domain, its sample inefficiency remains a significant challenge due to the intricate dynamics and partial observability of humanoid robots. To address this limitation, we propose PvP, a Proprioceptive-Privileged contrastive learning framework that leverages the intrinsic complementarity between proprioceptive and privileged states. PvP learns compact and task-relevant latent representations without requiring hand-crafted data augmentations, enabling faster and more stable policy learning. To support systematic evaluation, we develop SRL4Humanoid, the first unified and modular framework that provides high-quality implementations of representative state representation learning (SRL) methods for humanoid robot learning. Extensive experiments on the LimX Oli robot across velocity tracking and motion imitation tasks demonstrate that PvP significantly improves sample efficiency and final performance compared to baseline SRL methods. Our study further provides practical insights into integrating SRL with RL for humanoid WBC, offering valuable guidance for data-efficient humanoid robot learning.
【2】Sparse Concept Anchoring for Interpretable and Controllable Neural Representations
标题：用于可解释和可控制神经表示的稀疏概念分类
链接：https://arxiv.org/abs/2512.12469
摘要 ：We introduce Sparse Concept Anchoring, a method that biases latent space to position a targeted subset of concepts while allowing others to self-organize, using only minimal supervision (labels for <0.1% of examples per anchored concept). Training combines activation normalization, a separation regularizer, and anchor or subspace regularizers that attract rare labeled examples to predefined directions or axis-aligned subspaces. The anchored geometry enables two practical interventions: reversible behavioral steering that projects out a concept's latent component at inference, and permanent removal via targeted weight ablation of anchored dimensions. Experiments on structured autoencoders show selective attenuation of targeted concepts with negligible impact on orthogonal features, and complete elimination with reconstruction error approaching theoretical bounds. Sparse Concept Anchoring therefore provides a practical pathway to interpretable, steerable behavior in learned representations.
【3】DeepVekua: Geometric-Spectral Representation Learning for Physics-Informed Fields
标题：DeepVekua：物理知识领域的几何光谱表示学习
链接：https://arxiv.org/abs/2512.12402
摘要：We present DeepVekua, a hybrid architecture that unifies geometric deep learning with spectral analysis to solve partial differential equations (PDEs) in sparse data regimes. By learning a diffeomorphic coordinate transformation that maps complex geometries to a latent harmonic space, our method outperforms state-of-the-art implicit representations on advection-diffusion systems. Unlike standard coordinate-based networks which struggle with spectral bias, DeepVekua separates the learning of geometry from the learning of physics, solving for optimal spectral weights in closed form. We demonstrate a 100x improvement over spectral baselines. The code is available at https://github.com/VladimerKhasia/vekuanet.
【4】BaRISTA: Brain Scale Informed Spatiotemporal Representation of Human Intracranial Neural Activity
标题：BaRISTA：大脑量表了解人类脑内神经活动的时空代表
链接：https://arxiv.org/abs/2512.12135
备注：Published at the 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025). Code available at https://github.com/ShanechiLab/BaRISTA
摘要：Intracranial recordings have opened a unique opportunity to simultaneously measure activity across multiregional networks in the human brain. Recent works have focused on developing transformer-based neurofoundation models of such recordings that can generalize across subjects and datasets. However, these recordings exhibit highly complex spatiotemporal interactions across diverse spatial scales, from the single-channel scale to the scale of brain regions. As such, there remain critical open questions regarding how best to encode spatial information and how to design self-supervision tasks that enable the learning of brain network patterns and enhance downstream decoding performance using such high-dimensional, multiregional recordings. To allow for exploring these questions, we propose a new spatiotemporal transformer model of multiregional neural activity and a corresponding self-supervised masked latent reconstruction task, designed to enable flexibility in the spatial scale used for token encoding and masking. Applying this model on publicly available multiregional intracranial electrophysiology (iEEG) data, we demonstrate that adjusting the spatial scale for both token encoding and masked reconstruction significantly impacts downstream decoding. Further, we find that spatial encoding at larger scales than channel-level encoding, which is commonly used in existing iEEG transformer models, improves downstream decoding performance. Finally, we demonstrate that our method allows for region-level token encoding while also maintaining accurate channel-level neural reconstruction. Taken together, our modeling framework enables exploration of the spatial scales used for token encoding and masking, reveals their importance towards self-supervised pretraining of neurofoundation models of multiregional human brain activity, and enhances downstream decoding performance.
【5】Performance and Efficiency of Climate In-Situ Data Reconstruction: Why Optimized IDW Outperforms kriging and Implicit Neural Representation
标题：气候现场数据重建的性能和效率：为什么优化的IDW优于克里金法和隐式神经表示
链接：https://arxiv.org/abs/2512.11832
摘要：This study evaluates three reconstruction methods for sparse climate data: the simple inverse distance weighting (IDW), the statistically grounded ordinary kriging (OK), and the advanced implicit neural representation model (MMGN architecture). All methods were optimized through hyper-parameter tuning using validation splits. An extensive set of experiments was conducted, followed by a comprehensive statistical analysis. The results demonstrate the superiority of the simple IDW method over the other reference methods in terms of both reconstruction accuracy and computational efficiency. IDW achieved the lowest RMSE ($3.00 \pm 1.93$), MAE ($1.32 \pm 0.77$), and $Δ_{MAX}$ ($24.06 \pm 17.15$), as well as the highest $R^2$ ($0.68 \pm 0.16$), across 100 randomly sampled sparse datasets from the ECA\&D database. Differences in RMSE, MAE, and $R^2$ were statistically significant and exhibited moderate to large effect sizes. The Dunn post-hoc test further confirmed the consistent superiority of IDW across all evaluated quality measures [...]
编码器(2篇)
【1】Scalable Formal Verification via Autoencoder Latent Space Abstraction
标题：通过自动编码器潜在空间抽象的可扩展形式验证
链接：https://arxiv.org/abs/2512.13593
备注：14 pages, 7 figures, under review
摘要 ：Finite Abstraction methods provide a powerful formal framework for proving that systems satisfy their specifications. However, these techniques face scalability challenges for high-dimensional systems, as they rely on state-space discretization which grows exponentially with dimension. Learning-based approaches to dimensionality reduction, utilizing neural networks and autoencoders, have shown great potential to alleviate this problem. However, ensuring the correctness of the resulting verification results remains an open question. In this work, we provide a formal approach to reduce the dimensionality of systems via convex autoencoders and learn the dynamics in the latent space through a kernel-based method. We then construct a finite abstraction from the learned model in the latent space and guarantee that the abstraction contains the true behaviors of the original system. We show that the verification results in the latent space can be mapped back to the original system. Finally, we demonstrate the effectiveness of our approach on multiple systems, including a 26D system controlled by a neural network, showing significant scalability improvements without loss of rigor.
【2】Knowledge-Guided Masked Autoencoder with Linear Spectral Mixing and Spectral-Angle-Aware Reconstruction
标题：具有线性光谱混合和光谱角度感知重建的知识引导掩蔽自动编码器
链接：https://arxiv.org/abs/2512.12445
摘要：Integrating domain knowledge into deep learning has emerged as a promising direction for improving model interpretability, generalization, and data efficiency. In this work, we present a novel knowledge-guided ViT-based Masked Autoencoder that embeds scientific domain knowledge within the self-supervised reconstruction process. Instead of relying solely on data-driven optimization, our proposed approach incorporates the Linear Spectral Mixing Model (LSMM) as a physical constraint and physically-based Spectral Angle Mapper (SAM), ensuring that learned representations adhere to known structural relationships between observed signals and their latent components. The framework jointly optimizes LSMM and SAM loss with a conventional Huber loss objective, promoting both numerical accuracy and geometric consistency in the feature space. This knowledge-guided design enhances reconstruction fidelity, stabilizes training under limited supervision, and yields interpretable latent representations grounded in physical principles. The experimental findings indicate that the proposed model substantially enhances reconstruction quality and improves downstream task performance, highlighting the promise of embedding physics-informed inductive biases within transformer-based self-supervised learning.
优化|敛散性(13篇)
【1】Textual Gradients are a Flawed Metaphor for Automatic Prompt Optimization
标题：文本关联是自动提示优化的有缺陷的隐喻
链接：https://arxiv.org/abs/2512.13598
摘要：A well-engineered prompt can increase the performance of large language models; automatic prompt optimization techniques aim to increase performance without requiring human effort to tune the prompts. One leading class of prompt optimization techniques introduces the analogy of textual gradients. We investigate the behavior of these textual gradient methods through a series of experiments and case studies. While such methods often result in a performance improvement, our experiments suggest that the gradient analogy does not accurately explain their behavior. Our insights may inform the selection of prompt optimization strategies, and development of new approaches.
【2】Reflective Preference Optimization (RPO): Enhancing On-Policy Alignment via Hint-Guided Reflection
标题：反思性偏好优化（LPO）：通过提示引导反思增强政策一致性
链接：https://arxiv.org/abs/2512.13240
摘要：Direct Preference Optimization (DPO) has emerged as a lightweight and effective alternative to Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with AI Feedback (RLAIF) for aligning large language and vision-language models. However, the standard DPO formulation, in which both the chosen and rejected responses are generated by the same policy, suffers from a weak learning signal because the two responses often share similar errors and exhibit small Kullback-Leibler (KL) divergence. This leads to slow and unstable convergence. To address this limitation, we introduce Reflective Preference Optimization (RPO), a new framework that incorporates hint-guided reflection into the DPO paradigm. RPO uses external models to identify hallucination sources and generate concise reflective hints, enabling the construction of on-policy preference pairs with stronger contrastiveness and clearer preference signals. We theoretically show that conditioning on hints increases the expected preference margin through mutual information and improves sample efficiency while remaining within the policy distribution family. Empirically, RPO achieves superior alignment with fewer training samples and iterations, substantially reducing hallucination rates and delivering state-of-the-art performance across multimodal benchmarks.
【3】Deep Q-Learning-Based Intelligent Scheduling for ETL Optimization in Heterogeneous Data Environments
标题：基于深度Q学习的智能调度用于异类数据环境中的RTL优化
链接：https://arxiv.org/abs/2512.13060
摘要：This paper addresses the challenges of low scheduling efficiency, unbalanced resource allocation, and poor adaptability in ETL (Extract-Transform-Load) processes under heterogeneous data environments by proposing an intelligent scheduling optimization framework based on deep Q-learning. The framework formalizes the ETL scheduling process as a Markov Decision Process and enables adaptive decision-making by a reinforcement learning agent in high-dimensional state spaces to dynamically optimize task allocation and resource scheduling. The model consists of a state representation module, a feature embedding network, a Q-value estimator, and a reward evaluation mechanism, which collectively consider task dependencies, node load states, and data flow characteristics to derive the optimal scheduling strategy in complex environments. A multi-objective reward function is designed to balance key performance indicators such as average scheduling delay, task completion rate, throughput, and resource utilization. Sensitivity experiments further verify the model's robustness under changes in hyperparameters, environmental dynamics, and data scale. Experimental results show that the proposed deep Q-learning scheduling framework significantly reduces scheduling delay, improves system throughput, and enhances execution stability under multi-source heterogeneous task conditions, demonstrating the strong potential of reinforcement learning in complex data scheduling and resource management, and providing an efficient and scalable optimization strategy for intelligent data pipeline construction.
【4】Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift
标题：概念漂移下ML模型训练和部署的最优资源分配
链接：https://arxiv.org/abs/2512.12816
摘要：We study how to allocate resources for training and deployment of machine learning (ML) models under concept drift and limited budgets. We consider a setting in which a model provider distributes trained models to multiple clients whose devices support local inference but lack the ability to retrain those models, placing the burden of performance maintenance on the provider. We introduce a model-agnostic framework that captures the interaction between resource allocation, concept drift dynamics, and deployment timing. We show that optimal training policies depend critically on the aging properties of concept durations. Under sudden concept changes, we derive optimal training policies subject to budget constraints when concept durations follow distributions with Decreasing Mean Residual Life (DMRL), and show that intuitive heuristics are provably suboptimal under Increasing Mean Residual Life (IMRL). We further study model deployment under communication constraints, prove that the associated optimization problem is quasi-convex under mild conditions, and propose a randomized scheduling strategy that achieves near-optimal client-side performance. These results offer theoretical and algorithmic foundations for cost-efficient ML model management under concept drift, with implications for continual learning, distributed inference, and adaptive ML systems.
【5】Multi-Trajectory Physics-Informed Neural Networks for HJB Equations with Hard-Zero Terminal Inventory: Optimal Execution on Synthetic & SPY Data
标题：HJB方程的多轨迹物理信息神经网络与硬零终端库存：合成和SPY数据的最优执行
链接：https://arxiv.org/abs/2512.12708
备注：24 pages, 19 figures. Accepted to the NeurIPS 2025 Workshop on Generative AI in Finance
摘要：We study optimal trade execution with a hard-zero terminal inventory constraint, modeled via Hamilton-Jacobi-Bellman (HJB) equations. Vanilla PINNs often under-enforce this constraint and produce unstable controls. We propose a Multi-Trajectory PINN (MT-PINN) that adds a rollout-based trajectory loss and propagates a terminal penalty on terminal inventory via backpropagation-through-time, directly enforcing zero terminal inventory. A lightweight lambda-curriculum is adopted to stabilize training as the state expands from a risk-neutral reduced HJB to a risk-averse HJB. On the Gatheral-Schied single-asset model, MT-PINN aligns closely with their derived closed-form solutions and concentrates terminal inventory tightly around zero while reducing errors along optimal paths. We apply MT-PINNs on SPY intraday data, matching TWAP when risk-neutral, and achieving lower exposure and competitive costs, especially in falling windows, for higher risk-aversion.
【6】Optimal Mistake Bounds for Transductive Online Learning
标题：转化式在线学习的最佳错误界限
链接：https://arxiv.org/abs/2512.12567
摘要：We resolve a 30-year-old open problem concerning the power of unlabeled data in online learning by tightly quantifying the gap between transductive and standard online learning. In the standard setting, the optimal mistake bound is characterized by the Littlestone dimension $d$ of the concept class $H$ (Littlestone 1987). We prove that in the transductive setting, the mistake bound is at least $Ω(\sqrt{d})$. This constitutes an exponential improvement over previous lower bounds of $Ω(\log\log d)$, $Ω(\sqrt{\log d})$, and $Ω(\log d)$, due respectively to Ben-David, Kushilevitz, and Mansour (1995, 1997) and Hanneke, Moran, and Shafer (2023). We also show that this lower bound is tight: for every $d$, there exists a class of Littlestone dimension $d$ with transductive mistake bound $O(\sqrt{d})$. Our upper bound also improves upon the best known upper bound of $(2/3)d$ from Ben-David, Kushilevitz, and Mansour (1997). These results establish a quadratic gap between transductive and standard online learning, thereby highlighting the benefit of advance access to the unlabeled instance sequence. This contrasts with the PAC setting, where transductive and standard learning exhibit similar sample complexities.
【7】Policy Optimization for Dynamic Heart Transplant Allocation
标题：动态心脏移植分配的政策优化
链接：https://arxiv.org/abs/2512.12497
备注：An extended abstract of this paper was presented at the scientific sessions of AHA 2025
摘要：Heart transplantation is a viable path for patients suffering from advanced heart failure, but this lifesaving option is severely limited due to donor shortage. Although the current allocation policy was recently revised in 2018, a major concern is that it does not adequately take into account pretransplant and post-transplant mortality. In this paper, we take an important step toward addressing these deficiencies. To begin with, we use historical data from UNOS to develop a new simulator that enables us to evaluate and compare the performance of different policies. We then leverage our simulator to demonstrate that the status quo policy is considerably inferior to the myopic policy that matches incoming donors to the patient who maximizes the number of years gained by the transplant. Moreover, we develop improved policies that account for the dynamic nature of the allocation process through the use of potentials -- a measure of a patient's utility in future allocations that we introduce. We also show that batching together even a handful of donors -- which is a viable option for a certain type of donors -- further enhances performance. Our simulator also allows us to evaluate the effect of critical, and often unexplored, factors in the allocation, such as geographic proximity and the tendency to reject offers by the transplant centers.
【8】Optimized Architectures for Kolmogorov-Arnold Networks
标题：Kolmogorov-Arnold网络的优化架构
链接：https://arxiv.org/abs/2512.12448
备注：12 pages, 1 figure, 3 tables
摘要：Efforts to improve Kolmogorov-Arnold networks (KANs) with architectural enhancements have been stymied by the complexity those enhancements bring, undermining the interpretability that makes KANs attractive in the first place. Here we study overprovisioned architectures combined with sparsification to learn compact, interpretable KANs without sacrificing accuracy. Crucially, we focus on differentiable sparsification, turning architecture search into an end-to-end optimization problem. Across function approximation benchmarks, dynamical systems forecasting, and real-world prediction tasks, we demonstrate competitive or superior accuracy while discovering substantially smaller models. Overprovisioning and sparsification are synergistic, with the combination outperforming either alone. The result is a principled path toward models that are both more expressive and more interpretable, addressing a key tension in scientific machine learning.
【9】Optimized Learned Count-Min Sketch
标题：优化的学习计数最小草图
链接：https://arxiv.org/abs/2512.12252
备注：4 pages, 3 figures. Accepted at NeurIPS 2025 Workshop on Machine Learning for Systems
摘要：Count-Min Sketch (CMS) is a memory-efficient data structure for estimating the frequency of elements in a multiset. Learned Count-Min Sketch (LCMS) enhances CMS with a machine learning model to reduce estimation error under the same memory usage, but suffers from slow construction due to empirical parameter tuning and lacks theoretical guarantees on intolerable error probability. We propose Optimized Learned Count-Min Sketch (OptLCMS), which partitions the input domain and assigns each partition to its own CMS instance, with CMS parameters analytically derived for fixed thresholds, and thresholds optimized via dynamic programming with approximate feasibility checks. This reduces the need for empirical validation, enabling faster construction while providing theoretical guarantees under these assumptions. OptLCMS also allows explicit control of the allowable error threshold, improving flexibility in practice. Experiments show that OptLCMS builds faster, achieves lower intolerable error probability, and matches the estimation accuracy of LCMS.
【10】EnviroLLM: Resource Tracking and Optimization for Local AI
标题：EnviroLLM：本地人工智能的资源跟踪和优化
链接：https://arxiv.org/abs/2512.12004
备注：8 pages, 3 tables
摘要：Large language models (LLMs) are increasingly deployed locally for privacy and accessibility, yet users lack tools to measure their resource usage, environmental impact, and efficiency metrics. This paper presents EnviroLLM, an open-source toolkit for tracking, benchmarking, and optimizing performance and energy consumption when running LLMs on personal devices. The system provides real-time process monitoring, benchmarking across multiple platforms (Ollama, LM Studio, vLLM, and OpenAI-compatible APIs), persistent storage with visualizations for longitudinal analysis, and personalized model and optimization recommendations. The system includes LLM-as-judge evaluations alongside energy and speed metrics, enabling users to assess quality-efficiency tradeoffs when testing models with custom prompts.
【11】Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL
标题：基于拍卖和扩散MARL的低空智能网络分层任务卸载和轨迹优化
链接：https://arxiv.org/abs/2512.11862
摘要：The low-altitude intelligent networks (LAINs) emerge as a promising architecture for delivering low-latency and energy-efficient edge intelligence in dynamic and infrastructure-limited environments. By integrating unmanned aerial vehicles (UAVs), aerial base stations, and terrestrial base stations, LAINs can support mission-critical applications such as disaster response, environmental monitoring, and real-time sensing. However, these systems face key challenges, including energy-constrained UAVs, stochastic task arrivals, and heterogeneous computing resources. To address these issues, we propose an integrated air-ground collaborative network and formulate a time-dependent integer nonlinear programming problem that jointly optimizes UAV trajectory planning and task offloading decisions. The problem is challenging to solve due to temporal coupling among decision variables. Therefore, we design a hierarchical learning framework with two timescales. At the large timescale, a Vickrey-Clarke-Groves auction mechanism enables the energy-aware and incentive-compatible trajectory assignment. At the small timescale, we propose the diffusion-heterogeneous-agent proximal policy optimization, a generative multi-agent reinforcement learning algorithm that embeds latent diffusion models into actor networks. Each UAV samples actions from a Gaussian prior and refines them via observation-conditioned denoising, enhancing adaptability and policy diversity. Extensive simulations show that our framework outperforms baselines in energy efficiency, task success rate, and convergence performance.
【12】Generative Stochastic Optimal Transport: Guided Harmonic Path-Integral Diffusion
标题：生成随机最优传输：引导调和路径积分扩散
链接：https://arxiv.org/abs/2512.11859
备注：40 pages, 8 figures
摘要 ：We introduce Guided Harmonic Path-Integral Diffusion (GH-PID), a linearly-solvable framework for guided Stochastic Optimal Transport (SOT) with a hard terminal distribution and soft, application-driven path costs. A low-dimensional guidance protocol shapes the trajectory ensemble while preserving analytic structure: the forward and backward Kolmogorov equations remain linear, the optimal score admits an explicit Green-function ratio, and Gaussian-Mixture Model (GMM) terminal laws yield closed-form expressions. This enables stable sampling and differentiable protocol learning under exact terminal matching. We develop guidance-centric diagnostics -- path cost, centerline adherence, variance flow, and drift effort -- that make GH-PID an interpretable variational ansatz for empirical SOT. Three navigation scenarios illustrated in 2D: (i) Case A: hand-crafted protocols revealing how geometry and stiffness shape lag, curvature effects, and mode evolution; (ii) Case B: single-task protocol learning, where a PWC centerline is optimized to minimize integrated cost; (iii) Case C: multi-expert fusion, in which a commander reconciles competing expert/teacher trajectories and terminal beliefs through an exact product-of-experts law and learns a consensus protocol. Across all settings, GH-PID generates geometry-aware, trust-aware trajectories that satisfy the prescribed terminal distribution while systematically reducing integrated cost.
【13】Iterative Sampling Methods for Sinkhorn Distributionally Robust Optimization
标题：Sinkhorn分布鲁棒优化的迭代抽样方法
链接：https://arxiv.org/abs/2512.12550
备注：29 pages
摘要：Distributionally robust optimization (DRO) has emerged as a powerful paradigm for reliable decision-making under uncertainty. This paper focuses on DRO with ambiguity sets defined via the Sinkhorn discrepancy: an entropy-regularized Wasserstein distance, referred to as Sinkhorn DRO. Existing work primarily addresses Sinkhorn DRO from a dual perspective, leveraging its formulation as a conditional stochastic optimization problem, for which many stochastic gradient methods are applicable. However, the theoretical analyses of such methods often rely on the boundedness of the loss function, and it is indirect to obtain the worst-case distribution associated with Sinkhorn DRO. In contrast, we study Sinkhorn DRO from the primal perspective, by reformulating it as a bilevel program with several infinite-dimensional lower-level subproblems over probability space. This formulation enables us to simultaneously obtain the optimal robust decision and the worst-case distribution, which is valuable in practical settings, such as generating stress-test scenarios or designing robust learning algorithms. We propose both double-loop and single-loop sampling-based algorithms with theoretical guarantees to solve this bilevel program. Finally, we demonstrate the effectiveness of our approach through a numerical study on adversarial classification.
预测|估计(17篇)
【1】No One Left Behind: How to Exploit the Incomplete and Skewed Multi-Label Data for Conversion Rate Prediction
标题：不让一个人掉队：如何利用不完整和倾斜的多标签数据进行转化率预测
链接：https://arxiv.org/abs/2512.13300
摘要：In most real-world online advertising systems, advertisers typically have diverse customer acquisition goals. A common solution is to use multi-task learning (MTL) to train a unified model on post-click data to estimate the conversion rate (CVR) for these diverse targets. In practice, CVR prediction often encounters missing conversion data as many advertisers submit only a subset of user conversion actions due to privacy or other constraints, making the labels of multi-task data incomplete. If the model is trained on all available samples where advertisers submit user conversion actions, it may struggle when deployed to serve a subset of advertisers targeting specific conversion actions, as the training and deployment data distributions are mismatched. While considerable MTL efforts have been made, a long-standing challenge is how to effectively train a unified model with the incomplete and skewed multi-label data. In this paper, we propose a fine-grained Knowledge transfer framework for Asymmetric Multi-Label data (KAML). We introduce an attribution-driven masking strategy (ADM) to better utilize data with asymmetric multi-label data in training. However, the more relaxed masking in ADM is a double-edged sword: it provides additional training signals but also introduces noise due to skewed data. To address this, we propose a hierarchical knowledge extraction mechanism (HKE) to model the sample discrepancy within the target task tower. Finally, to maximize the utility of unlabeled samples, we incorporate ranking loss strategy to further enhance our model. The effectiveness of KAML has been demonstrated through comprehensive evaluations on offline industry datasets and online A/B tests, which show significant performance improvements over existing MTL baselines.
【2】WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory
标题：方式：全球AIS轨迹中船舶目的地的估计
链接：https://arxiv.org/abs/2512.13190
备注：Accepted to IEEE Transactions on Aerospace and Electronic Systems (TAES)
摘要：The Automatic Identification System (AIS) enables data-driven maritime surveillance but suffers from reliability issues and irregular intervals. We address vessel destination estimation using global-scope AIS data by proposing a differentiated approach that recasts long port-to-port trajectories as a nested sequence structure. Using spatial grids, this method mitigates spatio-temporal bias while preserving detailed resolution. We introduce a novel deep learning architecture, WAY, designed to process these reformulated trajectories for long-term destination estimation days to weeks in advance. WAY comprises a trajectory representation layer and Channel-Aggregative Sequential Processing (CASP) blocks. The representation layer generates multi-channel vector sequences from kinematic and non-kinematic features. CASP blocks utilize multi-headed channel- and self-attention for aggregation and sequential information delivery. Additionally, we propose a task-specialized Gradient Dropout (GD) technique to enable many-to-many training on single labels, preventing biased feedback surges by stochastically blocking gradient flow based on sample length. Experiments on 5-year AIS data demonstrate WAY's superiority over conventional spatial grid-based approaches regardless of trajectory progression. Results further confirm that adopting GD leads to performance gains. Finally, we explore WAY's potential for real-world application through multitask learning for ETA estimation.
【3】Iterative Tuning of Nonlinear Model Predictive Control for Robotic Manufacturing Tasks
标题：机器人制造任务非线性模型预测控制的迭代调整
链接：https://arxiv.org/abs/2512.13170
摘要 ：Manufacturing processes are often perturbed by drifts in the environment and wear in the system, requiring control re-tuning even in the presence of repetitive operations. This paper presents an iterative learning framework for automatic tuning of Nonlinear Model Predictive Control (NMPC) weighting matrices based on task-level performance feedback. Inspired by norm-optimal Iterative Learning Control (ILC), the proposed method adaptively adjusts NMPC weights Q and R across task repetitions to minimize key performance indicators (KPIs) related to tracking accuracy, control effort, and saturation. Unlike gradient-based approaches that require differentiating through the NMPC solver, we construct an empirical sensitivity matrix, enabling structured weight updates without analytic derivatives. The framework is validated through simulation on a UR10e robot performing carbon fiber winding on a tetrahedral core. Results demonstrate that the proposed approach converges to near-optimal tracking performance (RMSE within 0.3% of offline Bayesian Optimization (BO)) in just 4 online repetitions, compared to 100 offline evaluations required by BO algorithm. The method offers a practical solution for adaptive NMPC tuning in repetitive robotic tasks, combining the precision of carefully optimized controllers with the flexibility of online adaptation.
【4】Deep Learning-Driven Inversion Framework for Shear Modulus Estimation in Magnetic Resonance Elastography (DIME)
标题：深度学习驱动的磁共振弹性成像（DIME）中剪切模估计的倒置框架
链接：https://arxiv.org/abs/2512.13010
摘要：The Multimodal Direct Inversion (MMDI) algorithm is widely used in Magnetic Resonance Elastography (MRE) to estimate tissue shear stiffness. However, MMDI relies on the Helmholtz equation, which assumes wave propagation in a uniform, homogeneous, and infinite medium. Furthermore, the use of the Laplacian operator makes MMDI highly sensitive to noise, which compromises the accuracy and reliability of stiffness estimates. In this study, we propose the Deep-Learning driven Inversion Framework for Shear Modulus Estimation in MRE (DIME), aimed at enhancing the robustness of inversion. DIME is trained on the displacement fields-stiffness maps pair generated through Finite Element Modelling (FEM) simulations. To capture local wave behavior and improve robustness to global image variations, DIME is trained on small image patches. We first validated DIME using homogeneous and heterogeneous datasets simulated with FEM, where DIME produced stiffness maps with low inter-pixel variability, accurate boundary delineation, and higher correlation with ground truth (GT) compared to MMDI. Next, DIME was evaluated in a realistic anatomy-informed simulated liver dataset with known GT and compared directly to MMDI. DIME reproduced ground-truth stiffness patterns with high fidelity (r = 0.99, R^2 = 0.98), while MMDI showed greater underestimation. After validating DIME on synthetic data, we tested the model in in vivo liver MRE data from eight healthy and seven fibrotic subjects. DIME preserved physiologically consistent stiffness patterns and closely matched MMDI, which showed directional bias. Overall, DIME showed higher correlation with ground truth and visually similar stiffness patterns, whereas MMDI displayed a larger bias that can potentially be attributed to directional filtering. These preliminary results highlight the feasibility of DIME for clinical applications in MRE.
【5】Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset
标题：具有非金融特征的信用风险估计：来自伊斯坦布尔合成数据集的证据
链接：https://arxiv.org/abs/2512.12783
摘要：Financial exclusion constrains entrepreneurship, increases income volatility, and widens wealth gaps. Underbanked consumers in Istanbul often have no bureau file because their earnings and payments flow through informal channels. To study how such borrowers can be evaluated we create a synthetic dataset of one hundred thousand Istanbul residents that reproduces first quarter 2025 TÜİK census marginals and telecom usage patterns. Retrieval augmented generation feeds these public statistics into the OpenAI o3 model, which synthesises realistic yet private records. Each profile contains seven socio demographic variables and nine alternative attributes that describe phone specifications, online shopping rhythm, subscription spend, car ownership, monthly rent, and a credit card flag. To test the impact of the alternative financial data CatBoost, LightGBM, and XGBoost are each trained in two versions. Demo models use only the socio demographic variables; Full models include both socio demographic and alternative attributes. Across five fold stratified validation the alternative block raises area under the curve by about one point three percentage and lifts balanced \(F_{1}\) from roughly 0.84 to 0.95, a fourteen percent gain. We contribute an open Istanbul 2025 Q1 synthetic dataset, a fully reproducible modeling pipeline, and empirical evidence that a concise set of behavioural attributes can approach bureau level discrimination power while serving borrowers who lack formal credit records. These findings give lenders and regulators a transparent blueprint for extending fair and safe credit access to the underbanked.
【6】An End-to-End Approach for Microgrid Probabilistic Forecasting and Robust Operation via Decision-focused Learning
标题：通过以决策为中心的学习进行微电网概率预测和稳健运营的端到端方法
链接：https://arxiv.org/abs/2512.12755
备注：10 pages
摘要：High penetration of renewable energy sources (RES) introduces significant uncertainty and intermittency into microgrid operations, posing challenges to economic and reliable scheduling. To address this, this paper proposes an end-to-end decision-focused framework that jointly optimizes probabilistic forecasting and robust operation for microgrids. A multilayer encoder-decoder (MED) probabilistic forecasting model is integrated with a two-stage robust optimization (TSRO) model involving direct load control (DLC) through a differentiable decision pathway, enabling gradient-based feedback from operational outcomes to improve forecasting performance. Unlike conventional sequential approaches, the proposed method aligns forecasting accuracy with operational objectives by directly minimizing decision regret via a surrogate smart predict-then-optimize (SPO) loss function. This integration ensures that probabilistic forecasts are optimized for downstream decisions, enhancing both economic efficiency and robustness. Case studies on modified IEEE 33-bus and 69-bus systems demonstrate that the proposed framework achieves superior forecasting accuracy and operational performance, reducing total and net operation costs by up to 18% compared with conventional forecasting and optimization combinations. The results verify the effectiveness and scalability of the end-to-end decision-focused approach for resilient and cost-efficient microgrid management under uncertainty.
【7】CoLSE: A Lightweight and Robust Hybrid Learned Model for Single-Table Cardinality Estimation using Joint CDF
标题：CoLSE：一种轻量级且稳健的混合学习模型，用于使用联合EDF进行单表基数估计
链接：https://arxiv.org/abs/2512.12624
摘要：Cardinality estimation (CE), the task of predicting the result size of queries is a critical component of query optimization. Accurate estimates are essential for generating efficient query execution plans. Recently, machine learning techniques have been applied to CE, broadly categorized into query-driven and data-driven approaches. Data-driven methods learn the joint distribution of data, while query-driven methods construct regression models that map query features to cardinalities. Ideally, a CE technique should strike a balance among three key factors: accuracy, efficiency, and memory footprint. However, existing state-of-the-art models often fail to achieve this balance. To address this, we propose CoLSE, a hybrid learned approach for single-table cardinality estimation. CoLSE directly models the joint probability over queried intervals using a novel algorithm based on copula theory and integrates a lightweight neural network to correct residual estimation errors. Experimental results show that CoLSE achieves a favorable trade-off among accuracy, training time, inference latency, and model size, outperforming existing state-of-the-art methods.
【8】Skillful Subseasonal-to-Seasonal Forecasting of Extreme Events with a Multi-Sphere Coupled Probabilistic Model
标题：利用多球耦合概率模型巧妙地对极端事件进行亚季到季节预测
链接：https://arxiv.org/abs/2512.12545
摘要：Accurate subseasonal-to-seasonal (S2S) prediction of extreme events is critical for resource planning and disaster mitigation under accelerating climate change. However, such predictions remain challenging due to complex multi-sphere interactions and intrinsic atmospheric uncertainty. Here we present TianXing-S2S, a multi-sphere coupled probabilistic model for global S2S daily ensemble forecast. TianXing-S2S first encodes diverse multi-sphere predictors into a compact latent space, then employs a diffusion model to generate daily ensemble forecasts. A novel coupling module based on optimal transport (OT) is incorporated in the denoiser to optimize the interactions between atmospheric and multi-sphere boundary conditions. Across key atmospheric variables, TianXing-S2S outperforms both the European Centre for Medium-Range Weather Forecasts (ECMWF) S2S system and FuXi-S2S in 45-day daily-mean ensemble forecasts at 1.5 resolution. Our model achieves skillful subseasonal prediction of extreme events including heat waves and anomalous precipitation, identifying soil moisture as a critical precursor signal. Furthermore, we demonstrate that TianXing-S2S can generate stable rollout forecasts up to 180 days, establishing a robust framework for S2S research in a warming world.
【9】AI-Driven Early Warning Systems for Student Success: Discovering Static Feature Dominance in Temporal Prediction Models
标题：人工智能驱动的学生成功预警系统：发现时间预测模型中的静态特征主导地位
链接：https://arxiv.org/abs/2512.12493
备注：5 pages, 3 figures, KDD 2026
摘要：Early identification of at-risk students is critical for effective intervention in online learning environments. This study extends temporal prediction analysis to Week 20 (50% of course duration), comparing Decision Tree and Long Short- Term Memory (LSTM) models across six temporal snapshots. Our analysis reveals that different performance metrics matter at different intervention stages: high recall is critical for early intervention (Weeks 2-4), while balanced precision-recall is important for mid-course resource allocation (Weeks 8-16), and high precision becomes paramount in later stages (Week 20). We demonstrate that static demographic features dominate predictions (68% importance), enabling assessment-free early prediction. The LSTM model achieves 97% recall at Week 2, making it ideal for early intervention, while Decision Tree provides stable balanced performance (78% accuracy) during mid-course. By Week 20, both models converge to similar recall (68%), but LSTM achieves higher precision (90% vs 86%). Our findings also suggest that model selection should depend on intervention timing, and that early signals (Weeks 2-4) are sufficient for reliable initial prediction using primarily demographic and pre-enrollment information.
【10】Fractional Differential Equation Physics-Informed Neural Network and Its Application in Battery State Estimation
标题：分数阶方程物理信息神经网络及其在电池状态估计中的应用
链接：https://arxiv.org/abs/2512.12285
摘要：Accurate estimation of the State of Charge (SOC) is critical for ensuring the safety, reliability, and performance optimization of lithium-ion battery systems. Conventional data-driven neural network models often struggle to fully characterize the inherent complex nonlinearities and memory-dependent dynamics of electrochemical processes, significantly limiting their predictive accuracy and physical interpretability under dynamic operating conditions. To address this challenge, this study proposes a novel neural architecture termed the Fractional Differential Equation Physics-Informed Neural Network (FDIFF-PINN), which integrates fractional calculus with deep learning. The main contributions of this paper include: (1) Based on a fractional-order equivalent circuit model, a discretized fractional-order partial differential equation is constructed. (2) Comparative experiments were conducted using a dynamic charge/discharge dataset of Panasonic 18650PF batteries under multi-temperature conditions (from -10$^{\circ}$C to 20$^{\circ}$C).
【11】GRC-Net: Gram Residual Co-attention Net for epilepsy prediction
标题：GRC-Net：用于癫痫预测的革兰氏残留共同注意网
链接：https://arxiv.org/abs/2512.12273
摘要 ：Prediction of epilepsy based on electroencephalogram (EEG) signals is a rapidly evolving field. Previous studies have traditionally applied 1D processing to the entire EEG signal. However, we have adopted the Gram Matrix method to transform the signals into a 3D representation, enabling modeling of signal relationships across dimensions while preserving the temporal dependencies of the one-dimensional signals. Additionally, we observed an imbalance between local and global signals within the EEG data. Therefore, we introduced multi-level feature extraction, utilizing coattention for capturing global signal characteristics and an inception structure for processing local signals, achieving multi-granular feature extraction. Our experiments on the BONN dataset demonstrate that for the most challenging five-class classification task, GRC-Net achieved an accuracy of 93.66%, outperforming existing methods.
【12】HydroDiffusion: Diffusion-Based Probabilistic Streamflow Forecasting with a State Space Backbone
标题：水力扩散：以状态空间为主干的基于扩散的概率径流预测
链接：https://arxiv.org/abs/2512.12183
摘要：Recent advances have introduced diffusion models for probabilistic streamflow forecasting, demonstrating strong early flood-warning skill. However, current implementations rely on recurrent Long Short-Term Memory (LSTM) backbones and single-step training objectives, which limit their ability to capture long-range dependencies and produce coherent forecast trajectories across lead times. To address these limitations, we developed HydroDiffusion, a diffusion-based probabilistic forecasting framework with a decoder-only state space model backbone. The proposed framework jointly denoises full multi-day trajectories in a single pass, ensuring temporal coherence and mitigating error accumulation common in autoregressive prediction. HydroDiffusion is evaluated across 531 watersheds in the contiguous United States (CONUS) in the CAMELS dataset. We benchmark HydroDiffusion against two diffusion baselines with LSTM backbones, as well as the recently proposed Diffusion-based Runoff Model (DRUM). Results show that HydroDiffusion achieves strong nowcast accuracy when driven by observed meteorological forcings, and maintains consistent performance across the full simulation horizon. Moreover, HydroDiffusion delivers stronger deterministic and probabilistic forecast skill than DRUM in operational forecasting. These results establish HydroDiffusion as a robust generative modeling framework for medium-range streamflow forecasting, providing both a new modeling benchmark and a foundation for future research on probabilistic hydrologic prediction at continental scales.
【13】Semantic Nutrition Estimation: Predicting Food Healthfulness from Text Descriptions
标题：语义营养估计：根据文本描述预测食品健康性
链接：https://arxiv.org/abs/2512.11836
备注：10 pages, 4 figures, 6 tables, submitted to AMIA 2026 Informatics Summit
摘要：Accurate nutritional assessment is critical for public health, but existing profiling systems require detailed data often unavailable or inaccessible from colloquial text descriptions of food. This paper presents a machine learning pipeline that predicts the comprehensive Food Compass Score 2.0 (FCS) from text descriptions. Our approach uses multi-headed neural networks to process hybrid feature vectors that combine semantic text embeddings, lexical patterns, and domain heuristics, alongside USDA Food and Nutrient Database for Dietary Studies (FNDDS) data. The networks estimate the nutrient and food components necessary for the FCS algorithm. The system demonstratedstrong predictive power, achieving a median R^2 of 0.81 for individual nutrients. The predicted FCS correlated strongly with published values (Pearson's r = 0.77), with a mean absolute difference of 14.0 points. While errors were largest for ambiguous or processed foods, this methodology translates language into actionable nutritional information, enabling scalable dietary assessment for consumer applications and research.
【14】Hybrid twinning using PBDW and DeepONet for the effective state estimation and prediction on partially known systems
标题：使用PBDW和DeepONet的混合孪生用于部分已知系统的有效状态估计和预测
链接：https://arxiv.org/abs/2512.11834
摘要：The accurate estimation of the state of complex uncertain physical systems requires reconciling theoretical models, with inherent imperfections, with noisy experimental data. In this work, we propose an effective hybrid approach that combines physics-based modeling with data-driven learning to enhance state estimation and further prediction. Our method builds upon the Parameterized Background Data-Weak (PBDW) framework, which naturally integrates a reduced-order representation of the best-available model with measurement data to account for both anticipated and unanticipated uncertainties. To address model discrepancies not captured by the reduced-order space, and learn the structure of model deviation, we incorporate a Deep Operator Network (DeepONet) constrained to be an orthogonal complement of the best-knowledge manifold. This ensures that the learned correction targets only the unknown components of model bias, preserving the interpretability and fidelity of the physical model. An optimal sensor placement strategy is also investigated to maximize information gained from measurements. We validate the proposed approach on a representative problem involving the Helmholtz equation under various sources of modeling error, including those arising from boundary conditions and source terms.
【15】Towards a pretrained deep learning estimator of the Linfoot informational correlation
标题：迈向Linfoot信息相关性的预训练深度学习估计器
链接：https://arxiv.org/abs/2512.12358
备注：3 figures
摘要 ：We develop a supervised deep-learning approach to estimate mutual information between two continuous random variables. As labels, we use the Linfoot informational correlation, a transformation of mutual information that has many important properties. Our method is based on ground truth labels for Gaussian and Clayton copulas. We compare our method with estimators based on kernel density, k-nearest neighbours and neural estimators. We show generally lower bias and lower variance. As a proof of principle, future research could look into training the model with a more diverse set of examples from other copulas for which ground truth labels are available.
【16】Stochastic Volatility Modelling with LSTM Networks: A Hybrid Approach for S&P 500 Index Volatility Forecasting
标题：使用LSTM网络的随机波动率建模：标准普尔500指数波动率预测的混合方法
链接：https://arxiv.org/abs/2512.12250
备注：32 pages, 15 tables, 11 figures
摘要：Accurate volatility forecasting is essential in banking, investment, and risk management, because expectations about future market movements directly influence current decisions. This study proposes a hybrid modelling framework that integrates a Stochastic Volatility model with a Long Short Term Memory neural network. The SV model improves statistical precision and captures latent volatility dynamics, especially in response to unforeseen events, while the LSTM network enhances the model's ability to detect complex nonlinear patterns in financial time series. The forecasting is conducted using daily data from the S and P 500 index, covering the period from January 1 1998 to December 31 2024. A rolling window approach is employed to train the model and generate one step ahead volatility forecasts. The performance of the hybrid SV-LSTM model is evaluated through both statistical testing and investment simulations. The results show that the hybrid approach outperforms both the standalone SV and LSTM models and contributes to the development of volatility modelling techniques, providing a foundation for improving risk assessment and strategic investment planning in the context of the S and P 500.
【17】A Multitask VAE for Time Series Preprocessing and Prediction of Blood Glucose Level
标题：用于时间序列预处理和血糖水平预测的多任务VAE
链接：https://arxiv.org/abs/2410.00015
摘要：Data preprocessing is a critical part of time series data analysis. Data from connected medical devices often have missing or abnormal values during acquisition. Handling such situations requires additional assumptions and domain knowledge. This can be time-consuming, and can introduce a significant bias affecting predictive model accuracy and thus, medical interpretation. To overcome this issue, we propose a new deep learning model to mitigate the preprocessing assumptions. The model architecture relies on a variational auto-encoder (VAE) to produce a preprocessing latent space, and a recurrent VAE to preserve the temporal dynamics of the data. We demonstrate the effectiveness of such an architecture on telemonitoring data to forecast glucose-level of diabetic patients. Our results show an improvement in terms of accuracy with respect of existing state-of-the-art methods and architectures.
其他神经网络|深度学习|模型|建模(51篇)
【1】SEDULity: A Proof-of-Learning Framework for Distributed and Secure Blockchains with Efficient Useful Work
标题：SEDULity：一个用于分布式和安全区块链的学习证明框架，具有有效的有用工作
链接：https://arxiv.org/abs/2512.13666
摘要：The security and decentralization of Proof-of-Work (PoW) have been well-tested in existing blockchain systems. However, its tremendous energy waste has raised concerns about sustainability. Proof-of-Useful-Work (PoUW) aims to redirect the meaningless computation to meaningful tasks such as solving machine learning (ML) problems, giving rise to the branch of Proof-of-Learning (PoL). While previous studies have proposed various PoLs, they all, to some degree, suffer from security, decentralization, or efficiency issues. In this paper, we propose a PoL framework that trains ML models efficiently while maintaining blockchain security in a fully distributed manner. We name the framework SEDULity, which stands for a Secure, Efficient, Distributed, and Useful Learning-based blockchain system. Specifically, we encode the template block into the training process and design a useful function that is difficult to solve but relatively easy to verify, as a substitute for the PoW puzzle. We show that our framework is distributed, secure, and efficiently trains ML models. We further demonstrate that the proposed PoL framework can be extended to other types of useful work and design an incentive mechanism to incentivize task verification. We show theoretically that a rational miner is incentivized to train fully honestly with well-designed system parameters. Finally, we present simulation results to demonstrate the performance of our framework and validate our analysis.
【2】Learning under Distributional Drift: Reproducibility as an Intrinsic Statistical Resource
标题：分布漂移下的学习：作为固有统计资源的可重复性
链接：https://arxiv.org/abs/2512.13506
备注：37 pages, 4 figures
摘要：Statistical learning under distributional drift remains insufficiently characterized: when each observation alters the data-generating law, classical generalization bounds can collapse. We introduce a new statistical primitive, the reproducibility budget $C_T$, which quantifies a system's finite capacity for statistical reproducibility - the extent to which its sampling process can remain governed by a consistent underlying distribution in the presence of both exogenous change and endogenous feedback. Formally, $C_T$ is defined as the cumulative Fisher-Rao path length of the coupled learner-environment evolution, measuring the total distributional motion accumulated during learning. From this construct we derive a drift-feedback generalization bound of order $O(T^{-1/2} + C_T/T)$, and we prove a matching minimax lower bound showing that this rate is minimax-optimal. Consequently, the results establish a reproducibility speed limit: no algorithm can achieve smaller worst-case generalization error than that imposed by the average Fisher-Rao drift rate $C_T/T$ of the data-generating process. The framework situates exogenous drift, adaptive data analysis, and performative prediction within a common geometric structure, with $C_T$ emerging as the intrinsic quantity measuring distributional motion across these settings.
【3】DP-EMAR: A Differentially Private Framework for Autonomous Model Weight Repair in Federated IoT Systems
标题：DP-Eamar：联邦物联网系统中用于自主模型重量修复的差异私有框架
链接：https://arxiv.org/abs/2512.13460
备注：Accepted and presented at the AI-IoT Workshop, co-located with COMSNETS 2025
摘要：Federated Learning (FL) enables decentralized model training without sharing raw data, but model weight distortion remains a major challenge in resource constrained IoT networks. In multi tier Federated IoT (Fed-IoT) systems, unstable connectivity and adversarial interference can silently alter transmitted parameters, degrading convergence. We propose DP-EMAR, a differentially private, error model based autonomous repair framework that detects and reconstructs transmission induced distortions during FL aggregation. DP-EMAR estimates corruption patterns and applies adaptive correction before privacy noise is added, enabling reliable in network repair without violating confidentiality. By integrating Differential Privacy (DP) with Secure Aggregation (SA), the framework distinguishes DP noise from genuine transmission errors. Experiments on heterogeneous IoT sensor and graph datasets show that DP-EMAR preserves convergence stability and maintains near baseline performance under communication corruption while ensuring strict (epsilon, delta)-DP guarantees. The framework enhances robustness, communication efficiency, and trust in privacy preserving Federated IoT learning.
【4】XNNTab -- Interpretable Neural Networks for Tabular Data using Sparse Autoencoders
标题：XNNTab--使用稀疏自动编码器的表格数据可解释神经网络
链接：https://arxiv.org/abs/2512.13442
摘要：In data-driven applications relying on tabular data, where interpretability is key, machine learning models such as decision trees and linear regression are applied. Although neural networks can provide higher predictive performance, they are not used because of their blackbox nature. In this work, we present XNNTab, a neural architecture that combines the expressiveness of neural networks and interpretability. XNNTab first learns highly non-linear feature representations, which are decomposed into monosemantic features using a sparse autoencoder (SAE). These features are then assigned human-interpretable concepts, making the overall model prediction intrinsically interpretable. XNNTab outperforms interpretable predictive models, and achieves comparable performance to its non-interpretable counterparts.
【5】MineTheGap: Automatic Mining of Biases in Text-to-Image Models
标题：MineTheGap：文本到图像模型中的偏差自动挖掘
链接：https://arxiv.org/abs/2512.13427
备注：Code and examples are available on the project's webpage at https://noa-cohen.github.io/MineTheGap/
摘要：Text-to-Image (TTI) models generate images based on text prompts, which often leave certain aspects of the desired image ambiguous. When faced with these ambiguities, TTI models have been shown to exhibit biases in their interpretations. These biases can have societal impacts, e.g., when showing only a certain race for a stated occupation. They can also affect user experience when creating redundancy within a set of generated images instead of spanning diverse possibilities. Here, we introduce MineTheGap - a method for automatically mining prompts that cause a TTI model to generate biased outputs. Our method goes beyond merely detecting bias for a given prompt. Rather, it leverages a genetic algorithm to iteratively refine a pool of prompts, seeking for those that expose biases. This optimization process is driven by a novel bias score, which ranks biases according to their severity, as we validate on a dataset with known biases. For a given prompt, this score is obtained by comparing the distribution of generated images to the distribution of LLM-generated texts that constitute variations on the prompt. Code and examples are available on the project's webpage.
【6】Fast Policy Learning for 6-DOF Position Control of Underwater Vehicles
标题：水下航行器6自由度位置控制的快速策略学习
链接：https://arxiv.org/abs/2512.13359
摘要：Autonomous Underwater Vehicles (AUVs) require reliable six-degree-of-freedom (6-DOF) position control to operate effectively in complex and dynamic marine environments. Traditional controllers are effective under nominal conditions but exhibit degraded performance when faced with unmodeled dynamics or environmental disturbances. Reinforcement learning (RL) provides a powerful alternative but training is typically slow and sim-to-real transfer remains challenging. This work introduces a GPU-accelerated RL training pipeline built in JAX and MuJoCo-XLA (MJX). By jointly JIT-compiling large-scale parallel physics simulation and learning updates, we achieve training times of under two minutes.Through systematic evaluation of multiple RL algorithms, we show robust 6-DOF trajectory tracking and effective disturbance rejection in real underwater experiments, with policies transferred zero-shot from simulation. Our results provide the first explicit real-world demonstration of RL-based AUV position control across all six degrees of freedom.
【7】LINA: Learning INterventions Adaptively for Physical Alignment and Generalization in Diffusion Models
标题：LINA：适应性地学习干预以适应扩散模型中的物理对齐和概括
链接：https://arxiv.org/abs/2512.13290
摘要 ：Diffusion models (DMs) have achieved remarkable success in image and video generation. However, they still struggle with (1) physical alignment and (2) out-of-distribution (OOD) instruction following. We argue that these issues stem from the models' failure to learn causal directions and to disentangle causal factors for novel recombination. We introduce the Causal Scene Graph (CSG) and the Physical Alignment Probe (PAP) dataset to enable diagnostic interventions. This analysis yields three key insights. First, DMs struggle with multi-hop reasoning for elements not explicitly determined in the prompt. Second, the prompt embedding contains disentangled representations for texture and physics. Third, visual causal structure is disproportionately established during the initial, computationally limited denoising steps. Based on these findings, we introduce LINA (Learning INterventions Adaptively), a novel framework that learns to predict prompt-specific interventions, which employs (1) targeted guidance in the prompt and visual latent spaces, and (2) a reallocated, causality-aware denoising schedule. Our approach enforces both physical alignment and OOD instruction following in image and video DMs, achieving state-of-the-art performance on challenging causal generation tasks and the Winoground dataset. Our project page is at https://opencausalab.github.io/LINA.
【8】Learning to Retrieve with Weakened Labels: Robust Training under Label Noise
标题：学习使用弱化标签：标签噪音下的稳健训练
链接：https://arxiv.org/abs/2512.13237
摘要：Neural Encoders are frequently used in the NLP domain to perform dense retrieval tasks, for instance, to generate the candidate documents for a given query in question-answering tasks. However, sparse annotation and label noise in the training data make it challenging to train or fine-tune such retrieval models. Although existing works have attempted to mitigate these problems by incorporating modified loss functions or data cleaning, these approaches either require some hyperparameters to tune during training or add substantial complexity to the training setup. In this work, we consider a label weakening approach to generate robust retrieval models in the presence of label noise. Instead of enforcing a single, potentially erroneous label for each query document pair, we allow for a set of plausible labels derived from both the observed supervision and the model's confidence scores. We perform an extensive evaluation considering two retrieval models, one re-ranking model, considering four diverse ranking datasets. To this end, we also consider a realistic noisy setting by using a semantic-aware noise generation technique to generate different ratios of noise. Our initial results show that label weakening can improve the performance of the retrieval tasks in comparison to 10 different state-of-the-art loss functions.
【9】PolySet: Restoring the Statistical Ensemble Nature of Polymers for Machine Learning
标题：PolySet：恢复聚合物的统计包容性用于机器学习
链接：https://arxiv.org/abs/2512.13186
摘要：Machine-learning (ML) models in polymer science typically treat a polymer as a single, perfectly defined molecular graph, even though real materials consist of stochastic ensembles of chains with distributed lengths. This mismatch between physical reality and digital representation limits the ability of current models to capture polymer behaviour. Here we introduce PolySet, a framework that represents a polymer as a finite, weighted ensemble of chains sampled from an assumed molar-mass distribution. This ensemble-based encoding is independent of chemical detail, compatible with any molecular representation and illustrated here in the homopolymer case using a minimal language model. We show that PolySet retains higher-order distributional moments (such as Mz, Mz+1), enabling ML models to learn tail-sensitive properties with greatly improved stability and accuracy. By explicitly acknowledging the statistical nature of polymer matter, PolySet establishes a physically grounded foundation for future polymer machine learning, naturally extensible to copolymers, block architectures, and other complex topologies.
【10】Quanvolutional Neural Networks for Spectrum Peak-Finding
标题：用于谱峰查找的量子卷积神经网络
链接：https://arxiv.org/abs/2512.13125
摘要：The analysis of spectra, such as Nuclear Magnetic Resonance (NMR) spectra, for the comprehensive characterization of peaks is a challenging task for both experts and machines, especially with complex molecules. This process, also known as deconvolution, involves identifying and quantifying the peaks in the spectrum. Machine learning techniques have shown promising results in automating this process. With the advent of quantum computing, there is potential to further enhance these techniques. In this work, inspired by the success of classical Convolutional Neural Networks (CNNs), we explore the use of Quanvolutional Neural Networks (QuanvNNs) for the multi-task peak finding problem, involving both peak counting and position estimation. We implement a simple and interpretable QuanvNN architecture that can be directly compared to its classical CNN counterpart, and evaluate its performance on a synthetic NMR-inspired dataset. Our results demonstrate that QuanvNNs outperform classical CNNs on challenging spectra, achieving an 11\% improvement in F1 score and a 30\% reduction in mean absolute error for peak position estimation. Additionally, QuanvNNs appear to exhibit better convergence stability for harder problems.
【11】From Overfitting to Reliability: Introducing the Hierarchical Approximate Bayesian Neural Network
标题：从过拟适到可靠性：引入分层近似Bayesian神经网络
链接：https://arxiv.org/abs/2512.13111
备注：9 pages main body, 1 Figure, 15 pages Appendix
摘要：In recent years, neural networks have revolutionized various domains, yet challenges such as hyperparameter tuning and overfitting remain significant hurdles. Bayesian neural networks offer a framework to address these challenges by incorporating uncertainty directly into the model, yielding more reliable predictions, particularly for out-of-distribution data. This paper presents Hierarchical Approximate Bayesian Neural Network, a novel approach that uses a Gaussian-inverse-Wishart distribution as a hyperprior of the network's weights to increase both the robustness and performance of the model. We provide analytical representations for the predictive distribution and weight posterior, which amount to the calculation of the parameters of Student's t-distributions in closed form with linear complexity with respect to the number of weights. Our method demonstrates robust performance, effectively addressing issues of overfitting and providing reliable uncertainty estimates, particularly for out-of-distribution tasks. Experimental results indicate that HABNN not only matches but often outperforms state-of-the-art models, suggesting a promising direction for future applications in safety-critical environments.
【12】Motus: A Unified Latent Action World Model
标题：动机：统一的潜在行动世界模型
链接：https://arxiv.org/abs/2512.13030
摘要：While a general embodied agent must function as a unified system, current methods are built on isolated models for understanding, world modeling, and control. This fragmentation prevents unifying multimodal generative capabilities and hinders learning from large-scale, heterogeneous data. In this paper, we propose Motus, a unified latent action world model that leverages existing general pretrained models and rich, sharable motion information. Motus introduces a Mixture-of-Transformer (MoT) architecture to integrate three experts (i.e., understanding, video generation, and action) and adopts a UniDiffuser-style scheduler to enable flexible switching between different modeling modes (i.e., world models, vision-language-action models, inverse dynamics models, video generation models, and video-action joint prediction models). Motus further leverages the optical flow to learn latent actions and adopts a recipe with three-phase training pipeline and six-layer data pyramid, thereby extracting pixel-level "delta action" and enabling large-scale action pretraining. Experiments show that Motus achieves superior performance against state-of-the-art methods in both simulation (a +15% improvement over X-VLA and a +45% improvement over Pi0.5) and real-world scenarios(improved by +11~48%), demonstrating unified modeling of all functionalities and priors significantly benefits downstream robotic tasks.
【13】Comprehensive Deployment-Oriented Assessment for Cross-Environment Generalization in Deep Learning-Based mmWave Radar Sensing
标题：基于深度学习的毫米波雷达传感跨环境通用的面向部署的综合评估
链接：https://arxiv.org/abs/2512.13018
备注：8 pages, 6 figures. Comprehensive evaluation of preprocessing, data augmentation, and transfer learning for cross-environment generalization in deep learning-based mmWave radar sensing
摘要：This study presents the first comprehensive evaluation of spatial generalization techniques, which are essential for the practical deployment of deep learning-based radio-frequency (RF) sensing. Focusing on people counting in indoor environments using frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) radar, we systematically investigate a broad set of approaches, including amplitude-based statistical preprocessing (sigmoid weighting and threshold zeroing), frequency-domain filtering, autoencoder-based background suppression, data augmentation strategies, and transfer learning. Experimental results collected across two environments with different layouts demonstrate that sigmoid-based amplitude weighting consistently achieves superior cross-environment performance, yielding 50.1% and 55.2% reductions in root-mean-square error (RMSE) and mean absolute error (MAE), respectively, compared with baseline methods. Data augmentation provides additional though modest benefits, with improvements up to 8.8% in MAE. By contrast, transfer learning proves indispensable for large spatial shifts, achieving 82.1% and 91.3% reductions in RMSE and MAE, respectively, with 540 target-domain samples. Taken together, these findings establish a highly practical direction for developing radar sensing systems capable of maintaining robust accuracy under spatial variations by integrating deep learning models with amplitude-based preprocessing and efficient transfer learning.
【14】VoroLight: Learning Quality Volumetric Voronoi Meshes from General Inputs
标题：VoroLight：来自一般输入的学习质量体积Voronoi网格
链接：https://arxiv.org/abs/2512.12984
摘要：We present VoroLight, a differentiable framework for 3D shape reconstruction based on Voronoi meshing. Our approach generates smooth, watertight surfaces and topologically consistent volumetric meshes directly from diverse inputs, including images, implicit shape level-set fields, point clouds and meshes. VoroLight operates in three stages: it first initializes a surface using a differentiable Voronoi formulation, then refines surface quality through a polygon-face sphere training stage, and finally reuses the differentiable Voronoi formulation for volumetric optimization with additional interior generator points. Project page: https://jiayinlu19960224.github.io/vorolight/
【15】CoDeQ: End-to-End Joint Model Compression with Dead-Zone Quantizer for High-Sparsity and Low-Precision Networks
标题：CoDeQ：用于高稀疏度和低精度网络的端到端联合模型压缩和死区量化器
链接：https://arxiv.org/abs/2512.12981
备注：Source code at https://github.com/saintslab/CoDeQ
【16】Application of Deep Learning in Biological Data Compression
标题：深度学习在生物数据压缩中的应用
链接：https://arxiv.org/abs/2512.12975
【17】Investigating Data Pruning for Pretraining Biological Foundation Models at Scale
标题：调查大规模预训练生物基础模型的数据修剪
链接：https://arxiv.org/abs/2512.12932
备注：Accepted by AAAI 2026
【18】Qonvolution: Towards Learning High-Frequency Signals with Queried Convolution
标题：量子卷积：通过查询卷积学习高频信号
链接：https://arxiv.org/abs/2512.12898
备注：28 pages, 8 figures, Project Page: https://abhi1kumar.github.io/qonvolution/
【19】KANELÉ: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation
标题：KANELSYS：用于高效基于LU的评估的Kolmogorov-Arnold网络
链接：https://arxiv.org/abs/2512.12850
备注：International Symposium on Field-Programmable Gate Arrays 2026 (ISFPGA'2026)
【20】HaShiFlex: A High-Throughput Hardened Shifter DNN Accelerator with Fine-Tuning Flexibility
标题：HaShiFlex：具有微调灵活性的高精度硬化移位器DNN加速器
链接：https://arxiv.org/abs/2512.12847
备注：12 pages, 6 figures, 5 tables
【21】Network Level Evaluation of Hangup Susceptibility of HRGCs using Deep Learning and Sensing Techniques: A Goal Towards Safer Future
标题：使用深度学习和传感技术对HRGC的挂起敏感性进行网络级评估：迈向更安全未来的目标
链接：https://arxiv.org/abs/2512.12832
【22】SPARK: Igniting Communication-Efficient Decentralized Learning via Stage-wise Projected NTK and Accelerated Regularization
标题：SPARK：通过分阶段投射NTK和加速正规化来验证沟通高效的去中心化学习
链接：https://arxiv.org/abs/2512.12737
备注：11 pages, 8 figures
【23】Solving a Machine Learning Regression Problem Based on the Theory of Random Functions
标题：基于随机函数理论解决机器学习回归问题
链接：https://arxiv.org/abs/2512.12731
备注：Part 1 of 4 in the "Polyharmonic Cascade" cycle. 25 pages, 2 figures. Source code is available at: https://github.com/xolod7/polyharmonic-cascade
【24】On Approaches to Building Surrogate ODE Models for Diffusion Bridges
标题：扩散桥代理ODE模型的建立方法
链接：https://arxiv.org/abs/2512.12671
【25】PerNodeDrop: A Method Balancing Specialized Subnets and Regularization in Deep Neural Networks
标题：PerNodeDrop：一种平衡深度神经网络中专用子网络和规则化的方法
链接：https://arxiv.org/abs/2512.12663
【26】Learning Dynamics in Memristor-Based Equilibrium Propagation
标题：基于记忆存储器的平衡传播中的学习动力学
链接：https://arxiv.org/abs/2512.12428
【27】Anchoring Values in Temporal and Group Dimensions for Flow Matching Model Alignment
标题：流匹配模型对齐的时间和组维度中的锚定值
链接：https://arxiv.org/abs/2512.12387
【28】The Data Efficiency Frontier of Financial Foundation Models: Scaling Laws from Continued Pretraining
标题：金融基础模型的数据效率前沿：持续预训练的缩放定律
链接：https://arxiv.org/abs/2512.12384
备注：8 pages, 4 figures, 1 table
【29】Balancing Accuracy and Speed: A Multi-Fidelity Ensemble Kalman Filter with a Machine Learning Surrogate Model
标题：平衡精度和速度：具有机器学习代理模型的多保真度Enhancement Kalman滤波器
链接：https://arxiv.org/abs/2512.12276
【30】MeltwaterBench: Deep learning for spatiotemporal downscaling of surface meltwater
标题：MeltwaterBench：深度学习用于表面融水时空缩减
链接：https://arxiv.org/abs/2512.12142
【31】MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Models
标题：MixtureKit：用于构建、训练和可视化混合专家模型的通用框架
链接：https://arxiv.org/abs/2512.12121
【32】Neural CDEs as Correctors for Learned Time Series Models
标题：神经CDO作为习得时间序列模型的修正器
链接：https://arxiv.org/abs/2512.12116
【33】BAgger: Backwards Aggregation for Mitigating Drift in Autoregressive Video Diffusion Models
标题：Bagger：向后聚集以缓解自回归视频扩散模型中的漂移
链接：https://arxiv.org/abs/2512.12080
备注：Project page here: https://ryanpo.com/bagger
【34】SigTime: Learning and Visually Explaining Time Series Signatures
标题：SigTime：学习和可视化解释时间序列签名
链接：https://arxiv.org/abs/2512.12076
【35】Physics-informed neural networks to solve inverse problems in unbounded domains
标题：物理信息神经网络解决无界域中的逆问题
链接：https://arxiv.org/abs/2512.12074
备注：19 pages, 15 figures
【36】Phase transitions reveal hierarchical structure in deep neural networks
标题：相转变揭示深度神经网络中的分层结构
链接：https://arxiv.org/abs/2512.11866
备注：15 pages, 5 figures
【37】Rep Smarter, Not Harder: AI Hypertrophy Coaching with Wearable Sensors and Edge Neural Networks
标题：Rep更聪明，而不是更难：使用可穿戴传感器和边缘神经网络进行AI肥大辅导
链接：https://arxiv.org/abs/2512.11854
备注：24th International Conference on Machine Learning and Applications
【38】Evolving Deep Learning Optimizers
标题：不断发展的深度学习优化器
链接：https://arxiv.org/abs/2512.11853
【39】Tiny Recursive Models on ARC-AGI-1: Inductive Biases, Identity Conditioning, and Test-Time Compute
标题：ARC-AGI-1上的微型回归模型：归纳偏差、身份条件反射和测试时间计算
链接：https://arxiv.org/abs/2512.11847
备注：13 pages, 0 figures, 6 tables
【40】Amortized Causal Discovery with Prior-Fitted Networks
标题：利用先验匹配网络的摊销因果发现
链接：https://arxiv.org/abs/2512.11840
【41】D-STEER - Preference Alignment Techniques Learn to Behave, not to Believe - Beneath the Surface, DPO as Steering Vector Perturbation in Activation Space
标题：D-STEER -偏好对齐技术学会行为，而不是相信-在表面之下，DPO作为激活空间中的引导载体扰动
链接：https://arxiv.org/abs/2512.11838
【42】A Nonparametric Statistics Approach to Feature Selection in Deep Neural Networks with Theoretical Guarantees
标题：具有理论保证的深度神经网络特征选择的非参数统计方法
链接：https://arxiv.org/abs/2512.13565
备注：64 pages
【43】Actively Learning Joint Contours of Multiple Computer Experiments
标题：积极学习多个计算机实验的联合轮廓
链接：https://arxiv.org/abs/2512.13530
【44】Enhancing lithological interpretation from petrophysical well log of IODP expedition 390/393 using machine learning
标题：使用机器学习加强IODP探险390/393岩石物理井记录的岩石学解释
链接：https://arxiv.org/abs/2512.13529
备注：Accepted for presentation at the International Meeting for Applied Geoscience & Energy (IMAGE) 2025
【45】A Deep Learning Model of Mental Rotation Informed by Interactive VR Experiments
标题：交互式VR实验提供的心理旋转深度学习模型
链接：https://arxiv.org/abs/2512.13517
【46】Evaluating Singular Value Thresholds for DNN Weight Matrices based on Random Matrix Theory
标题：基于随机矩阵理论的DNN权矩阵奇异值阈值估计
链接：https://arxiv.org/abs/2512.12911
【47】Flow-matching Operators for Residual-Augmented Probabilistic Learning of Partial Differential Equations
标题：偏方程剩余增广概率学习的流匹配操作
链接：https://arxiv.org/abs/2512.12749
【48】Limits To (Machine) Learning
标题：（机器）学习的局限性
链接：https://arxiv.org/abs/2512.12735
【49】Scalable Quantum Error Mitigation with Neighbor-Informed Learning
标题：通过邻居知情学习实现可扩展的量子错误缓解
链接：https://arxiv.org/abs/2512.12578
【50】Extending the application of dynamic Bayesian networks in calculating market risk: Standard and stressed expected shortfall
标题：扩展动态Bayesian网络在计算市场风险中的应用：标准和强调预期缺口
链接：https://arxiv.org/abs/2512.12334
【51】Vision Foundry: A System for Training Foundational Vision AI Models
标题：Vision Foundry：训练基础视觉人工智能模型的系统
链接：https://arxiv.org/abs/2512.11837
备注：10 pages, 4 figures, 3 tables, submitted to AMIA 2026 Informatics Summit
其他(61篇)
【1】DiffusionBrowser: Interactive Diffusion Previews via Multi-Branch Decoders
标题：扩散浏览器：通过多分支解码器进行交互式扩散预览
链接：https://arxiv.org/abs/2512.13690
备注：Project page: https://susunghong.github.io/DiffusionBrowser
【2】Image Diffusion Preview with Consistency Solver
标题：使用一致性求解器的图像扩散预览
链接：https://arxiv.org/abs/2512.13592
【3】DP-CSGP: Differentially Private Stochastic Gradient Push with Compressed Communication
标题：DP-CSGP：采用压缩通信的差异私有随机梯度推送
链接：https://arxiv.org/abs/2512.13583
备注：13 pages
【4】From Zipf's Law to Neural Scaling through Heaps' Law and Hilberg's Hypothesis
标题
：从齐普夫定律到希普斯定律和希尔伯格假说的神经缩放
链接：https://arxiv.org/abs/2512.13491
备注：32 pages, no figures
【5】Real-Time AI-Driven Milling Digital Twin Towards Extreme Low-Latency
标题：实时人工智能驱动的铣削数字双胞胎迈向极低延迟
链接：https://arxiv.org/abs/2512.13482
【6】Element-wise Modulation of Random Matrices for Efficient Neural Layers
标题：有效神经层的随机矩阵逐元素调制
链接：https://arxiv.org/abs/2512.13480
【7】rNCA: Self-Repairing Segmentation Masks
标题：rNCA：自我修复的分割口罩
链接：https://arxiv.org/abs/2512.13397
【8】Dual-Phase Federated Deep Unlearning via Weight-Aware Rollback and Reconstruction
标题：通过权重感知回滚和重建的双阶段联合深度取消学习
链接：https://arxiv.org/abs/2512.13381
备注：10 pages, submitted to INFOCOM 2026
【9】Control of a Twin Rotor using Twin Delayed Deep Deterministic Policy Gradient (TD3)
标题：使用双延迟深度确定性政策梯度（TD 3）控制双螺旋桨
链接：https://arxiv.org/abs/2512.13356
备注：This is the Author Accepted Manuscript version of a paper accepted for publication. The final published version is available via IEEE Xplore
【10】KD-PINN: Knowledge-Distilled PINNs for ultra-low-latency real-time neural PDE solvers
标题：KD-PINN：用于超低延迟实时神经DTE求解器的知识蒸馏PINN
链接：https://arxiv.org/abs/2512.13336
【11】Face Identity Unlearning for Retrieval via Embedding Dispersion
标题：通过嵌入离散来检索人脸身份
链接：https://arxiv.org/abs/2512.13317
备注：12 pages, 1 figure, 5 tables, 10 equations. Preprint
【12】SACn: Soft Actor-Critic with n-step Returns
标题：SACN：n步回归的软演员评论家
链接：https://arxiv.org/abs/2512.13165
备注：Accepted at ICAART 2026
【13】DiRe: Diversity-promoting Regularization for Dataset Condensation
标题：DiRe：促进数据集压缩多元化的正规化
链接：https://arxiv.org/abs/2512.13083
备注：Accepted to WACV 2026
【14】Progressive Refinement of E-commerce Search Ranking Based on Short-Term Activities of the Buyer
标题：根据买家短期活动逐步细化电子商务搜索排名
链接：https://arxiv.org/abs/2512.13037
【15】Scaling Bidirectional Spans and Span Violations in Attention Mechanism
标题：注意机制中的尺度双向广度和广度违反
链接：https://arxiv.org/abs/2512.13033
【16】Continuous Edit Distance, Geodesics and Barycenters of Time-varying Persistence Diagrams
标题：时变持久图的连续编辑距离、测地线和重心
链接：https://arxiv.org/abs/2512.12939
备注：30 pages, 13 figures, 2 tables
【17】Forgetful but Faithful: A Cognitive Memory Architecture and Benchmark for Privacy-Aware Generative Agents
标题：健忘但忠诚：认知记忆架构和隐私意识生成代理的基准
链接：https://arxiv.org/abs/2512.12856
【18】Selective Conformal Risk Control
标题：选择性合规风险控制
链接：https://arxiv.org/abs/2512.12844
【19】SAGA: Open-World Mobile Manipulation via Structured Affordance Grounding
标题：SAGA：通过结构化负担基础进行开放世界移动操纵
链接：https://arxiv.org/abs/2512.12842
备注：9 pages, 7 figures
【20】On the continuity of flows
标题：关于流动的连续性
链接：https://arxiv.org/abs/2512.12821
备注：9 pages, 2 figures
【21】Hindsight is 20/20: Building Agent Memory that Retains, Recalls, and Reflects
标题：事后诸葛亮是20/20：保留、召回和反映的构建代理记忆
链接：https://arxiv.org/abs/2512.12818
【22】Unveiling Statistical Significance of Online Regression over Multiple Datasets
标题：揭示多个数据集在线回归的统计意义
链接：https://arxiv.org/abs/2512.12787
【23】Co-Exploration and Co-Exploitation via Shared Structure in Multi-Task Bandits
标题：多任务盗贼通过共享结构进行共同探索和共同利用
链接：https://arxiv.org/abs/2512.12693
备注：18 pages, 9 figures, preprint
【24】WebOperator: Action-Aware Tree Search for Autonomous Agents in Web Environment
标题：Web操作员：Web环境中自治代理的感知树搜索
链接：https://arxiv.org/abs/2512.12692
备注：Under review at ICLR 2026. Project page: https://kagnlp.github.io/WebOperator/
【25】Theoretical Foundations of Prompt Engineering: From Heuristics to Expressivity
标题：即时工程的理论基础：从启发式到表达性
链接：https://arxiv.org/abs/2512.12688
备注：24 pages
【26】Error-Free Linear Attention is a Free Lunch: Exact Solution from Continuous-Time Dynamics
标题：无错误线性注意力是免费午餐：连续时间动力学的精确解决方案
链接：https://arxiv.org/abs/2512.12602
备注：17 pages, 2 figures
【27】ceLLMate: Sandboxing Browser AI Agents
标题：ceLLMate：沙箱浏览器人工智能代理
链接：https://arxiv.org/abs/2512.12594
备注：Homepage: https://cellmate-sandbox.github.io
【28】On the Accuracy of Newton Step and Influence Function Data Attributions
标题：牛顿步和影响函数数据属性的准确性
链接：https://arxiv.org/abs/2512.12572
【29】Effective Fine-Tuning with Eigenvector Centrality Based Pruning
标题：利用基于特征载体中心性的修剪进行有效的微调
链接：https://arxiv.org/abs/2512.12543
备注：12 pages
【30】Generative Spatiotemporal Data Augmentation
标题：生成式时空数据增强
链接：https://arxiv.org/abs/2512.12508
【31】Exploring the Design Space of Transition Matching
标题：探索过渡匹配的设计空间
链接：https://arxiv.org/abs/2512.12465
【32】Breaking the Curse of Dimensionality: On the Stability of Modern Vector Retrieval
标题：打破主观性诅咒：现代载体检索的稳定性
链接：https://arxiv.org/abs/2512.12458
备注：27 pages
【33】ViInfographicVQA: A Benchmark for Single and Multi-image Visual Question Answering on Vietnamese Infographics
标题：ViInfographicVQA：越南信息图表上单图像和多图像视觉问题回答的基准
链接：https://arxiv.org/abs/2512.12424
备注：10 pages, 4 figures, Accepted to AI4Research @ AAAI
【34】ElasticVR: Elastic Task Computing in Multi-User Multi-Connectivity Wireless Virtual Reality (VR) Systems
标题：ElasticVR：多用户多连接性无线虚拟现实（VR）系统中的弹性任务计算
链接：https://arxiv.org/abs/2512.12366
备注：Submitted to ACM TOMM
【35】Eventually LIL Regret: Almost Sure $\ln\ln T$ Regret for a sub-Gaussian Mixture on Unbounded Data
链接：https://arxiv.org/abs/2512.12325
备注：24 pages
【36】GrowTAS: Progressive Expansion from Small to Large Subnets for Efficient ViT Architecture Search
标题：Grow塔斯：从小到大的子网络逐步扩展，以实现高效的ViT架构搜索
链接：https://arxiv.org/abs/2512.12296
备注：Accepted to WACV 2026
【37】Journey Before Destination: On the importance of Visual Faithfulness in Slow Thinking
标题：目的地前之旅：视觉忠实在缓慢思维中的重要性
链接：https://arxiv.org/abs/2512.12218
备注：Preprint
【38】A Novel Patch-Based TDA Approach for Computed Tomography
标题：一种新型的基于补丁的计算机断层扫描方法
链接：https://arxiv.org/abs/2512.12108
【39】SPDMark: Selective Parameter Displacement for Robust Video Watermarking
标题：SPDMark：用于鲁棒视频水印的选择性参数置换
链接：https://arxiv.org/abs/2512.12090
【40】Reliable Policy Iteration: Performance Robustness Across Architecture and Environment Perturbations
标题：可靠的策略迭代：跨架构和环境扰动的性能稳健性
链接：https://arxiv.org/abs/2512.12088
【41】CLOAK: Contrastive Guidance for Latent Diffusion-Based Data Obfuscation
标题：COTEK：基于潜在扩散的数据混淆的对比指南
链接：https://arxiv.org/abs/2512.12086
【42】CreativeVR: Diffusion-Prior-Guided Approach for Structure and Motion Restoration in Generative and Real Videos
标题
：CreativeVR：扩散优先引导方法，用于生成和真实视频中的结构和运动恢复
链接：https://arxiv.org/abs/2512.12060
备注：The first two authors contributed equally
【43】Policy Gradient Algorithms for Age-of-Information Cost Minimization
标题：信息共享成本最小化的政策梯度算法
链接：https://arxiv.org/abs/2512.11990
【44】mmWEAVER: Environment-Specific mmWave Signal Synthesis from a Photo and Activity Description
标题：mmWEAVER：根据照片和活动描述进行环境特定毫米波信号合成
链接：https://arxiv.org/abs/2512.11894
备注：Accepted at the IEEE/CVF Winter Conference on Applications of Computer Vision 2026 (WACV 2026)
【45】The Art of Storytelling in Authoritarian Regimes: Crafting State Narratives on Chinese Social Media
标题：威权政权中的讲故事艺术：在中国社交媒体上制作国家叙事
链接：https://arxiv.org/abs/2512.11875
【46】TopicProphet: Prophesies on Temporal Topic Trends and Stocks
标题：TopicProphet：关于时间话题趋势和股票的疑问
链接：https://arxiv.org/abs/2512.11857
【47】Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry
标题：实现大致对称比精确对称更容易
链接：https://arxiv.org/abs/2512.11855
备注：32 pages, 2 figures
【48】Spiking Manifesto
标题：尖峰宣言
链接：https://arxiv.org/abs/2512.11843
备注：This is a declaration of principles and roadmap for spiking networks, intended as a manifesto rather than a conventional research article
【49】On the Design of One-step Diffusion via Shortcutting Flow Paths
标题：捷径一步扩散的设计
链接：https://arxiv.org/abs/2512.11831
备注：10 pages of main body, conference paper
【50】A Neural-preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions
标题：混合Dirichlet和Neumann边界条件的神经预处理Poisson求解器
链接：https://arxiv.org/abs/2310.00177
【51】Universality of high-dimensional scaling limits of stochastic gradient descent
标题：随机梯度下降的多维标度极限的普适性
链接：https://arxiv.org/abs/2512.13634
备注：30 pages
【52】Better LMO-based Momentum Methods with Second-Order Information
标题：具有二阶信息的更好的基于LMO的动量方法
链接：https://arxiv.org/abs/2512.13227
【53】Rethinking Physics-Informed Regression Beyond Training Loops and Bespoke Architectures
标题：重新思考超越训练循环和定制架构的基于物理的回归
链接：https://arxiv.org/abs/2512.13217
【54】MicroPhaseNO: Adapting an Earthquake-Trained Phase Neural Operator for Microseismic Phase Picking
标题：MicroPhaseNO：调整地震训练的相神经操作器进行微地震相拾取
链接：https://arxiv.org/abs/2512.13197
备注：Submitted to Pure and Applied Geophysics
【55】Stopping Rules for Stochastic Gradient Descent via Anytime-Valid Confidence Sequences
标题：通过随时有效置信序列的随机梯度下降停止规则
链接：https://arxiv.org/abs/2512.13123
【56】PAC-Bayes Bounds for Multivariate Linear Regression and Linear Autoencoders
标题：多元线性回归和线性自动编码器的PAC-Bayes界限
链接：https://arxiv.org/abs/2512.12905
备注：Accepted at NeurIPS 2025 (https://openreview.net/forum?id=S1zkFSby8G)
【57】Robust Variational Bayes by Min-Max Median Aggregation
标题：基于最小-最大中位数聚集的鲁棒变分Bayes
链接：https://arxiv.org/abs/2512.12676
备注：34 pages, 11 figures
【58】Mind the Jumps: A Scalable Robust Local Gaussian Process for Multidimensional Response Surfaces with Discontinuities
标题：注意跳跃：具有不连续性的多维响应表面的可扩展鲁棒局部高斯过程
链接：https://arxiv.org/abs/2512.12574
备注：25 pages, 4 figures
【59】Efficient Level-Crossing Probability Calculation for Gaussian Process Modeled Data
标题：高斯过程模型数据的高效水平交叉概率计算
链接：https://arxiv.org/abs/2512.12442
备注：IEEE PacificVis 2024
【60】Data-driven modelling of autonomous and forced dynamical systems
标题：自主和强制动态系统的数据驱动建模
链接：https://arxiv.org/abs/2512.12432
备注：Keywords: Invariant foliation, Invariant manifold, Reduced order model, Instantaneous frequency, Tensor approximation, Machine learning, JEPA
【61】Semantic search for 100M+ galaxy images using AI-generated captions
标题：使用人工智能生成的字幕对1亿多个星系图像进行语义搜索
链接：https://arxiv.org/abs/2512.11982
备注：Presented at the NeurIPS 2025 AI4Science Workshop
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