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cs.LG 方向，今日共计92篇
大模型相关(9篇)
【1】Semiparametric Preference Optimization: Your Language Model is Secretly a Single-Index Model
标题：半参数偏好优化：您的语言模型秘密地是一个单指数模型
链接：https://arxiv.org/abs/2512.21917
摘要：将大型语言模型与偏好数据对齐通常通过假设观察到的偏好的分布与未观察到的奖励之间的已知链接函数（例如，后勤联系，如布拉德利-特里）。然而，如果这种联系是错误的，那么推断出的奖励可能是有偏见的，政策也可能是不一致的。我们研究政策调整的偏好下一个未知的和不受限制的链接。我们考虑一个$f$-发散约束的奖励最大化问题，并表明，在一个政策类的解决方案的可实现性意味着一个半参数单指标二元选择模型，其中一个标量值的指数确定的政策捕获依赖于演示和其余的偏好分布是一个不受限制的函数。我们不像计量经济学那样专注于估计指数中可识别的有限维结构参数，而是专注于政策学习，专注于最优政策的误差，并允许不可识别和非参数指数。我们开发了各种政策学习器的基础上分析的链接函数，正交化的链接函数，并使用链接不可知的二分排名目标。我们分析这些，并提供有限样本的政策误差范围，依赖于通用的功能复杂性指标的索引类。我们进一步考虑使用适合神经网络和批量数据的一阶优化的实际实现。所得到的方法是鲁棒的未知偏好噪声分布和规模，同时保持直接优化的政策，而不显式拟合奖励。
摘要：Aligning large language models to preference data is commonly implemented by assuming a known link function between the distribution of observed preferences and the unobserved rewards (e.g., a logistic link as in Bradley-Terry). If the link is wrong, however, inferred rewards can be biased and policies be misaligned. We study policy alignment to preferences under an unknown and unrestricted link. We consider an $f$-divergence-constrained reward maximization problem and show that realizability of the solution in a policy class implies a semiparametric single-index binary choice model, where a scalar-valued index determined by a policy captures the dependence on demonstrations and the rest of the preference distribution is an unrestricted function thereof. Rather than focus on estimation of identifiable finite-dimensional structural parameters in the index as in econometrics, we focus on policy learning, focusing on error to the optimal policy and allowing unidentifiable and nonparametric indices. We develop a variety of policy learners based on profiling the link function, orthogonalizing the link function, and using link-agnostic bipartite ranking objectives. We analyze these and provide finite-sample policy error bounds that depend on generic functional complexity measures of the index class. We further consider practical implementations using first-order optimization suited to neural networks and batched data. The resulting methods are robust to unknown preference noise distribution and scale, while preserving the direct optimization of policies without explicitly fitting rewards.
【2】Exploring the Heterogeneity of Tabular Data: A Diversity-aware Data Generator via LLMs
标题：探索表格数据的异类：通过LLM的多样性感知数据生成器
链接：https://arxiv.org/abs/2512.21915
备注：This manuscript has been submitted to IEEE Transactions on Knowledge and Data Engineering (TKDE) for peer review
摘要：表格数据生成对于实现强大的机器学习应用程序变得越来越重要，这些应用程序需要大规模，高质量的数据。现有的解决方案利用生成模型来学习原始数据分布。然而，现实世界中的数据自然是异构的，具有不同的分布，这使得它具有挑战性，以获得一个通用的良好的模型，为不同的数据生成。为了解决这一限制，我们引入了多样性感知表格数据gEnerator（DATE），这是一个框架，它（i）通过有效地将原始异构数据划分为多个不同的子集，为上下文学习准备高质量且分布不同的示例;（ii）利用大型语言模型（LLM）来探索分区分布的多样性，并以决策树推理作为反馈，为每个子集生成高质量的标记数据。然而，大量生成的数据本质上涉及多样性和质量之间的权衡。为了解决这个问题，现有的解决方案会严格选择验证最佳的数据。然而，我们证明了在异构环境中的选择不具有贪婪选择属性，并设计了一个多臂Bandit为基础的采样算法，平衡的多样性和生成的数据的质量。在表格分类和回归基准上进行的大量实验表明，DATE始终优于最先进的基于GAN和基于LLM的方法。平均而言，DATE仅生成100个数据就可以将错误率降低23.75%。经验上，我们证明，由日期生成的数据可以提高直接偏好优化（DPO）的准确性，并提高目标数据的LLM的推理能力。代码可在https://github.com/windblow32/DATE上获得。
摘要：Tabular data generation has become increasingly essential for enabling robust machine learning applications, which require large-scale, high-quality data. Existing solutions leverage generative models to learn original data distributions. However, real-world data are naturally heterogeneous with diverse distributions, making it challenging to obtain a universally good model for diverse data generation. To address this limitation, we introduce Diversity-Aware Tabular data gEnerator (DATE), a framework that (i) prepares high-quality and distributionally distinct examples for in-context learning by effectively partitioning the original heterogeneous data into multiple diverse subsets; (ii) harnesses Large Language Models (LLMs) to explore the diversity of the partitioned distribution with decision tree reasoning as feedback, generating high-quality labeled data for each subset. However, the massive generated data inherently involves a trade-off between diversity and quality. To integrate this issue, existing solutions greedily select the validation-best data. However, we prove that the selection in heterogeneous settings does not possess the greedy-choice property, and design a Multi-Arm Bandit-based sampling algorithm that balances the diversity and quality of generated data. Extensive experiments on tabular classification and regression benchmarks demonstrate that DATE consistently outperforms state-of-the-art GAN-based and LLM-based methods. On average, DATE achieves a 23.75% reduction in error rate with just 100 generated data. Empirically, we demonstrate that data generated by DATE can improve the accuracy of Direct Preference Optimization (DPO) and enhance the reasoning capability of LLMs on the target data. Code is available at https://github.com/windblow32/DATE.
【3】A Comedy of Estimators: On KL Regularization in RL Training of LLMs
标题：估计量的喜剧：LLM RL训练中的KL正则化
链接：https://arxiv.org/abs/2512.21852
摘要：大型语言模型（LLM）的推理性能可以通过强化学习（RL）来训练它们。LLM训练的RL目标涉及正则化项，其是训练策略和参考策略之间的反向Kullback-Leibler（KL）发散。由于精确计算KL散度是棘手的，因此在实践中使用各种估计器来从策略样本中估计它。尽管它被广泛采用，包括在几个开源库中，但没有系统的研究分析将KL估计量纳入目标的多种方法及其对RL训练模型下游性能的影响。最近的工作表明，普遍的做法，纳入KL正规化不提供正确的梯度所述的目标，创建目标和其实现之间的差异。在本文中，我们进一步分析这些做法，并研究几种估计配置的梯度，揭示设计选择如何形成梯度偏差。我们通过RL微调\texttt{Qwen2.5- 7 B}、\texttt{Llama-3.1-8B-Instruct}和\texttt{Qwen 3 - 4 B-Instruct-2507}的不同配置，并评估它们在分布内和分布外任务上的表现，用实证观察证实了这些发现。通过我们的分析，我们观察到，在策略设置中：（1）具有有偏梯度的估计器配置可能导致训练不稳定性;（2）使用导致无偏梯度的估计器配置可以在域内和域外任务上获得更好的性能。我们还研究了在非策略设置中不同KL配置所导致的性能，并观察到KL正则化可以帮助稳定异步设置所导致的非策略RL训练。
摘要 ：The reasoning performance of large language models (LLMs) can be substantially improved by training them with reinforcement learning (RL). The RL objective for LLM training involves a regularization term, which is the reverse Kullback-Leibler (KL) divergence between the trained policy and the reference policy. Since computing the KL divergence exactly is intractable, various estimators are used in practice to estimate it from on-policy samples. Despite its wide adoption, including in several open-source libraries, there is no systematic study analyzing the numerous ways of incorporating KL estimators in the objective and their effect on the downstream performance of RL-trained models. Recent works show that prevailing practices for incorporating KL regularization do not provide correct gradients for stated objectives, creating a discrepancy between the objective and its implementation. In this paper, we further analyze these practices and study the gradients of several estimators configurations, revealing how design choices shape gradient bias. We substantiate these findings with empirical observations by RL fine-tuning \texttt{Qwen2.5-7B}, \texttt{Llama-3.1-8B-Instruct} and \texttt{Qwen3-4B-Instruct-2507} with different configurations and evaluating their performance on both in- and out-of-distribution tasks. Through our analysis, we observe that, in on-policy settings: (1) estimator configurations with biased gradients can result in training instabilities; and (2) using estimator configurations resulting in unbiased gradients leads to better performance on in-domain as well as out-of-domain tasks. We also investigate the performance resulting from different KL configurations in off-policy settings and observe that KL regularization can help stabilize off-policy RL training resulting from asynchronous setups.
【4】Few Tokens Matter: Entropy Guided Attacks on Vision-Language Models
标题：很少有令牌重要：对视觉语言模型的熵引导攻击
链接：https://arxiv.org/abs/2512.21815
备注：19 Pages,11 figures,8 tables
摘要：视觉语言模型（VLM）具有出色的性能，但仍然容易受到对抗性攻击。熵是模型不确定性的度量，与VLM的可靠性密切相关。先前的基于熵的攻击在所有解码步骤中最大化不确定性，隐含地假设每个令牌对生成不稳定性的贡献相等。相反，我们表明，一小部分（约20%）的高熵令牌，即，自回归生成中的关键决策点，不成比例地控制着输出轨迹。通过将对抗性扰动集中在这些位置上，我们实现了与全局方法相当的语义退化，同时使用了更小的预算。更重要的是，在多个代表性的VLM中，这种选择性攻击将35-49%的良性输出转换为有害输出，从而暴露出更严重的安全风险。值得注意的是，这些易受攻击的高熵分叉在不同架构的VLM中重复出现，从而实现了可行的可转移性（对不可见目标的有害率为17-26%）。受这些发现的启发，我们提出了熵库引导的对抗攻击（EGA），它实现了竞争性攻击成功率（93-95%）以及高有害转换，从而揭示了当前VLM安全机制的新弱点。
摘要：Vision-language models (VLMs) achieve remarkable performance but remain vulnerable to adversarial attacks. Entropy, a measure of model uncertainty, is strongly correlated with the reliability of VLM. Prior entropy-based attacks maximize uncertainty at all decoding steps, implicitly assuming that every token contributes equally to generation instability. We show instead that a small fraction (about 20%) of high-entropy tokens, i.e., critical decision points in autoregressive generation, disproportionately governs output trajectories. By concentrating adversarial perturbations on these positions, we achieve semantic degradation comparable to global methods while using substantially smaller budgets. More importantly, across multiple representative VLMs, such selective attacks convert 35-49% of benign outputs into harmful ones, exposing a more critical safety risk. Remarkably, these vulnerable high-entropy forks recur across architecturally diverse VLMs, enabling feasible transferability (17-26% harmful rates on unseen targets). Motivated by these findings, we propose Entropy-bank Guided Adversarial attacks (EGA), which achieves competitive attack success rates (93-95%) alongside high harmful conversion, thereby revealing new weaknesses in current VLM safety mechanisms.
【5】Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models
标题：重新思考大型语言模型的1位训练后量化的输出对齐
链接：https://arxiv.org/abs/2512.21651
摘要：大型语言模型（LLM）在各种NLP任务中提供了强大的性能，但其庞大的规模阻碍了在资源受限的设备上的部署。为了减少它们的计算和存储负担，已经提出了各种压缩技术，包括量化、修剪和知识蒸馏。其中，后训练量化（PTQ）因其效率而被广泛采用，因为它不需要再训练，只需要一个小的数据集进行校准，从而实现低成本部署。训练后量化的最新进展表明，即使是亚4位方法也可以保持大部分原始模型性能。然而，将浮点权重转换为\（\pm\）1的1位量化仍然特别具有挑战性，因为与全精度模型相比，现有的1位PTQ方法通常会遭受显着的性能下降。具体来说，大多数现有的1位PTQ方法都专注于权重对齐，将全精度模型权重与量化模型的权重对齐，而不是直接对齐它们的输出。虽然输出匹配方法目标更直观，并且与量化目标一致，但在1位LLM中天真地应用它通常会导致显着的性能下降。在本文中，我们调查为什么以及在什么条件下输出匹配失败，在1位LLM量化的上下文中。基于我们的研究结果，我们提出了一种新的数据感知PTQ方法，用于1位LLM，该方法在保持优化效率的同时明确考虑了激活误差累积。实证实验表明，我们的解决方案始终优于现有的1位PTQ方法，以最小的开销。
摘要：Large Language Models (LLMs) deliver strong performance across a wide range of NLP tasks, but their massive sizes hinder deployment on resource-constrained devices. To reduce their computational and memory burden, various compression techniques have been proposed, including quantization, pruning, and knowledge distillation. Among these, post-training quantization (PTQ) is widely adopted for its efficiency, as it requires no retraining and only a small dataset for calibration, enabling low-cost deployment. Recent advances for post-training quantization have demonstrated that even sub-4-bit methods can maintain most of the original model performance. However, 1-bit quantization that converts floating-point weights to \(\pm\)1, remains particularly challenging, as existing 1-bit PTQ methods often suffer from significant performance degradation compared to the full-precision models. Specifically, most of existing 1-bit PTQ approaches focus on weight alignment, aligning the full-precision model weights with those of the quantized models, rather than directly aligning their outputs. Although the output-matching approach objective is more intuitive and aligns with the quantization goal, naively applying it in 1-bit LLMs often leads to notable performance degradation. In this paper, we investigate why and under what conditions output-matching fails, in the context of 1-bit LLM quantization. Based on our findings, we propose a novel data-aware PTQ approach for 1-bit LLMs that explicitly accounts for activation error accumulation while keeping optimization efficient. Empirical experiments demonstrate that our solution consistently outperforms existing 1-bit PTQ methods with minimal overhead.
【6】nncase: An End-to-End Compiler for Efficient LLM Deployment on Heterogeneous Storage Architectures
标题：nncase：一款端到端整合器，用于在异类存储架构上高效部署LLM
链接：https://arxiv.org/abs/2512.21571
摘要：大型语言模型（LLM）的有效部署受到内存架构异构性的阻碍，传统编译器遭受碎片化的工作流程和高昂的适应成本。我们提出了nncase，一个开源的，端到端的编译框架，旨在统一优化不同的目标。nncase的核心是一个基于e图的术语重写引擎，它可以缓解阶段排序问题，从而实现计算和数据移动策略的全局探索。该框架集成了三个关键模块：Auto Vectorize用于适应异构计算单元，Auto Distribution用于搜索具有成本感知通信优化的并行策略，Auto Schedule用于最大化片上缓存位置。此外，缓冲区感知的Codegen阶段确保了高效的内核实例化。评估表明，nncase在Qwen 3系列模型上的性能优于MLC LLM和Intel IPEX等主流框架，并在CPU上实现了与手工优化的llama.cpp相当的性能，证明了自动编译对于高性能LLM部署的可行性。源代码可在https://github.com/kendryte/nncase上获得。
摘要 ：The efficient deployment of large language models (LLMs) is hindered by memory architecture heterogeneity, where traditional compilers suffer from fragmented workflows and high adaptation costs. We present nncase, an open-source, end-to-end compilation framework designed to unify optimization across diverse targets. Central to nncase is an e-graph-based term rewriting engine that mitigates the phase ordering problem, enabling global exploration of computation and data movement strategies. The framework integrates three key modules: Auto Vectorize for adapting to heterogeneous computing units, Auto Distribution for searching parallel strategies with cost-aware communication optimization, and Auto Schedule for maximizing on-chip cache locality. Furthermore, a buffer-aware Codegen phase ensures efficient kernel instantiation. Evaluations show that nncase outperforms mainstream frameworks like MLC LLM and Intel IPEX on Qwen3 series models and achieves performance comparable to the hand-optimized llama.cpp on CPUs, demonstrating the viability of automated compilation for high-performance LLM deployment. The source code is available at https://github.com/kendryte/nncase.
【7】MotionTeller: Multi-modal Integration of Wearable Time-Series with LLMs for Health and Behavioral Understanding
标题：MotionTeller：可穿戴时间序列与LLM的多模式集成，以促进健康和行为理解
链接：https://arxiv.org/abs/2512.21506
摘要：随着可穿戴传感变得越来越普遍，一个关键的挑战仍然存在：我们如何从原始生理信号（如通过加速度计收集的分钟级运动数据）中生成自然语言摘要？在这项工作中，我们引入了MotionTeller，这是一个生成框架，可以将分钟级可穿戴活动数据与大型语言模型（LLM）进行本地集成。MotionTeller将预训练的体动记录编码器与轻量级投影模块相结合，该模块将行为嵌入映射到冻结的仅解码器LLM的令牌空间中，从而实现每日行为摘要的自由文本自回归生成。我们构建了一个新的数据集的54383（体动记录，文本）对来自现实世界的NHANES记录，和训练模型使用交叉熵损失与监督只对语言令牌。MotionTeller实现了较高的语义保真度（BERTScore-F1 = 0.924）和词汇准确性（ROUGE-1 = 0.722），在ROUGE-1中比基于语义的基线高出7%。到epoch 15时，平均训练损失收敛到0.38，表明稳定优化。定性分析证实，MotionTeller捕捉昼夜节律结构和行为转变，而PCA图显示嵌入空间训练后的聚类对齐增强。总之，这些结果将MotionTeller定位为一个可扩展的，可解释的系统，用于将可穿戴传感器数据转换为流畅的，以人为本的描述，为行为监测，临床审查和个性化健康干预引入新的途径。
摘要：As wearable sensing becomes increasingly pervasive, a key challenge remains: how can we generate natural language summaries from raw physiological signals such as actigraphy - minute-level movement data collected via accelerometers? In this work, we introduce MotionTeller, a generative framework that natively integrates minute-level wearable activity data with large language models (LLMs). MotionTeller combines a pretrained actigraphy encoder with a lightweight projection module that maps behavioral embeddings into the token space of a frozen decoder-only LLM, enabling free-text, autoregressive generation of daily behavioral summaries. We construct a novel dataset of 54383 (actigraphy, text) pairs derived from real-world NHANES recordings, and train the model using cross-entropy loss with supervision only on the language tokens. MotionTeller achieves high semantic fidelity (BERTScore-F1 = 0.924) and lexical accuracy (ROUGE-1 = 0.722), outperforming prompt-based baselines by 7 percent in ROUGE-1. The average training loss converges to 0.38 by epoch 15, indicating stable optimization. Qualitative analysis confirms that MotionTeller captures circadian structure and behavioral transitions, while PCA plots reveal enhanced cluster alignment in embedding space post-training. Together, these results position MotionTeller as a scalable, interpretable system for transforming wearable sensor data into fluent, human-centered descriptions, introducing new pathways for behavioral monitoring, clinical review, and personalized health interventions.
【8】dUltra: Ultra-Fast Diffusion Language Models via Reinforcement Learning
标题：dUltra：通过强化学习的超快扩散语言模型
链接：https://arxiv.org/abs/2512.21446
摘要：掩蔽扩散语言模型（MDLM）提供了并行令牌生成的潜力，但大多数开源MDLM解码少于5个令牌每个模型前向传递，即使使用复杂的采样策略。因此，它们的采样速度通常与AR +推测解码方案相当，限制了它们相对于主流自回归方法的优势。现有的基于蒸馏的加速器（dParallel，d3 LLM）在基础模型生成的轨迹上微调MDLM，这可能在微调期间变得偏离策略，并将性能限制在基础模型样本的质量上。我们提出了一个基于组相对策略优化（GRPO）的策略强化学习框架，它可以学习有效并行解码的解蔽策略。dUltra引入了一个解蔽规划器头，它可以预测独立伯努利分布下的每个令牌解蔽可能性。我们共同优化的基础扩散LLM和解蔽顺序规划器使用奖励信号相结合的可验证的奖励，蒸馏奖励，和解蔽步骤的数量。在数学推理和代码生成任务中，dUltra提高了最先进的启发式和蒸馏基线的准确性-效率权衡，朝着实现自回归模型的“扩散至上”迈进。
摘要：Masked diffusion language models (MDLMs) offer the potential for parallel token generation, but most open-source MDLMs decode fewer than 5 tokens per model forward pass even with sophisticated sampling strategies. As a result, their sampling speeds are often comparable to AR + speculative decoding schemes, limiting their advantage over mainstream autoregressive approaches. Existing distillation-based accelerators (dParallel, d3LLM) finetune MDLMs on trajectories generated by a base model, which can become off-policy during finetuning and restrict performance to the quality of the base model's samples. We propose \texttt{dUltra}, an on-policy reinforcement learning framework based on Group Relative Policy Optimization (GRPO) that learns unmasking strategies for efficient parallel decoding. dUltra introduces an unmasking planner head that predicts per-token unmasking likelihoods under independent Bernoulli distributions. We jointly optimize the base diffusion LLM and the unmasking order planner using reward signals combining verifiable reward, distillation reward, and the number of unmasking steps. Across mathematical reasoning and code generation tasks, dUltra improves the accuracy--efficiency trade-off over state-of-the-art heuristic and distillation baselines, moving towards achieving ``diffusion supremacy'' over autoregressive models.
【9】Morality is Contextual: Learning Interpretable Moral Contexts from Human Data with Probabilistic Clustering and Large Language Models
标题：道德是上下文的：利用概率集群和大型语言模型从人类数据中学习可解释的道德上下文
链接：https://arxiv.org/abs/2512.21439
备注：11 pages, 5 figures, +24 pages of Appendix
摘要：道德行为的判断不仅要看其结果，还要看其发生的背景。我们提出了COMETH（从文本人类输入的道德评价的上下文组织），一个框架，集成了概率上下文学习与基于LLM的语义抽象和人类的道德评价，以模拟上下文如何塑造模糊行为的可接受性。我们策划了一个基于经验的数据集，其中包含300个场景，涉及六个核心行动（违反“不杀人”、“不欺骗”和“不违法”），并从N=101名参与者中收集三元判断（指责/中立/支持）。预处理流水线通过LLM过滤器和具有K-means的MiniLM嵌入来处理动作，从而产生鲁棒的、可再现的核心动作集群。然后，COMETH通过使用原则性分歧标准从人类判断分布在线聚类场景来学习特定于行动的道德背景。为了概括和解释预测，泛化模块提取简洁的、非评估性的二元上下文特征，并在透明的基于似然的模型中学习特征权重。从经验上讲，COMETH与大多数人的判断相对于端到端的LLM提示（大约是两倍）。60% vs.约平均30%），同时揭示哪些上下文特征驱动其预测。这些捐款包括：（i）基于经验的道德背景数据集，（ii）将人类判断与基于模型的上下文学习和LLM语义相结合的可再现管道，以及（iii）端到端LLM的可解释替代方案，用于上下文敏感的道德预测和解释。
摘要 ：Moral actions are judged not only by their outcomes but by the context in which they occur. We present COMETH (Contextual Organization of Moral Evaluation from Textual Human inputs), a framework that integrates a probabilistic context learner with LLM-based semantic abstraction and human moral evaluations to model how context shapes the acceptability of ambiguous actions. We curate an empirically grounded dataset of 300 scenarios across six core actions (violating Do not kill, Do not deceive, and Do not break the law) and collect ternary judgments (Blame/Neutral/Support) from N=101 participants. A preprocessing pipeline standardizes actions via an LLM filter and MiniLM embeddings with K-means, producing robust, reproducible core-action clusters. COMETH then learns action-specific moral contexts by clustering scenarios online from human judgment distributions using principled divergence criteria. To generalize and explain predictions, a Generalization module extracts concise, non-evaluative binary contextual features and learns feature weights in a transparent likelihood-based model. Empirically, COMETH roughly doubles alignment with majority human judgments relative to end-to-end LLM prompting (approx. 60% vs. approx. 30% on average), while revealing which contextual features drive its predictions. The contributions are: (i) an empirically grounded moral-context dataset, (ii) a reproducible pipeline combining human judgments with model-based context learning and LLM semantics, and (iii) an interpretable alternative to end-to-end LLMs for context-sensitive moral prediction and explanation.
Graph相关(图学习|图神经网络|图优化等)(2篇)
【1】AnchorGK: Anchor-based Incremental and Stratified Graph Learning Framework for Inductive Spatio-Temporal Kriging
标题：AnchorGK：基于锚点的增量和分层图学习框架，用于归纳时空克里金
链接：https://arxiv.org/abs/2512.21569
摘要：时空克里格法是传感器网络中的一个基本问题，由部署的传感器的稀疏性和由此产生的缺失观测驱动。尽管最近的方法对空间和时间相关性进行建模，但它们通常没有充分利用实际部署的两个实际特性：位置的稀疏空间分布和跨位置的辅助功能的异构可用性。为了解决这些挑战，我们提出了AnchorGK，一个基于锚的增量和分层图学习框架，用于感应时空克里金。AnchorGK引入了锚点位置，以原则性的方式对数据进行分层。根据特征可用性构造锚定物，然后在这些锚定物周围形成地层。这种分层起到两个互补的作用。首先，它显式地表示并不断更新图学习框架内未观察到的区域与周围观察到的位置之间的相关性。其次，它通过增量表示机制系统地使用所有可用的分层特征，在不丢弃信息信号的情况下减轻特征不完整性。在分层结构的基础上，我们设计了一个双视图图学习层，它联合聚合特征相关和位置相关的信息，学习特定于层的表示，支持归纳设置下的准确推理。在多个基准数据集上进行的大量实验表明，AnchorGK始终优于最先进的时空克里金基线。
摘要：Spatio-temporal kriging is a fundamental problem in sensor networks, driven by the sparsity of deployed sensors and the resulting missing observations. Although recent approaches model spatial and temporal correlations, they often under-exploit two practical characteristics of real deployments: the sparse spatial distribution of locations and the heterogeneous availability of auxiliary features across locations. To address these challenges, we propose AnchorGK, an Anchor-based Incremental and Stratified Graph Learning framework for inductive spatio-temporal kriging. AnchorGK introduces anchor locations to stratify the data in a principled manner. Anchors are constructed according to feature availability, and strata are then formed around these anchors. This stratification serves two complementary roles. First, it explicitly represents and continuously updates correlations between unobserved regions and surrounding observed locations within a graph learning framework. Second, it enables the systematic use of all available features across strata via an incremental representation mechanism, mitigating feature incompleteness without discarding informative signals. Building on the stratified structure, we design a dual-view graph learning layer that jointly aggregates feature-relevant and location-relevant information, learning stratum-specific representations that support accurate inference under inductive settings. Extensive experiments on multiple benchmark datasets demonstrate that AnchorGK consistently outperforms state-of-the-art baselines for spatio-temporal kriging.
【2】Global-Graph Guided and Local-Graph Weighted Contrastive Learning for Unified Clustering on Incomplete and Noise Multi-View Data
标题：全球图引导和局部图加权对比学习，用于不完整和有噪多视图数据的统一集群
链接：https://arxiv.org/abs/2512.21516
摘要：近年来，对比学习在挖掘多视图聚类（MVC）中的互补信息方面发挥着重要作用，并受到越来越多的关注。然而，现实世界的多视图数据遭受数据不完整或噪声，导致罕见配对的样本或错配对的样本，这显著挑战基于CL的MVC的有效性。也就是说，稀疏配对问题阻碍了MVC提取足够的多视图互补信息，而错误配对问题导致对比学习在错误的方向上优化模型。为了解决这些问题，我们提出了一个统一的CL为基础的MVC框架，以提高不完整和噪声多视图数据的聚类效果。首先，为了克服稀有配对问题，我们设计了一个全局图引导的对比学习，其中所有视图样本构建一个全局视图亲和图，以形成新的样本对，以充分探索互补信息。其次，为了减轻错误配对问题，我们提出了一种局部图加权对比学习，它利用局部邻居来生成成对权重，以自适应地增强或减弱成对对比学习。我们的方法是无估算的，可以集成到一个统一的全局-局部图引导的对比学习框架。在多视图数据的不完整和噪声设置上进行的大量实验表明，与最先进的方法相比，我们的方法具有更好的性能。
摘要：Recently, contrastive learning (CL) plays an important role in exploring complementary information for multi-view clustering (MVC) and has attracted increasing attention. Nevertheless, real-world multi-view data suffer from data incompleteness or noise, resulting in rare-paired samples or mis-paired samples which significantly challenges the effectiveness of CL-based MVC. That is, rare-paired issue prevents MVC from extracting sufficient multi-view complementary information, and mis-paired issue causes contrastive learning to optimize the model in the wrong direction. To address these issues, we propose a unified CL-based MVC framework for enhancing clustering effectiveness on incomplete and noise multi-view data. First, to overcome the rare-paired issue, we design a global-graph guided contrastive learning, where all view samples construct a global-view affinity graph to form new sample pairs for fully exploring complementary information. Second, to mitigate the mis-paired issue, we propose a local-graph weighted contrastive learning, which leverages local neighbors to generate pair-wise weights to adaptively strength or weaken the pair-wise contrastive learning. Our method is imputation-free and can be integrated into a unified global-local graph-guided contrastive learning framework. Extensive experiments on both incomplete and noise settings of multi-view data demonstrate that our method achieves superior performance compared with state-of-the-art approaches.
Transformer(1篇)
【1】Unifying Learning Dynamics and Generalization in Transformers Scaling Law
标题：Transformers标度律中学习动力学与泛化的统一
链接：https://arxiv.org/abs/2512.22088
摘要：作为大型语言模型（LLM）开发的基石，缩放定律预测了随着计算资源的增加模型性能的改善。然而，虽然经验验证，其理论基础仍然知之甚少。本文将基于transformer的语言模型的学习动力学形式化为一个常微分方程（ODE）系统，然后将这个过程近似为内核行为。从之前的玩具模型分析出发，我们严格分析了随机梯度下降（SGD）训练，用于具有任意数据分布的序列到序列数据的多层Transformers，密切反映了现实世界的条件。我们的分析特征的泛化误差收敛到不可约的风险计算资源规模与数据，特别是在优化过程中。 我们建立了一个理论上的上限，超额风险的特点是一个明显的相变。在初始优化阶段，超额风险相对于计算成本呈指数衰减。然而，一旦超过特定的资源分配阈值，系统就进入统计阶段，其中泛化误差遵循$Θ（\mathsf{C}^{-1/6}）$的幂律衰减。在这个统一的框架之外，我们的理论推导出了模型大小、训练时间和数据集大小的独立比例律，阐明了每个变量如何独立地控制泛化的上限。
摘要：The scaling law, a cornerstone of Large Language Model (LLM) development, predicts improvements in model performance with increasing computational resources. Yet, while empirically validated, its theoretical underpinnings remain poorly understood. This work formalizes the learning dynamics of transformer-based language models as an ordinary differential equation (ODE) system, then approximates this process to kernel behaviors. Departing from prior toy-model analyses, we rigorously analyze stochastic gradient descent (SGD) training for multi-layer transformers on sequence-to-sequence data with arbitrary data distribution, closely mirroring real-world conditions. Our analysis characterizes the convergence of generalization error to the irreducible risk as computational resources scale with data, especially during the optimization process. We establish a theoretical upper bound on excess risk characterized by a distinct phase transition. In the initial optimization phase, the excess risk decays exponentially relative to the computational cost ${\sf C}$. However, once a specific resource allocation threshold is crossed, the system enters a statistical phase, where the generalization error follows a power-law decay of $Θ(\mathsf{C}^{-1/6})$. Beyond this unified framework, our theory derives isolated scaling laws for model size, training time, and dataset size, elucidating how each variable independently governs the upper bounds of generalization.
GAN|对抗|攻击|生成相关(9篇)
【1】A2P-Vis: an Analyzer-to-Presenter Agentic Pipeline for Visual Insights Generation and Reporting
标题：A2 P-Vis：用于视觉洞察生成和报告的分析师到演示者的统计管道
链接：https://arxiv.org/abs/2512.22101
备注：3 pages, 3 figures; Accepted by 1st Workshop on GenAI, Agents and the Future of VIS as Mini-challenge paper and win the Honorable Mention award. Submit number is 7597 and the paper is archived on the workshop website: https://visxgenai.github.io/subs-2025/7597/7597-doc.pdf
摘要：使用人工智能代理自动化端到端数据科学管道仍然存在两个差距：生成有洞察力的、多样化的视觉证据，并将其组装成一个连贯的、专业的报告。我们提出了A2 P-Vis，这是一个由两部分组成的多代理管道，可以将原始数据集转化为高质量的数据可视化报告。数据分析器协调分析，提出不同的可视化方向，生成和执行绘图代码，使用易读性检查器过滤低质量的图形，并自动为深度，正确性，特异性，深度和可操作性评分的候选见解。然后，演示者对主题进行排序，根据排名靠前的见解撰写图表式叙述，编写合理的过渡，并修改文档以确保清晰和一致性，从而生成一份连贯的、可供发布的报告。总之，这些代理将原始数据转换为策划材料（图表+经过审查的见解），并转换为可读的叙述，而无需手动粘合工作。我们声称，通过将有质量保证的分析器与叙事演示者相结合，A2 P-Vis实现了端到端的协同分析，提高了从业者自动数据分析的真实有用性。有关完整的数据集报告，请参阅：https://www.visagent.org/api/output/f2a3486d-2c3b-4825-98d4-5af25a819f56。
摘要：Automating end-to-end data science pipeline with AI agents still stalls on two gaps: generating insightful, diverse visual evidence and assembling it into a coherent, professional report. We present A2P-Vis, a two-part, multi-agent pipeline that turns raw datasets into a high-quality data-visualization report. The Data Analyzer orchestrates profiling, proposes diverse visualization directions, generates and executes plotting code, filters low-quality figures with a legibility checker, and elicits candidate insights that are automatically scored for depth, correctness, specificity, depth and actionability. The Presenter then orders topics, composes chart-grounded narratives from the top-ranked insights, writes justified transitions, and revises the document for clarity and consistency, yielding a coherent, publication-ready report. Together, these agents convert raw data into curated materials (charts + vetted insights) and into a readable narrative without manual glue work. We claim that by coupling a quality-assured Analyzer with a narrative Presenter, A2P-Vis operationalizes co-analysis end-to-end, improving the real-world usefulness of automated data analysis for practitioners. For the complete dataset report, please see: https://www.visagent.org/api/output/f2a3486d-2c3b-4825-98d4-5af25a819f56.
【2】Scaling Adversarial Training via Data Selection
标题：通过数据选择扩展对抗训练
链接：https://arxiv.org/abs/2512.22069
备注：6 pages. Conference workshop paper
摘要：投影梯度下降（PGD）是一种强大且广泛使用的一阶对抗攻击，但其计算成本很低，因为所有训练样本都经过相同的迭代内环优化，尽管对鲁棒性的贡献不一样。出于这种低效率的动机，我们提出了\n {选择性对抗训练}，它只扰动每个小批量中的关键样本的子集。具体来说，我们引入了两个原则性的选择标准：（1）基于边缘的采样，它优先考虑决策边界附近的样本，以及（2）梯度匹配采样，它选择梯度与主要批次优化方向一致的样本。对抗性示例仅针对所选子集生成，而其余样本则使用混合目标进行干净训练。在MNIST和CIFAR-10上的实验表明，所提出的方法实现了与完全PGD对抗训练相当甚至超过完全PGD对抗训练的鲁棒性，同时将对抗计算减少了高达50美元，表明明智的样本选择足以实现可扩展的对抗鲁棒性。
摘要：Projected Gradient Descent (PGD) is a strong and widely used first-order adversarial attack, yet its computational cost scales poorly, as all training samples undergo identical iterative inner-loop optimization despite contributing unequally to robustness. Motivated by this inefficiency, we propose \emph{Selective Adversarial Training}, which perturbs only a subset of critical samples in each minibatch. Specifically, we introduce two principled selection criteria: (1) margin-based sampling, which prioritizes samples near the decision boundary, and (2) gradient-matching sampling, which selects samples whose gradients align with the dominant batch optimization direction. Adversarial examples are generated only for the selected subset, while the remaining samples are trained cleanly using a mixed objective. Experiments on MNIST and CIFAR-10 show that the proposed methods achieve robustness comparable to, or even exceeding, full PGD adversarial training, while reducing adversarial computation by up to $50\%$, demonstrating that informed sample selection is sufficient for scalable adversarial robustness.
【3】From In Silico to In Vitro: Evaluating Molecule Generative Models for Hit Generation
标题：从模拟到体外：评估热门一代的分子生成模型
链接：https://arxiv.org/abs/2512.22031
摘要：命中识别是药物发现管道中的关键但资源密集型步骤，传统上依赖于大型化合物库的高通量筛选。尽管在虚拟筛选方面取得了进展，但这些方法仍然耗时且昂贵。深度学习的最新进展使得能够开发能够学习复杂分子表示并从头生成新化合物的生成模型。然而，使用ML来取代整个药物发现管道是非常具有挑战性的。在这项工作中，我们更倾向于研究生成模型是否可以取代管道的一个步骤：命中样分子生成。据我们所知，这是第一项明确将命中样分子生成作为一项独立任务的研究，并根据经验测试生成模型是否可以直接支持药物发现管道的这一阶段。具体来说，我们研究了这些模型是否可以被训练来生成命中样分子，从而能够直接并入甚至取代传统的命中识别工作流程。我们提出了一个评估框架，专门针对这一任务，整合了物理化学，结构和生物活性相关的标准，在一个多阶段的过滤管道，定义了命中一样的化学空间。两个自回归和一个基于扩散的生成模型在各种数据集和训练设置中进行基准测试，使用标准度量和目标特定的对接分数评估输出。我们的研究结果表明，这些模型可以产生有效的，多样化的，生物相关的化合物在多个目标，与一些选定的GSK-3$β$命中合成和确认在体外活性。我们还确定了当前评估指标和可用训练数据的关键限制。
摘要：Hit identification is a critical yet resource-intensive step in the drug discovery pipeline, traditionally relying on high-throughput screening of large compound libraries. Despite advancements in virtual screening, these methods remain time-consuming and costly. Recent progress in deep learning has enabled the development of generative models capable of learning complex molecular representations and generating novel compounds de novo. However, using ML to replace the entire drug-discovery pipeline is highly challenging. In this work, we rather investigate whether generative models can replace one step of the pipeline: hit-like molecule generation. To the best of our knowledge, this is the first study to explicitly frame hit-like molecule generation as a standalone task and empirically test whether generative models can directly support this stage of the drug discovery pipeline. Specifically, we investigate if such models can be trained to generate hit-like molecules, enabling direct incorporation into, or even substitution of, traditional hit identification workflows. We propose an evaluation framework tailored to this task, integrating physicochemical, structural, and bioactivity-related criteria within a multi-stage filtering pipeline that defines the hit-like chemical space. Two autoregressive and one diffusion-based generative models were benchmarked across various datasets and training settings, with outputs assessed using standard metrics and target-specific docking scores. Our results show that these models can generate valid, diverse, and biologically relevant compounds across multiple targets, with a few selected GSK-3$β$ hits synthesized and confirmed active in vitro. We also identify key limitations in current evaluation metrics and available training data.
【4】Look Closer! An Adversarial Parametric Editing Framework for Hallucination Mitigation in VLMs
标题：仔细看！用于缓解VLM幻觉的对抗性参数编辑框架
链接：https://arxiv.org/abs/2512.21999
摘要：虽然视觉语言模型（VLM）由于其有前途的实际应用而在AI社区中获得了越来越多的关注，但它们表现出持续的幻觉问题，产生与视觉输入不一致的输出。最近的研究将这些幻觉归因于VLM对语言先验的过度依赖和视觉特征整合不足，提出了启发式解码校准策略来减轻它们。然而，这些策略的不可训练性质固有地限制了它们的优化潜力。为此，我们提出了一个对抗性的参数编辑框架，用于VLM中的幻觉缓解，它遵循\textbf{A}ctivate-\textbf{L}ocate-\textbf{E}dit \textbf{A}dversarially范式。具体来说，我们首先构建了一个激活数据集，其中包括接地响应（积极样本专注地锚定在视觉特征）和幻觉响应（消极样本反映LLM先验偏差和内部知识伪影）。接下来，我们通过分析响应对的差分隐藏状态来识别关键的易产生幻觉的参数簇。然后，这些集群使用注入对抗性调谐前缀的提示进行微调，这些前缀被优化以最大化视觉忽略，从而迫使模型优先考虑视觉证据而不是固有的参数偏差。对生成性和辨别性VLM任务的评估表明ALEAHallu在缓解幻觉方面具有显著的有效性。我们的代码可在https://github.com/hujiayu1223/ALEAHallu上获得。
摘要：While Vision-Language Models (VLMs) have garnered increasing attention in the AI community due to their promising practical applications, they exhibit persistent hallucination issues, generating outputs misaligned with visual inputs. Recent studies attribute these hallucinations to VLMs' over-reliance on linguistic priors and insufficient visual feature integration, proposing heuristic decoding calibration strategies to mitigate them. However, the non-trainable nature of these strategies inherently limits their optimization potential. To this end, we propose an adversarial parametric editing framework for Hallucination mitigation in VLMs, which follows an \textbf{A}ctivate-\textbf{L}ocate-\textbf{E}dit \textbf{A}dversarially paradigm. Specifically, we first construct an activation dataset that comprises grounded responses (positive samples attentively anchored in visual features) and hallucinatory responses (negative samples reflecting LLM prior bias and internal knowledge artifacts). Next, we identify critical hallucination-prone parameter clusters by analyzing differential hidden states of response pairs. Then, these clusters are fine-tuned using prompts injected with adversarial tuned prefixes that are optimized to maximize visual neglect, thereby forcing the model to prioritize visual evidence over inherent parametric biases. Evaluations on both generative and discriminative VLM tasks demonstrate the significant effectiveness of ALEAHallu in alleviating hallucinations. Our code is available at https://github.com/hujiayu1223/ALEAHallu.
【5】AutoPP: Towards Automated Product Poster Generation and Optimization
标题：AutoPP：迈向自动化产品海报生成和优化
链接：https://arxiv.org/abs/2512.21921
备注：Accepted to AAAI 2026
摘要：产品海报将醒目的视觉效果与信息丰富的文字相结合，以突出产品并吸引客户的注意力。然而，制作吸引人的海报并根据在线性能手动优化它们是费力和消耗资源的。为了解决这个问题，我们引入了AutoPP，这是一种用于产品海报生成和优化的自动化管道，无需人工干预。具体来说，生成器仅依赖于基本的产品信息，首先使用统一的设计模块将海报的三个关键元素（背景，文本和布局）集成为内聚输出。然后，元素呈现模块将这些元素编码成条件令牌，高效且可控地生成产品海报。基于生成的海报，优化器通过利用在线反馈来提高其点击率（CTR）。它系统地替换元素以收集细粒度的CTR比较，并利用孤立直接偏好优化（IDPO）将CTR收益归因于孤立元素。我们的工作得到了AutoPP1M的支持，AutoPP1M是专门为产品海报生成和优化设计的最大数据集，其中包含100万张高质量的海报和从100多万用户收集的反馈。实验表明，AutoPP在离线和在线设置下都达到了最先进的结果。我们的代码和数据集可在https://github.com/JD-GenX/AutoPP上公开获取
摘要：Product posters blend striking visuals with informative text to highlight the product and capture customer attention. However, crafting appealing posters and manually optimizing them based on online performance is laborious and resource-consuming. To address this, we introduce AutoPP, an automated pipeline for product poster generation and optimization that eliminates the need for human intervention. Specifically, the generator, relying solely on basic product information, first uses a unified design module to integrate the three key elements of a poster (background, text, and layout) into a cohesive output. Then, an element rendering module encodes these elements into condition tokens, efficiently and controllably generating the product poster. Based on the generated poster, the optimizer enhances its Click-Through Rate (CTR) by leveraging online feedback. It systematically replaces elements to gather fine-grained CTR comparisons and utilizes Isolated Direct Preference Optimization (IDPO) to attribute CTR gains to isolated elements. Our work is supported by AutoPP1M, the largest dataset specifically designed for product poster generation and optimization, which contains one million high-quality posters and feedback collected from over one million users. Experiments demonstrate that AutoPP achieves state-of-the-art results in both offline and online settings. Our code and dataset are publicly available at: https://github.com/JD-GenX/AutoPP
【6】Synthetic Financial Data Generation for Enhanced Financial Modelling
标题：用于增强财务建模的合成财务数据生成
链接：https://arxiv.org/abs/2512.21791
备注：23 pages, 7 figures, 6 tables. Submitted as a preprint. This work presents a unified multi-criteria evaluation framework for synthetic financial data, applied to ARIMA-GARCH, VAEs, and TimeGAN models
摘要：金融领域的数据稀缺性和保密性往往阻碍模型开发和可靠的测试。本文提出了一个统一的综合金融数据的多标准评估框架，并将其应用于三个代表性的生成范式：统计ARIMA-GARCH基线，变分自编码器（VAE）和时间序列生成对抗网络（TimeGAN）。使用历史S和P 500每日数据，我们评估保真度（最大平均差异，MMD）、时间结构（自相关和波动率聚类）以及下游任务的实际效用，特别是均值-方差投资组合优化和波动率预测。实证结果表明，ARIMA-GAN捕捉线性趋势和条件波动，但未能再现非线性动态; VAE产生低估极端事件的平滑轨迹; TimeGAN实现了现实主义和时间一致性之间的最佳权衡（例如，TimeGAN获得最低的MMD：1.84e-3，平均超过5粒种子）。最后，我们阐明了实用的指导方针，选择生成模型，根据应用需求和计算限制。我们统一的评估协议和可复制的代码库旨在标准化合成金融数据研究中的基准测试。
摘要：Data scarcity and confidentiality in finance often impede model development and robust testing. This paper presents a unified multi-criteria evaluation framework for synthetic financial data and applies it to three representative generative paradigms: the statistical ARIMA-GARCH baseline, Variational Autoencoders (VAEs), and Time-series Generative Adversarial Networks (TimeGAN). Using historical S and P 500 daily data, we evaluate fidelity (Maximum Mean Discrepancy, MMD), temporal structure (autocorrelation and volatility clustering), and practical utility in downstream tasks, specifically mean-variance portfolio optimization and volatility forecasting. Empirical results indicate that ARIMA-GARCH captures linear trends and conditional volatility but fails to reproduce nonlinear dynamics; VAEs produce smooth trajectories that underestimate extreme events; and TimeGAN achieves the best trade-off between realism and temporal coherence (e.g., TimeGAN attained the lowest MMD: 1.84e-3, average over 5 seeds). Finally, we articulate practical guidelines for selecting generative models according to application needs and computational constraints. Our unified evaluation protocol and reproducible codebase aim to standardize benchmarking in synthetic financial data research.
【7】Dictionary-Transform Generative Adversarial Networks
标题：词典转换生成对抗网络
链接：https://arxiv.org/abs/2512.21677
摘要 ：生成对抗网络（GANs）被广泛用于分布学习，但它们的经典公式在理论上仍然很脆弱，具有不适定的目标，不稳定的训练动态和有限的可解释性。在这项工作中，我们介绍了一个完全基于模型的对抗框架，其中生成器是一个稀疏的合成字典，而生成器是一个分析变换，充当能量模型。通过将两个参与者限制为具有显式约束的线性算子，DT-GAN从根本上脱离神经GAN架构，并接受严格的理论分析。 我们证明了DT-GAN对抗博弈是适定性的，并且至少有一个纳什均衡。在稀疏生成模型下，平衡解可证明可识别到标准排列和符号模糊性，并且在合成和分析算子之间呈现精确的几何对准。我们进一步建立了经验均衡的有限样本稳定性和一致性，证明了DT-GAN训练在标准采样假设下可靠地收敛，并且在重尾制度中保持稳健。 混合结构的合成数据上的实验验证了理论预测，表明DT-GAN一致地恢复底层结构，并在标准GAN退化的相同优化预算下表现出稳定的行为。DT-GAN不是作为神经GANs的通用替代品提出的，而是作为允许稀疏合成结构的数据分布的原则性对抗替代品。结果表明，当基于经典稀疏建模时，对抗学习可以变得可解释，稳定和可证明的正确。
摘要：Generative adversarial networks (GANs) are widely used for distribution learning, yet their classical formulations remain theoretically fragile, with ill-posed objectives, unstable training dynamics, and limited interpretability. In this work, we introduce \emph{Dictionary-Transform Generative Adversarial Networks} (DT-GAN), a fully model-based adversarial framework in which the generator is a sparse synthesis dictionary and the discriminator is an analysis transform acting as an energy model. By restricting both players to linear operators with explicit constraints, DT-GAN departs fundamentally from neural GAN architectures and admits rigorous theoretical analysis. We show that the DT-GAN adversarial game is well posed and admits at least one Nash equilibrium. Under a sparse generative model, equilibrium solutions are provably identifiable up to standard permutation and sign ambiguities and exhibit a precise geometric alignment between synthesis and analysis operators. We further establish finite-sample stability and consistency of empirical equilibria, demonstrating that DT-GAN training converges reliably under standard sampling assumptions and remains robust in heavy-tailed regimes. Experiments on mixture-structured synthetic data validate the theoretical predictions, showing that DT-GAN consistently recovers underlying structure and exhibits stable behavior under identical optimization budgets where a standard GAN degrades. DT-GAN is not proposed as a universal replacement for neural GANs, but as a principled adversarial alternative for data distributions that admit sparse synthesis structure. The results demonstrate that adversarial learning can be made interpretable, stable, and provably correct when grounded in classical sparse modeling.
【8】Fuzzwise: Intelligent Initial Corpus Generation for Fuzzing
标题：Fuzzwise：Fuzzzing的智能初始数据库生成
链接：https://arxiv.org/abs/2512.21440
摘要：在基于变异的灰盒模糊中，为初始语料库生成高质量的输入种子是有效模糊的关键。我们提出了FuzzWise，它将它们集成到一个过程中，以生成最佳的初始种子语料库（ICS），而不是进行单独的阶段来生成一个大型语料库，并随后将其最小化。FuzzWise利用基于大型语言模型（LLM）的多代理框架。第一个LLM代理为目标程序生成测试用例。第二个LLM代理作为预测代码覆盖模块，评估每个生成的测试用例是否会增强当前语料库的整体覆盖率。简化的过程允许立即评估每个新生成的测试种子对整体覆盖率的贡献。FuzzWise采用了使用LLM的预测方法，消除了实际执行的需要，节省了计算资源和时间，特别是在执行不可取甚至不可能的情况下。我们的实证评估表明，FuzzWise生成的测试用例比基线方法少得多。尽管测试用例的数量较少，但FuzzWise实现了高代码覆盖率，并触发了比基线更多的运行时错误。此外，它在产生捕获更多错误的初始语料库方面具有更高的时间效率和覆盖效率。
摘要：In mutation-based greybox fuzzing, generating high-quality input seeds for the initial corpus is essential for effective fuzzing. Rather than conducting separate phases for generating a large corpus and subsequently minimizing it, we propose FuzzWise which integrates them into one process to generate the optimal initial corpus of seeds (ICS). FuzzWise leverages a multi-agent framework based on Large Language Models (LLMs). The first LLM agent generates test cases for the target program. The second LLM agent, which functions as a predictive code coverage module, assesses whether each generated test case will enhance the overall coverage of the current corpus. The streamlined process allows each newly generated test seed to be immediately evaluated for its contribution to the overall coverage. FuzzWise employs a predictive approach using an LLM and eliminates the need for actual execution, saving computational resources and time, particularly in scenarios where the execution is not desirable or even impossible. Our empirical evaluation demonstrates that FuzzWise generates significantly fewer test cases than baseline methods. Despite the lower number of test cases, FuzzWise achieves high code coverage and triggers more runtime errors compared to the baselines. Moreover, it is more time-efficient and coverage-efficient in producing an initial corpus catching more errors.
【9】A Reinforcement Learning Approach to Synthetic Data Generation
标题：合成数据生成的强化学习方法
链接：https://arxiv.org/abs/2512.21395
摘要：合成数据生成（SDG）是一种很有前途的方法，可以在生物医学研究中实现数据共享，同时保护患者隐私。然而，最先进的生成模型通常需要大型数据集和复杂的训练过程，限制了它们在小样本环境中的适用性。在这项工作中，我们将SDG重新定义为强化学习（RL）问题，并引入RLSyn，这是一种新的框架，将数据生成器建模为患者记录上的随机策略，并使用具有鉴别器衍生奖励的邻近策略优化来优化它，从而产生更稳定和更有效的数据训练。我们在两个生物医学数据集- AI-READI和MIMIC-IV上评估RLSyn，并将其与最先进的生成对抗网络（GANs）和基于扩散的方法进行基准测试，包括广泛的隐私，实用性和保真度评估。RL-Syn在MIMIC-IV上执行扩散模型，并优于GANs，同时在较小的AI-READI数据集上优于扩散模型和GANs。这些结果表明，强化学习为合成生物医学数据生成提供了一种原则性和有效的替代方案，特别是在数据稀缺的情况下。
摘要：Synthetic data generation (SDG) is a promising approach for enabling data sharing in biomedical studies while preserving patient privacy. Yet, state-of-the-art generative models often require large datasets and complex training procedures, limiting their applicability in small-sample settings. In this work, we reframe SDG as a reinforcement learning (RL) problem and introduce RLSyn, a novel framework that models the data generator as a stochastic policy over patient records and optimizes it using Proximal Policy Optimization with discriminator-derived rewards, yielding more stable and data-efficient training. We evaluate RLSyn on two biomedical datasets - AI-READI and MIMIC-IV- and benchmark it against state-of-the-art generative adversarial networks (GANs) and diffusion-based methods across extensive privacy, utility, and fidelity evaluations. RL-Syn performs comparably to diffusion models and outperforms GANs on MIMIC-IV, while outperforming both diffusion models and GANs on the smaller AI-READI dataset. These results demonstrate that reinforcement learning provides a principled and effective alternative for synthetic biomedical data generation, particularly in data-scarce regimes.
半/弱/无/有监督|不确定性|主动学习(2篇)
【1】Data relativistic uncertainty framework for low-illumination anime scenery image enhancement
标题：低照度动漫风景图像增强的数据相对论不确定性框架
链接：https://arxiv.org/abs/2512.21944
备注：Preprint, awaiting submission to the appropriate conference or journal
摘要：与目前自然图像和视频中的弱光增强相比，本研究针对动漫场景图像中的弱光质量下降问题，弥补了这一领域的空白。对于这样一个探索不足的增强任务，我们首先从各种来源策划图像，并构建一个具有不同环境和照明条件的未配对动漫场景数据集，以解决数据稀缺问题。为了利用不同光照条件下固有的不确定性信息的能力，我们提出了一个数据相对论不确定性（DRU）框架，其动机来自相对论GAN。通过与光的波粒二象性类比，我们的框架可解释地定义和量化暗/亮样本的照明不确定性，这被用来动态调整目标函数，以在数据不确定性下重新校准模型学习。大量的实验证明了DRU框架的有效性，通过训练几个版本的EnlightenGANs，产生了超越最先进方法的卓越感知和美学品质，这些方法无法从数据不确定性的角度进行学习。我们希望我们的框架可以为潜在的视觉和语言领域提供一种以数据为中心的学习新范式。代码可用。
摘要 ：By contrast with the prevailing works of low-light enhancement in natural images and videos, this study copes with the low-illumination quality degradation in anime scenery images to bridge the domain gap. For such an underexplored enhancement task, we first curate images from various sources and construct an unpaired anime scenery dataset with diverse environments and illumination conditions to address the data scarcity. To exploit the power of uncertainty information inherent with the diverse illumination conditions, we propose a Data Relativistic Uncertainty (DRU) framework, motivated by the idea from Relativistic GAN. By analogy with the wave-particle duality of light, our framework interpretably defines and quantifies the illumination uncertainty of dark/bright samples, which is leveraged to dynamically adjust the objective functions to recalibrate the model learning under data uncertainty. Extensive experiments demonstrate the effectiveness of DRU framework by training several versions of EnlightenGANs, yielding superior perceptual and aesthetic qualities beyond the state-of-the-art methods that are incapable of learning from data uncertainty perspective. We hope our framework can expose a novel paradigm of data-centric learning for potential visual and language domains. Code is available.
【2】Dynamic Feedback Engines: Layer-Wise Control for Self-Regulating Continual Learning
标题：动态反馈引擎：自我调节持续学习的分层控制
链接：https://arxiv.org/abs/2512.21743
备注：14 pages
摘要：持续学习的目的是获得新的任务，同时保持以前学习过的任务的性能，但大多数方法都与灾难性的遗忘作斗争。现有的方法通常统一处理所有层，通常以稳定性换取塑性，反之亦然。然而，在对任务进行分类时，不同的层自然会表现出不同程度的不确定性（熵）。高熵层由于无法捕获特定任务的模式而往往不适合，而低熵层由于过于自信和专业化而有过度适合的风险。为了解决这种不平衡，我们提出了一种熵感知的持续学习方法，该方法采用动态反馈机制来根据其熵来调节每一层。具体来说，我们的方法减少高熵层中的熵以减轻欠拟合，并增加过度自信层中的熵以减轻过度拟合。这种自适应调节鼓励模型收敛到更宽的局部最小值，这已被证明可以提高泛化能力。我们的方法是通用的，可以与基于重放和正则化的方法无缝集成。在各种数据集上的实验表明，与最先进的持续学习基线相比，性能有了很大的提高。
摘要：Continual learning aims to acquire new tasks while preserving performance on previously learned ones, but most methods struggle with catastrophic forgetting. Existing approaches typically treat all layers uniformly, often trading stability for plasticity or vice versa. However, different layers naturally exhibit varying levels of uncertainty (entropy) when classifying tasks. High-entropy layers tend to underfit by failing to capture task-specific patterns, while low-entropy layers risk overfitting by becoming overly confident and specialized. To address this imbalance, we propose an entropy-aware continual learning method that employs a dynamic feedback mechanism to regulate each layer based on its entropy. Specifically, our approach reduces entropy in high-entropy layers to mitigate underfitting and increases entropy in overly confident layers to alleviate overfitting. This adaptive regulation encourages the model to converge to wider local minima, which have been shown to improve generalization. Our method is general and can be seamlessly integrated with both replay- and regularization-based approaches. Experiments on various datasets demonstrate substantial performance gains over state-of-the-art continual learning baselines.
迁移|Zero/Few/One-Shot|自适应(2篇)
【1】Multi-agent Adaptive Mechanism Design
标题：多智能体自适应机制设计
链接：https://arxiv.org/abs/2512.21794
摘要：我们研究了一个顺序机制设计问题，其中一个主要的目的是从多个理性的代理人，而开始没有事先知道代理人的信念，以引起真实的报告。我们引入了分布式鲁棒自适应机制（DRAM），这是一个通用框架，结合了机制设计和在线学习的见解，共同解决了真实性和成本最优性问题。在整个序列游戏中，该机制估计代理人的信念，并迭代地更新一个具有收缩模糊集的分布鲁棒线性规划，以减少支付，同时保持真实性。我们的机制保证真实的报告，以高概率，同时实现$\tilde{O}（\sqrt{T}）$累积遗憾，我们建立了一个匹配的下限表明，没有真实的自适应机制可以渐近做得更好。该框架推广到插件估计器，支持结构化先验和延迟反馈。据我们所知，这是第一个自适应机制，在一般设置下，保持真实性，并实现最佳的遗憾时，激励约束是未知的，必须学习。
摘要：We study a sequential mechanism design problem in which a principal seeks to elicit truthful reports from multiple rational agents while starting with no prior knowledge of agents' beliefs. We introduce Distributionally Robust Adaptive Mechanism (DRAM), a general framework combining insights from both mechanism design and online learning to jointly address truthfulness and cost-optimality. Throughout the sequential game, the mechanism estimates agents' beliefs and iteratively updates a distributionally robust linear program with shrinking ambiguity sets to reduce payments while preserving truthfulness. Our mechanism guarantees truthful reporting with high probability while achieving $\tilde{O}(\sqrt{T})$ cumulative regret, and we establish a matching lower bound showing that no truthful adaptive mechanism can asymptotically do better. The framework generalizes to plug-in estimators, supporting structured priors and delayed feedback. To our knowledge, this is the first adaptive mechanism under general settings that maintains truthfulness and achieves optimal regret when incentive constraints are unknown and must be learned.
【2】AVP-Fusion: Adaptive Multi-Modal Fusion and Contrastive Learning for Two-Stage Antiviral Peptide Identification
标题：AVP-Fusion：自适应多模式融合和对比学习用于两阶段抗病毒肽识别
链接：https://arxiv.org/abs/2512.21544
摘要：抗病毒肽的准确鉴定是加速新药开发的关键。然而，目前的计算方法难以捕获复杂的序列依赖性，并有效地处理模糊的，难以分类的样品。为了应对这些挑战，我们提出了AVP融合，这是一种新的两阶段深度学习框架，集成了自适应特征融合和对比学习。与传统的静态特征拼接不同，我们使用10个不同的描述符构建了一个全景特征空间，并引入了自适应门控机制。该机制基于序列上下文动态调节CNN提取的局部模体和BiLSTM捕获的全局依赖关系的权重。此外，为了解决数据分布的挑战，我们采用了由在线硬示例挖掘（OHEM）和基于BLOSUM62的数据增强驱动的对比学习策略，这显着锐化了模型的决策边界。在基准集1数据集上的实验结果表明，AVP融合实现了0.9531的准确度和0.9064的MCC，显著优于最先进的方法。在第二阶段，利用迁移学习，即使在有限的样本量下，该模型也可以对六个病毒家族和八种特定病毒进行精确的子类预测。总之，AVP融合作为一个强大的和可解释的高通量抗病毒药物筛选工具。
摘要：Accurate identification of antiviral peptides (AVPs) is critical for accelerating novel drug development. However, current computational methods struggle to capture intricate sequence dependencies and effectively handle ambiguous, hard-to-classify samples. To address these challenges, we propose AVP-Fusion, a novel two-stage deep learning framework integrating adaptive feature fusion and contrastive learning. Unlike traditional static feature concatenation, we construct a panoramic feature space using 10 distinct descriptors and introduce an Adaptive Gating Mechanism.This mechanism dynamically regulates the weights of local motifs extracted by CNNs and global dependencies captured by BiLSTMs based on sequence context. Furthermore, to address data distribution challenges, we employ a contrastive learning strategy driven by Online Hard Example Mining (OHEM) and BLOSUM62-based data augmentation, which significantly sharpens the model's decision boundaries. Experimental results on the benchmark Set 1 dataset demonstrate that AVP-Fusion achieves an accuracy of 0.9531 and an MCC of 0.9064, significantly outperforming state-of-the-art methods. In the second stage, leveraging transfer learning, the model enables precise subclass prediction for six viral families and eight specific viruses, even under limited sample sizes. In summary, AVP-Fusion serves as a robust and interpretable tool for high-throughput antiviral drug screening.
强化学习(1篇)
【1】A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning
标题：采用强化学习的新鲜感无线网络调查
链接：https://arxiv.org/abs/2512.21412
摘要：信息时代（AoI）已经成为现代无线系统中数据新鲜度的核心衡量标准，但现有的调查要么集中在经典的AoI公式上，要么提供无线网络中强化学习（RL）的广泛讨论，而没有将新鲜度作为统一的学习问题。出于这一差距的动机，这项调查专门通过AoI和广义新鲜度优化的镜头来检查RL。我们将AoI及其变体组织成原生的、基于功能的和面向应用的系列，从而更清晰地了解B5G和6G系统中应如何对新鲜度进行建模。在此基础上，我们引入了一个以策略为中心的分类，反映了与新鲜度最相关的决策，包括更新控制RL，中等访问RL，风险敏感RL和多代理RL。这种结构为理解学习如何支持采样、调度、轨迹规划、介质访问和分布式协调提供了一个连贯的框架。我们进一步综合了RL驱动的新鲜度控制的最新进展，并强调了与延迟决策过程，随机可变性和跨层设计相关的开放性挑战。目标是为下一代无线网络中基于学习的新鲜度优化建立统一的基础。
摘要：The age of information (AoI) has become a central measure of data freshness in modern wireless systems, yet existing surveys either focus on classical AoI formulations or provide broad discussions of reinforcement learning (RL) in wireless networks without addressing freshness as a unified learning problem. Motivated by this gap, this survey examines RL specifically through the lens of AoI and generalized freshness optimization. We organize AoI and its variants into native, function-based, and application-oriented families, providing a clearer view of how freshness should be modeled in B5G and 6G systems. Building on this foundation, we introduce a policy-centric taxonomy that reflects the decisions most relevant to freshness, consisting of update-control RL, medium-access RL, risk-sensitive RL, and multi-agent RL. This structure provides a coherent framework for understanding how learning can support sampling, scheduling, trajectory planning, medium access, and distributed coordination. We further synthesize recent progress in RL-driven freshness control and highlight open challenges related to delayed decision processes, stochastic variability, and cross-layer design. The goal is to establish a unified foundation for learning-based freshness optimization in next-generation wireless networks.
元学习(1篇)
【1】MAD-NG: Meta-Auto-Decoder Neural Galerkin Method for Solving Parametric Partial Differential Equations
标题：MAD-NG：求解参数偏微分方程的元自动解码神经Galerkin方法
链接：https://arxiv.org/abs/2512.21633
摘要：参数偏微分方程（PDE）是建模的基础，广泛的物理和工程系统的不确定性或变化的参数的影响。传统的基于神经网络的求解器，如物理信息神经网络（PINN）和深度伽辽金方法，由于依赖于全时空近似，通常面临泛化和长时间预测效率的挑战。为了解决这些问题，我们提出了一个新的和可扩展的框架，显着提高神经伽辽金方法（NGM），通过纳入元自动解码器（MAD）的范例。我们的方法利用时空解耦来实现更稳定和有效的时间整合，而元学习驱动的自适应允许快速泛化到看不见的参数配置，只需最少的再训练。此外，随机稀疏更新有效地降低了计算成本，而不影响精度。总之，这些进步使我们的方法能够实现物理上一致的，长期的预测复杂的参数化演化方程，显着降低计算开销。基准问题的数值实验表明，我们的方法表现相对较好的精度，鲁棒性和适应性。
摘要：Parametric partial differential equations (PDEs) are fundamental for modeling a wide range of physical and engineering systems influenced by uncertain or varying parameters. Traditional neural network-based solvers, such as Physics-Informed Neural Networks (PINNs) and Deep Galerkin Methods, often face challenges in generalization and long-time prediction efficiency due to their dependence on full space-time approximations. To address these issues, we propose a novel and scalable framework that significantly enhances the Neural Galerkin Method (NGM) by incorporating the Meta-Auto-Decoder (MAD) paradigm. Our approach leverages space-time decoupling to enable more stable and efficient time integration, while meta-learning-driven adaptation allows rapid generalization to unseen parameter configurations with minimal retraining. Furthermore, randomized sparse updates effectively reduce computational costs without compromising accuracy. Together, these advancements enable our method to achieve physically consistent, long-horizon predictions for complex parameterized evolution equations with significantly lower computational overhead. Numerical experiments on benchmark problems demonstrate that our methods performs comparatively well in terms of accuracy, robustness, and adaptability.
医学相关(4篇)
【1】MMCTOP: A Multimodal Textualization and Mixture-of-Experts Framework for Clinical Trial Outcome Prediction
标题：MMCTOP：临床试验结果预测的多模式文本化和专家混合框架
链接：https://arxiv.org/abs/2512.21897
备注：15 pages, 3 figures, 5 tables
摘要：为了解决高维生物医学信息学中多模态数据融合的挑战，我们提出了MMCTOP，一个多模态临床试验结果预测框架，它集成了异构生物医学信号，包括（i）分子结构表示，（ii）协议元数据和长格式资格叙述，以及（iii）疾病本体。MMCTOP将模式引导的文本化和输入保真度验证与模态感知的表示学习相结合，其中特定于域的编码器生成对齐的嵌入，这些嵌入由Transformer骨干融合，该骨干由药物疾病条件稀疏专家混合（SMoE）增强。这种设计明确支持跨治疗和设计子空间的专业化，同时通过top-k路由保持可扩展的计算。MMCTOP在基准数据集的单峰和多峰基线上实现了精度，F1和AUC的一致改进，消融表明模式引导的文本化和选择性专家路由对性能和稳定性有重大贡献。我们还应用温度缩放来获得校准的概率，确保下游决策支持的可靠风险估计。总的来说，MMCTOP通过结合受控叙述规范化，上下文条件专家融合和旨在生物医学信息学中的可重复性和再现性的操作保障措施来推进多模式试验建模。
摘要：Addressing the challenge of multimodal data fusion in high-dimensional biomedical informatics, we propose MMCTOP, a MultiModal Clinical-Trial Outcome Prediction framework that integrates heterogeneous biomedical signals spanning (i) molecular structure representations, (ii) protocol metadata and long-form eligibility narratives, and (iii) disease ontologies. MMCTOP couples schema-guided textualization and input-fidelity validation with modality-aware representation learning, in which domain-specific encoders generate aligned embeddings that are fused by a transformer backbone augmented with a drug-disease-conditioned sparse Mixture-of-Experts (SMoE). This design explicitly supports specialization across therapeutic and design subspaces while maintaining scalable computation through top-k routing. MMCTOP achieves consistent improvements in precision, F1, and AUC over unimodal and multimodal baselines on benchmark datasets, and ablations show that schema-guided textualization and selective expert routing contribute materially to performance and stability. We additionally apply temperature scaling to obtain calibrated probabilities, ensuring reliable risk estimation for downstream decision support. Overall, MMCTOP advances multimodal trial modeling by combining controlled narrative normalization, context-conditioned expert fusion, and operational safeguards aimed at auditability and reproducibility in biomedical informatics.
【2】VAMP-Net: An Interpretable Multi-Path Framework of Genomic Permutation-Invariant Set Attention and Quality-Aware 1D-CNN for MTB Drug Resistance
标题：VAMP-Net：基因组置换的可解释多路径框架-不变性针对MTB耐药性设置关注和质量感知1D-CNN
链接：https://arxiv.org/abs/2512.21786
摘要 ：由于复杂的上位相互作用和高度可变的测序数据质量，结核分枝杆菌耐药性的基因组预测仍然具有挑战性。我们提出了一种新的可解释变量感知多路径网络（VAMP-Net），通过互补的机器学习路径来解决这两个挑战。Path-1采用Set Attention Transformer处理置换不变的变体集，以捕获基因组位点之间的上位相互作用。Path-2利用1D卷积神经网络分析变体调用格式质量指标，以学习自适应置信度分数。融合模块结合两种途径进行最终抗性分类。我们对未掩蔽与填充掩蔽的Set Attention Blocks进行了比较评估，并证明我们的多路径架构比基线CNN和MLP模型具有更好的性能，利福平（RIF）和利福平（RFB）抗性预测的准确率超过95%，AUC约为97%。该框架提供了双层可解释性：注意力权重分析揭示了上位性网络，并将整合因子（IG）应用于关键耐药位点（特别是rpoB），而来自CNN途径的基于梯度的特征重要性揭示了药物对数据质量指标的特定依赖性。这种架构通过在两个不同的层面上提供最先进的预测性能以及可审计的可解释性来推进临床基因组学，即突变集的遗传因果关系和测序证据的技术置信度，为稳健的，临床可操作的耐药性预测建立了新的范式。
摘要：Genomic prediction of drug resistance in Mycobacterium tuberculosis remains challenging due to complex epistatic interactions and highly variable sequencing data quality. We present a novel Interpretable Variant-Aware Multi-Path Network (VAMP-Net) that addresses both challenges through complementary machine learning pathways. Path-1 employs a Set Attention Transformer processing permutation-invariant variant sets to capture epistatic interactions between genomic loci. Path-2 utilizes a 1D Convolutional Neural Network that analyzes Variant Call Format quality metrics to learn adaptive confidence scores. A fusion module combines both pathways for final resistance classification. We conduct comparative evaluations of unmasked versus padding-masked Set Attention Blocks, and demonstrate that our multi-path architecture achieves superior performance over baseline CNN and MLP models, with accuracy exceeding 95% and AUC around 97% for Rifampicin (RIF) and Rifabutin (RFB) resistance prediction. The framework provides dual-layer interpretability: Attention Weight Analysis reveals Epistatic networks, and Integrated Gradients (IG) was applied for critical resistance loci (notably rpoB), while gradient-based feature importance from the CNN pathway uncovers drug-specific dependencies on data quality metrics. This architecture advances clinical genomics by delivering state-of-the-art predictive performance alongside auditable interpretability at two distinct levels, genetic causality of mutation sets and technical confidence of sequencing evidence, establishing a new paradigm for robust, clinically-actionable resistance prediction.
【3】Robustness and Scalability Of Machine Learning for Imbalanced Clinical Data in Emergency and Critical Care
标题：紧急和重症监护中不平衡临床数据的机器学习的稳健性和可扩展性
链接：https://arxiv.org/abs/2512.21602
摘要：急诊和重症监护环境需要准确且计算效率高的预测模型，但这些环境中的临床数据往往严重失衡。这种偏斜破坏了模型的可靠性，特别是对于罕见但临床关键的结果，使得鲁棒性和可扩展性对于现实世界的使用至关重要。在本文中，我们系统地评估了经典机器学习模型在MIMIC-IV-ED和eICU不平衡表格数据上的鲁棒性和可扩展性。使用互补指标量化类不平衡，并比较了基于树的方法，最先进的TabNet深度学习模型和自定义轻量级残差网络的性能。TabResNet被设计为TabNet的计算效率替代方案，用精简的剩余架构取代其复杂的注意力机制，以保持实时临床使用的代表性能力。所有模型均通过贝叶斯超参数搜索进行优化，并评估预测性能，对增加不平衡的鲁棒性和计算可扩展性。我们在七个临床重要预测任务上的结果表明，基于树的方法，特别是XGBoost，在不平衡水平上始终实现了最稳定的性能，并随着样本量的增加而有效地扩展。深度表格模型在不平衡的情况下退化得更厉害，计算成本更高，而TabResNet提供了TabNet的更轻的替代品，但没有超过集成基准。这些研究结果表明，在紧急和重症监护，鲁棒性的不平衡和计算的可扩展性可能超过架构的复杂性。基于树的集成方法目前提供了最实用和临床可行的选择，为从业者提供了一个框架，用于选择适合高风险，时间敏感环境的模型。
摘要：Emergency and intensive care environments require predictive models that are both accurate and computationally efficient, yet clinical data in these settings are often severely imbalanced. Such skewness undermines model reliability, particularly for rare but clinically crucial outcomes, making robustness and scalability essential for real-world usage. In this paper, we systematically evaluate the robustness and scalability of classical machine learning models on imbalanced tabular data from MIMIC-IV-ED and eICU. Class imbalance was quantified using complementary metrics, and we compared the performance of tree-based methods, the state-of-the-art TabNet deep learning model, and a custom lightweight residual network. TabResNet was designed as a computationally efficient alternative to TabNet, replacing its complex attention mechanisms with a streamlined residual architecture to maintain representational capacity for real-time clinical use. All models were optimized via a Bayesian hyperparameter search and assessed on predictive performance, robustness to increasing imbalance, and computational scalability. Our results, on seven clinically vital predictive tasks, show that tree-based methods, particularly XGBoost, consistently achieved the most stable performance across imbalance levels and scaled efficiently with sample size. Deep tabular models degraded more sharply under imbalance and incurred higher computational costs, while TabResNet provided a lighter alternative to TabNet but did not surpass ensemble benchmarks. These findings indicate that in emergency and critical care, robustness to imbalance and computational scalability could outweigh architectural complexity. Tree-based ensemble methods currently offer the most practical and clinically feasible choice, equipping practitioners with a framework for selecting models suited to high-stakes, time-sensitive environments.
【4】A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding
标题：临床知情和可解释医学图像理解的工具瓶颈框架
链接：https://arxiv.org/abs/2512.21414
摘要：最近由视觉语言模型（VLM）提供支持的工具使用框架通过使用专用工具进行模型预测来提高图像理解。从广义上讲，这些框架利用VLM和预先指定的工具箱将预测任务分解为多个工具调用（通常是深度学习模型），这些工具调用被组合以进行预测。组合工具的主要方法是使用文本，通过嵌入在VLM生成的代码或自然语言中的函数调用。然而，这些方法通常在医学图像理解上表现不佳，其中显著信息被编码为难以单独通过文本组成或融合的空间定位特征。为了解决这个问题，我们提出了一个工具使用框架的医学图像理解称为工具瓶颈框架（TBF），它组成的VLM-selected工具使用一个学习的工具瓶颈模型（TBM）。对于给定的图像和任务，TBF利用现成的医学VLM从工具箱中选择工具，每个工具提取临床相关特征。这些工具不是基于文本的组合，而是由TBM组合，TBM在输出最终预测之前使用神经网络计算和融合工具输出。我们提出了一个简单而有效的策略，让隧道掘进机作出预测与任何任意的VLM工具选择。总的来说，我们的框架不仅提高了医学成像环境中的工具使用，而且还产生了更多可解释的，临床上的预测因子。我们评估了TBF在组织病理学和皮肤病学任务中的表现，发现这些优势使我们的框架能够与基于深度学习的分类器，VLM和最先进的工具使用框架相媲美或更好，特别是在数据有限的情况下。我们的代码可在https://github.com/christinaliu2020/tool-bottleneck-framework上获得。
摘要：Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools. Broadly, these frameworks leverage VLMs and a pre-specified toolbox to decompose the prediction task into multiple tool calls (often deep learning models) which are composed to make a prediction. The dominant approach to composing tools is using text, via function calls embedded in VLM-generated code or natural language. However, these methods often perform poorly on medical image understanding, where salient information is encoded as spatially-localized features that are difficult to compose or fuse via text alone. To address this, we propose a tool-use framework for medical image understanding called the Tool Bottleneck Framework (TBF), which composes VLM-selected tools using a learned Tool Bottleneck Model (TBM). For a given image and task, TBF leverages an off-the-shelf medical VLM to select tools from a toolbox that each extract clinically-relevant features. Instead of text-based composition, these tools are composed by the TBM, which computes and fuses the tool outputs using a neural network before outputting the final prediction. We propose a simple and effective strategy for TBMs to make predictions with any arbitrary VLM tool selection. Overall, our framework not only improves tool-use in medical imaging contexts, but also yields more interpretable, clinically-grounded predictors. We evaluate TBF on tasks in histopathology and dermatology and find that these advantages enable our framework to perform on par with or better than deep learning-based classifiers, VLMs, and state-of-the-art tool-use frameworks, with particular gains in data-limited regimes. Our code is available at https://github.com/christinaliu2020/tool-bottleneck-framework.
蒸馏|知识提取(1篇)
【1】Secure and Explainable Fraud Detection in Finance via Hierarchical Multi-source Dataset Distillation
标题：通过分层多源数据集蒸馏进行安全且可解释的金融欺诈检测
链接：https://arxiv.org/abs/2512.21866
摘要：我们提出了一个可解释的，隐私保护的数据集蒸馏框架，用于协同金融欺诈检测。经过训练的随机森林被转换成透明的、轴对齐的规则区域（叶超矩形），并且通过在每个区域内均匀采样来生成合成事务。这产生了一个紧凑的，可审计的代理数据集，保留了本地功能的相互作用，而不会暴露敏感的原始记录。规则区域还支持可解释性：聚合的规则统计（例如，支持度和提升度）描述全局模式，同时将每个案例分配到其生成区域，给出了简洁的人类可读的基本原理和基于树投票分歧的校准不确定性。 在IEEE-CIS欺诈数据集（三个类似机构的集群中的59万笔交易）上，经过提炼的数据集将数据量减少了85%至93%（通常低于原始数据的15%），同时保持了具有竞争力的精度和微F1，只有适度的AUC下降。跨机构共享和增强合成数据可以提高跨集群的精确度、召回率和AUC。真实与合成结构保持高度相似（通过最近邻余弦分析超过93%）。当区分训练和保留记录时，记忆推理攻击在机会水平（约0.50）上执行，这表明记忆风险较低。使用分歧分数去除高不确定性合成点进一步提高AUC（高达0.687）并改善校准。灵敏度试验显示对蒸馏比的依赖性较弱（AUC从6%至60%约为0.641至0.645）。 总体而言，树区域蒸馏实现了可信赖的，可部署的欺诈分析，具有可解释的全局规则，具有量化不确定性的每个案例的基本原理，以及适用于多机构设置和监管审计的强大隐私属性。
摘要：We propose an explainable, privacy-preserving dataset distillation framework for collaborative financial fraud detection. A trained random forest is converted into transparent, axis-aligned rule regions (leaf hyperrectangles), and synthetic transactions are generated by uniformly sampling within each region. This produces a compact, auditable surrogate dataset that preserves local feature interactions without exposing sensitive original records. The rule regions also support explainability: aggregated rule statistics (for example, support and lift) describe global patterns, while assigning each case to its generating region gives concise human-readable rationales and calibrated uncertainty based on tree-vote disagreement. On the IEEE-CIS fraud dataset (590k transactions across three institution-like clusters), distilled datasets reduce data volume by 85% to 93% (often under 15% of the original) while maintaining competitive precision and micro-F1, with only a modest AUC drop. Sharing and augmenting with synthesized data across institutions improves cross-cluster precision, recall, and AUC. Real vs. synthesized structure remains highly similar (over 93% by nearest-neighbor cosine analysis). Membership-inference attacks perform at chance level (about 0.50) when distinguishing training from hold-out records, suggesting low memorization risk. Removing high-uncertainty synthetic points using disagreement scores further boosts AUC (up to 0.687) and improves calibration. Sensitivity tests show weak dependence on the distillation ratio (AUC about 0.641 to 0.645 from 6% to 60%). Overall, tree-region distillation enables trustworthy, deployable fraud analytics with interpretable global rules, per-case rationales with quantified uncertainty, and strong privacy properties suitable for multi-institution settings and regulatory audit.
聚类(3篇)
【1】Missing Pattern Tree based Decision Grouping and Ensemble for Deep Incomplete Multi-View Clustering
标题：基于缺失模式树的深度不完整多视图集群决策集和集合
链接：https://arxiv.org/abs/2512.21510
摘要：现实世界中的多视图数据通常表现出高度不一致的缺失模式，这对不完全多视图聚类（IMVC）的有效性提出了挑战。虽然现有的IMVC方法已经从基于估算和无估算的途径两方面取得了进展，但它们忽略了对利用不足的问题，即，不一致的缺失模式使得不完整但可用的多视图对不能被充分利用，从而限制了模型性能。为了解决这个问题，我们提出了一种新型的基于缺失模式树的IMVC框架，名为TreeEIC。具体来说，TreeEIC为了充分利用可用的多视图对，首先定义了缺失模式树模型，根据不同的缺失模式将数据分组为多个决策集，然后在每个决策集中执行多视图聚类。此外，提出了一个多视图决策集成模块聚合聚类结果从所有的决策集，推断基于不确定性的权重，以抑制不可靠的聚类决策，并产生强大的决策。最后，一个集成到个人的知识蒸馏模块将集成知识转移到特定于视图的聚类模型，这使得集成和个人模块，以促进彼此通过优化跨视图的一致性和集群间的歧视损失。在多个基准数据集上的大量实验表明，我们的TreeEIC实现了最先进的IMVC性能，并在高度不一致的缺失模式下表现出卓越的鲁棒性。
摘要：Real-world multi-view data usually exhibits highly inconsistent missing patterns which challenges the effectiveness of incomplete multi-view clustering (IMVC). Although existing IMVC methods have made progress from both imputation-based and imputation-free routes, they have overlooked the pair under-utilization issue, i.e., inconsistent missing patterns make the incomplete but available multi-view pairs unable to be fully utilized, thereby limiting the model performance. To address this, we propose a novel missing-pattern tree based IMVC framework entitled TreeEIC. Specifically, to achieve full exploitation of available multi-view pairs, TreeEIC first defines the missing-pattern tree model to group data into multiple decision sets according to different missing patterns, and then performs multi-view clustering within each set. Furthermore, a multi-view decision ensemble module is proposed to aggregate clustering results from all decision sets, which infers uncertainty-based weights to suppress unreliable clustering decisions and produce robust decisions. Finally, an ensemble-to-individual knowledge distillation module transfers the ensemble knowledge to view-specific clustering models, which enables ensemble and individual modules to promote each other by optimizing cross-view consistency and inter-cluster discrimination losses. Extensive experiments on multiple benchmark datasets demonstrate that our TreeEIC achieves state-of-the-art IMVC performance and exhibits superior robustness under highly inconsistent missing patterns.
【2】Scalable Deep Subspace Clustering Network
标题：可扩展深子空间集群网络
链接：https://arxiv.org/abs/2512.21434
备注：Published at the 2025 IEEE 12th International Conference on Data Science and Advanced Analytics (DSAA)
摘要：子空间聚类方法面临着固有的可扩展性限制，因为构建完整的$n\times n$仿射和执行谱分解的成本为O（n^3）$（其中$n$表示数据样本的数量）。虽然基于深度学习的方法改进了特征提取，但它们通过穷举的成对相似性计算来保持这种计算瓶颈。我们提出了SDSNet（可扩展的深子空间网络），一个深子空间聚类框架，通过（1）基于地标的近似，避免完全亲和矩阵，（2）自动编码器重建与自表达目标的联合优化，以及（3）因子分解表示的直接谱聚类，实现$\mathcal{O}（n）$复杂度。该框架将卷积自动编码器与子空间保留约束相结合。实验结果表明，SDSNet实现了与最先进的方法相当的聚类质量，并显着提高了计算效率。
摘要：Subspace clustering methods face inherent scalability limits due to the $O(n^3)$ cost (with $n$ denoting the number of data samples) of constructing full $n\times n$ affinities and performing spectral decomposition. While deep learning-based approaches improve feature extraction, they maintain this computational bottleneck through exhaustive pairwise similarity computations. We propose SDSNet (Scalable Deep Subspace Network), a deep subspace clustering framework that achieves $\mathcal{O}(n)$ complexity through (1) landmark-based approximation, avoiding full affinity matrices, (2) joint optimization of auto-encoder reconstruction with self-expression objectives, and (3) direct spectral clustering on factorized representations. The framework combines convolutional auto-encoders with subspace-preserving constraints. Experimental results demonstrate that SDSNet achieves comparable clustering quality to state-of-the-art methods with significantly improved computational efficiency.
【3】Modeling high dimensional point clouds with the spherical cluster model
标题：用球状团模型建模多维点云
链接：https://arxiv.org/abs/2512.21960
备注：Main text: 4 figures, 15 pages
摘要 ：参数聚类模型是一种统计模型，提供对定义聚类的点的几何洞察。{\em球形簇模型}（SC）通过球体$S（c，r）$逼近有限点集$P\subset \mathbb{R}^d$，如下所示。将$r$作为中心$c$和数据点之间距离的标准偏差的分数$η\in（0，1）$（超参数），SC模型的成本是位于球体$S$之外的所有数据点关于$S$的幂距离的总和。SC模型的中心点c是使该成本最小化的点。请注意，$η=0$会产生KMeans聚类中使用的著名质心。我们做了三个贡献。 首先，我们拟合一个球形集群产生一个严格的凸，但不光滑的组合优化问题。其次，我们提出了一个精确的求解器，使用克拉克梯度上的一个合适的分层细胞复杂的超球的安排定义。最后，我们提出了各种数据集上的实验范围从$d=9$到$d=10,000$，有两个主要的观察。首先，对于小/中维和小值$η$的数据集，以及对于高维数据集（例如$d>100$），无论$η$的值如何，精确算法都比基于BFGS的算法快几个数量级。其次，SC模型的中心表现为参数化的高维中位数。 SC模型是高维多变量数据分析的直接利益，和应用程序的混合物的SC设计将在配套文件中报告。
摘要：A parametric cluster model is a statistical model providing geometric insights onto the points defining a cluster. The {\em spherical cluster model} (SC) approximates a finite point set $P\subset \mathbb{R}^d$ by a sphere $S(c,r)$ as follows. Taking $r$ as a fraction $η\in(0,1)$ (hyper-parameter) of the std deviation of distances between the center $c$ and the data points, the cost of the SC model is the sum over all data points lying outside the sphere $S$ of their power distance with respect to $S$. The center $c$ of the SC model is the point minimizing this cost. Note that $η=0$ yields the celebrated center of mass used in KMeans clustering. We make three contributions. First, we show fitting a spherical cluster yields a strictly convex but not smooth combinatorial optimization problem. Second, we present an exact solver using the Clarke gradient on a suitable stratified cell complex defined from an arrangement of hyper-spheres. Finally, we present experiments on a variety of datasets ranging in dimension from $d=9$ to $d=10,000$, with two main observations. First, the exact algorithm is orders of magnitude faster than BFGS based heuristics for datasets of small/intermediate dimension and small values of $η$, and for high dimensional datasets (say $d>100$) whatever the value of $η$. Second, the center of the SC model behave as a parameterized high-dimensional median. The SC model is of direct interest for high dimensional multivariate data analysis, and the application to the design of mixtures of SC will be reported in a companion paper.
自动驾驶|车辆|车道检测等(2篇)
【1】RIPCN: A Road Impedance Principal Component Network for Probabilistic Traffic Flow Forecasting
标题：RIPCO：一种用于概率交通流预测的道路阻力主成分网络
链接：https://arxiv.org/abs/2512.21685
备注：Accepted at KDD 2026. 12 pages, 10 figures
摘要：准确的交通流量预测对于导航和叫车等智能交通服务至关重要。在这些应用中，预测中的不确定性估计是重要的，因为它有助于评估交通风险水平，评估预测的可靠性，并提供及时的警告。因此，概率交通流预测（PTFF）得到了极大的关注，因为它产生点预测和不确定性估计。然而，现有的PTFF方法仍然面临着两个关键的挑战：（1）如何发现和建模的交通流不确定性的原因，可靠的预测，（2）如何捕捉不确定性的时空相关性，准确的预测。 为了解决这些挑战，我们提出了RIPCN，道路阻抗主成分网络，集成了特定领域的交通理论与时空主成分学习PTFF。RIPCN引入了动态阻抗演化网络，该网络捕获由道路拥堵水平和流量变化驱动的定向交通转移模式，揭示了不确定性的直接原因，并提高了可靠性和可解释性。此外，主成分网络的设计，以预测未来流量协方差的主导特征向量，使模型能够捕捉时空不确定性的相关性。这种设计允许准确和有效的不确定性估计，同时也提高了点预测性能。在真实数据集上的实验结果表明，该方法优于现有的概率预测方法。
摘要：Accurate traffic flow forecasting is crucial for intelligent transportation services such as navigation and ride-hailing. In such applications, uncertainty estimation in forecasting is important because it helps evaluate traffic risk levels, assess forecast reliability, and provide timely warnings. As a result, probabilistic traffic flow forecasting (PTFF) has gained significant attention, as it produces both point forecasts and uncertainty estimates. However, existing PTFF approaches still face two key challenges: (1) how to uncover and model the causes of traffic flow uncertainty for reliable forecasting, and (2) how to capture the spatiotemporal correlations of uncertainty for accurate prediction. To address these challenges, we propose RIPCN, a Road Impedance Principal Component Network that integrates domain-specific transportation theory with spatiotemporal principal component learning for PTFF. RIPCN introduces a dynamic impedance evolution network that captures directional traffic transfer patterns driven by road congestion level and flow variability, revealing the direct causes of uncertainty and enhancing both reliability and interpretability. In addition, a principal component network is designed to forecast the dominant eigenvectors of future flow covariance, enabling the model to capture spatiotemporal uncertainty correlations. This design allows for accurate and efficient uncertainty estimation while also improving point prediction performance. Experimental results on real-world datasets show that our approach outperforms existing probabilistic forecasting methods.
【2】Comparative Analysis of Deep Learning Models for Perception in Autonomous Vehicles
标题：用于自动驾驶汽车感知的深度学习模型的比较分析
链接：https://arxiv.org/abs/2512.21673
备注：6 pages, 3 figures
摘要：最近，已经提出了大量的机器学习（ML）和深度学习（DL）算法来实现自动驾驶汽车（AV）的效率，安全性和可靠性。自动驾驶汽车使用感知系统来检测、定位和识别其他车辆、行人和路标，以执行安全导航和决策。在本文中，我们比较了DL模型的性能，包括YOLO-NAS和YOLOv 8，基于检测的感知任务。我们捕获一个自定义数据集，并使用我们的自定义数据集对两个DL模型进行实验。我们的分析表明，与YOLO-NAS模型相比，YOLOv 8 s模型节省了75%的训练时间。此外，当目标是实现最高的对象检测精度时，YOLOv 8 s模型（83%）优于YOLO-NAS模型（81%）。这些新兴DL模型的比较分析将使相关研究社区能够了解模型在真实用例场景下的性能。
摘要：Recently, a plethora of machine learning (ML) and deep learning (DL) algorithms have been proposed to achieve the efficiency, safety, and reliability of autonomous vehicles (AVs). The AVs use a perception system to detect, localize, and identify other vehicles, pedestrians, and road signs to perform safe navigation and decision-making. In this paper, we compare the performance of DL models, including YOLO-NAS and YOLOv8, for a detection-based perception task. We capture a custom dataset and experiment with both DL models using our custom dataset. Our analysis reveals that the YOLOv8s model saves 75% of training time compared to the YOLO-NAS model. In addition, the YOLOv8s model (83%) outperforms the YOLO-NAS model (81%) when the target is to achieve the highest object detection accuracy. These comparative analyses of these new emerging DL models will allow the relevant research community to understand the models' performance under real-world use case scenarios.
推理|分析|理解|解释(4篇)
【1】Explainable Multimodal Regression via Information Decomposition
标题：通过信息分解的可解释多峰回归
链接：https://arxiv.org/abs/2512.22102
备注：Project Page: https://github.com/zhaozhaoma/PIDReg
摘要：多模态回归旨在从异质输入源预测连续目标，并且通常依赖于融合策略，例如早期或晚期融合。然而，现有的方法缺乏原则性的工具来解开和量化每个模态及其相互作用的单独贡献，限制了多模态融合的可解释性。我们提出了一个新的多模态回归框架接地部分信息分解（PID），它分解成独特的，冗余的，和协同组件的特定模态表示。基本的PID框架本质上是欠定的。为了解决这个问题，我们通过在潜在表示和变换后的响应变量（逆正态变换后）的联合分布中强制高斯性来引入归纳偏差，从而实现PID项的分析计算。此外，我们推导出一个封闭形式的条件独立正则化，以促进每个模态内的独特信息的隔离。在六个真实世界数据集上进行的实验，包括一个关于多模态神经成像数据的大规模脑年龄预测的案例研究，表明我们的框架在预测准确性和可解释性方面都优于最先进的方法，同时还能够进行明智的模态选择以进行有效的推理。可在https://github.com/zhaozhaoma/PIDReg上获得实现。
摘要 ：Multimodal regression aims to predict a continuous target from heterogeneous input sources and typically relies on fusion strategies such as early or late fusion. However, existing methods lack principled tools to disentangle and quantify the individual contributions of each modality and their interactions, limiting the interpretability of multimodal fusion. We propose a novel multimodal regression framework grounded in Partial Information Decomposition (PID), which decomposes modality-specific representations into unique, redundant, and synergistic components. The basic PID framework is inherently underdetermined. To resolve this, we introduce inductive bias by enforcing Gaussianity in the joint distribution of latent representations and the transformed response variable (after inverse normal transformation), thereby enabling analytical computation of the PID terms. Additionally, we derive a closed-form conditional independence regularizer to promote the isolation of unique information within each modality. Experiments on six real-world datasets, including a case study on large-scale brain age prediction from multimodal neuroimaging data, demonstrate that our framework outperforms state-of-the-art methods in both predictive accuracy and interpretability, while also enabling informed modality selection for efficient inference. Implementation is available at https://github.com/zhaozhaoma/PIDReg.
【2】Assessing the Effectiveness of Membership Inference on Generative Music
标题：评估成员推理对生成音乐的有效性
链接：https://arxiv.org/abs/2512.21762
备注：10 pages, 3 figures, 3 tables
摘要：生成式AI系统正在迅速改进，现在能够以多种形式产生可信的输出，包括图像，文本和音频。然而，这种快速发展促使人们对用户隐私和在培训中使用受版权保护的作品进行越来越严格的审查。最近对机器学习模型的攻击称为隶属推理，位于这两个问题的十字路口。该攻击被给予一组记录和一个训练模型作为输入，并试图识别这些记录中的哪些记录可能已被用于训练模型。一方面，这种攻击可用于识别用于训练模型的用户数据，这可能会侵犯他们的隐私，特别是在敏感应用程序中，例如在医疗数据上训练的模型。另一方面，这种攻击可以被权利人用作公司未经许可使用他们的作品来训练模特的证据。 值得注意的是，似乎没有研究成员推理攻击（MIA）对生成音乐的影响。鉴于音乐产业价值数十亿美元，艺术家将从能够确定他们的作品是否在未经许可的情况下被使用中获益，我们认为这是一个迫切需要研究的问题。因此，在这项工作中，我们开始初步研究MIA是否对生成音乐有效。我们研究了几种现有攻击对MuseGAN的影响，MuseGAN是一种流行且有影响力的生成音乐模型。类似于以前的工作生成音频MIA，我们的研究结果表明，音乐数据是相当弹性的已知成员推理技术。
摘要：Generative AI systems are quickly improving, now able to produce believable output in several modalities including images, text, and audio. However, this fast development has prompted increased scrutiny concerning user privacy and the use of copyrighted works in training. A recent attack on machine-learning models called membership inference lies at the crossroads of these two concerns. The attack is given as input a set of records and a trained model and seeks to identify which of those records may have been used to train the model. On one hand, this attack can be used to identify user data used to train a model, which may violate their privacy especially in sensitive applications such as models trained on medical data. On the other hand, this attack can be used by rights-holders as evidence that a company used their works without permission to train a model. Remarkably, it appears that no work has studied the effect of membership inference attacks (MIA) on generative music. Given that the music industry is worth billions of dollars and artists would stand to gain from being able to determine if their works were being used without permission, we believe this is a pressing issue to study. As such, in this work we begin a preliminary study into whether MIAs are effective on generative music. We study the effect of several existing attacks on MuseGAN, a popular and influential generative music model. Similar to prior work on generative audio MIAs, our findings suggest that music data is fairly resilient to known membership inference techniques.
【3】Cerberus: Multi-Agent Reasoning and Coverage-Guided Exploration for Static Detection of Runtime Errors
标题：Cerberus：多智能体推理和覆盖引导探索，用于静态检测收件箱错误
链接：https://arxiv.org/abs/2512.21431
摘要：在一些软件开发场景中，希望在没有实际执行的情况下检测代码片段中的运行时错误和异常。一个典型的例子是在将在线代码片段集成到代码库之前检测它们中的运行时异常。在本文中，我们提出了Cerberus，一种新型的预测性、免执行的覆盖引导测试框架。Cerberus使用LLM来生成触发运行时错误的输入，并在不执行代码的情况下执行代码覆盖预测和错误检测。通过两阶段反馈回路，Cerberus首先旨在增加代码覆盖率和检测运行时错误，然后转移到只在覆盖率达到100%或其最大值时检测运行时错误，使其能够比提示LLM更好地执行这两个目的。我们的实证评估表明，Cerberus比传统的和基于学习的测试框架（在）完整的代码片段更有效地生成高覆盖率的测试用例，导致发现更多的运行时错误。
摘要：In several software development scenarios, it is desirable to detect runtime errors and exceptions in code snippets without actual execution. A typical example is to detect runtime exceptions in online code snippets before integrating them into a codebase. In this paper, we propose Cerberus, a novel predictive, execution-free coverage-guided testing framework. Cerberus uses LLMs to generate the inputs that trigger runtime errors and to perform code coverage prediction and error detection without code execution. With a two-phase feedback loop, Cerberus first aims to both increasing code coverage and detecting runtime errors, then shifts to focus only detecting runtime errors when the coverage reaches 100% or its maximum, enabling it to perform better than prompting the LLMs for both purposes. Our empirical evaluation demonstrates that Cerberus performs better than conventional and learning-based testing frameworks for (in)complete code snippets by generating high-coverage test cases more efficiently, leading to the discovery of more runtime errors.
【4】Sensitivity Analysis of the Consistency Assumption
标题：一致性假设的敏感性分析
链接：https://arxiv.org/abs/2512.21379
摘要：敏感性分析通过评估结论对偏离假设的敏感性来告知因果推断。一致性假设指出，没有隐藏的治疗版本，自然产生的结果等于干预产生的结果。在推理一致性违反的可能性时，区分协变量和处理版本可能会有所帮助。例如，在外科手术的背景下，基因组变量是协变量，特定外科医生的技能是治疗的一个版本。可能存在隐藏的治疗版本，本文通过一种新的敏感性分析解决了这一问题。鉴于敏感性分析的许多方法都集中在未测量的协变量的混杂，本文的方法是集中在治疗的隐藏版本的混杂。在本文中，新的数学符号被引入到支持新的方法，并举例说明应用程序。
摘要：Sensitivity analysis informs causal inference by assessing the sensitivity of conclusions to departures from assumptions. The consistency assumption states that there are no hidden versions of treatment and that the outcome arising naturally equals the outcome arising from intervention. When reasoning about the possibility of consistency violations, it can be helpful to distinguish between covariates and versions of treatment. In the context of surgery, for example, genomic variables are covariates and the skill of a particular surgeon is a version of treatment. There may be hidden versions of treatment, and this paper addresses that concern with a new kind of sensitivity analysis. Whereas many methods for sensitivity analysis are focused on confounding by unmeasured covariates, the methodology of this paper is focused on confounding by hidden versions of treatment. In this paper, new mathematical notation is introduced to support the novel method, and example applications are described.
检测相关(3篇)
【1】Smart IoT-Based Leak Forecasting and Detection for Energy-Efficient Liquid Cooling in AI Data Centers
标题：基于智能物联网的泄漏预测和检测，用于AI数据中心的节能液体冷却
链接：https://arxiv.org/abs/2512.21801
备注：7 pages, 6 figures, IEEE conference format
摘要：以GPU为中心的人工智能数据中心采用液体冷却来处理极端的热负荷，但冷却剂泄漏会导致计划外停机和延长维修时间，从而造成大量能源损失。我们提出了一个概念验证的智能物联网监控系统，该系统将LSTM神经网络用于概率泄漏预测，并将随机森林分类器用于即时检测。在符合ASHRAE 2021标准的合成数据上进行测试，我们的方法在正负30分钟的窗口内以90%的概率实现了96.5%的检测准确率和87%的预测准确率。分析表明，湿度、压力和流速提供了强预测信号，而温度由于服务器硬件中的热惯性而表现出最小的即时响应。该系统采用MQTT流，InfluxDB存储和Streamlit仪表板，提前2-4小时预测泄漏，同时在1分钟内识别突发事件。对于一个典型的47机架设施，这种方法可以通过主动维护而不是被动应急程序来防止大约1，500千瓦时的年度能源浪费。虽然验证仍然是综合性的，但结果为可持续数据中心运营中的未来运营部署建立了可行性。
摘要：AI data centers which are GPU centric, have adopted liquid cooling to handle extreme heat loads, but coolant leaks result in substantial energy loss through unplanned shutdowns and extended repair periods. We present a proof-of-concept smart IoT monitoring system combining LSTM neural networks for probabilistic leak forecasting with Random Forest classifiers for instant detection. Testing on synthetic data aligned with ASHRAE 2021 standards, our approach achieves 96.5% detection accuracy and 87% forecasting accuracy at 90% probability within plus or minus 30-minute windows. Analysis demonstrates that humidity, pressure, and flow rate deliver strong predictive signals, while temperature exhibits minimal immediate response due to thermal inertia in server hardware. The system employs MQTT streaming, InfluxDB storage, and Streamlit dashboards, forecasting leaks 2-4 hours ahead while identifying sudden events within 1 minute. For a typical 47-rack facility, this approach could prevent roughly 1,500 kWh annual energy waste through proactive maintenance rather than reactive emergency procedures. While validation remains synthetic-only, results establish feasibility for future operational deployment in sustainable data center operations.
【2】Causal-HM: Restoring Physical Generative Logic in Multimodal Anomaly Detection via Hierarchical Modulation
标题：Cause-HM：通过分层调制在多模式异常检测中恢复物理生成逻辑
链接：https://arxiv.org/abs/2512.21650
备注：10 pages, 4 figures
摘要：多模态无监督异常检测（UAD）对于智能制造的质量保证至关重要，特别是在机器人焊接等复杂过程中。然而，现有的方法经常遭受因果盲目性，治疗过程模态（例如，实时视频、音频和传感器）和结果模态（例如，焊后图像）作为相等的特征源，从而忽略了固有的物理生成逻辑。此外，高维视觉数据和低维传感器信号之间的异质性差距经常导致关键过程上下文被淹没。在本文中，我们提出了一个统一的多模态UAD框架，明确地模拟物理过程的结果依赖。具体来说，我们的框架结合了两个关键的创新：传感器引导的CHM调制机制，利用低维传感器信号作为上下文来指导高维视听特征提取，以及强制执行单向生成映射以识别违反物理一致性的异常的Cause-Hierarchical架构。在我们新构建的Weld-4 M基准上进行的四种模式的广泛实验表明，CaCl-HM达到了90.7%的最先进（SOTA）I-AUROC。代码将在论文被接受后发布。
摘要：Multimodal Unsupervised Anomaly Detection (UAD) is critical for quality assurance in smart manufacturing, particularly in complex processes like robotic welding. However, existing methods often suffer from causal blindness, treating process modalities (e.g., real-time video, audio, and sensors) and result modalities (e.g., post-weld images) as equal feature sources, thereby ignoring the inherent physical generative logic. Furthermore, the heterogeneity gap between high-dimensional visual data and low-dimensional sensor signals frequently leads to critical process context being drowned out. In this paper, we propose Causal-HM, a unified multimodal UAD framework that explicitly models the physical Process to Result dependency. Specifically, our framework incorporates two key innovations: a Sensor-Guided CHM Modulation mechanism that utilizes low-dimensional sensor signals as context to guide high-dimensional audio-visual feature extraction , and a Causal-Hierarchical Architecture that enforces a unidirectional generative mapping to identify anomalies that violate physical consistency. Extensive experiments on our newly constructed Weld-4M benchmark across four modalities demonstrate that Causal-HM achieves a state-of-the-art (SOTA) I-AUROC of 90.7%. Code will be released after the paper is accepted.
【3】CCAD: Compressed Global Feature Conditioned Anomaly Detection
标题：CCAD：压缩全局特征条件异常检测
链接：https://arxiv.org/abs/2512.21459
备注：18 pages, 9 figures
摘要：异常检测具有相当大的工业意义，特别是在异常数据有限的情况下。目前，基于重建和无监督表示的方法是主要的焦点。然而，基于无监督表示的方法难以在域偏移下提取鲁棒的特征，而基于重构的方法由于约束不足而经常遭受低训练效率和性能下降。为了解决这些问题，我们提出了一种新的方法，称为压缩全局特征条件异常检测（CCAD）。CCAD通过调整全局特征作为重建模型的新模态条件来协同这两种范式的优势。此外，我们设计了一个自适应压缩机制，以提高泛化和训练效率。大量的实验表明，CCAD在AUC方面始终优于最先进的方法，同时实现更快的收敛。此外，我们贡献了一个重组和重新注释的DAGM 2007数据集与新的注释版本，以进一步验证我们的方法的有效性。用于再现主要结果的代码可在https://github.com/chloeqxq/CCAD上获得。
摘要：Anomaly detection holds considerable industrial significance, especially in scenarios with limited anomalous data. Currently, reconstruction-based and unsupervised representation-based approaches are the primary focus. However, unsupervised representation-based methods struggle to extract robust features under domain shift, whereas reconstruction-based methods often suffer from low training efficiency and performance degradation due to insufficient constraints. To address these challenges, we propose a novel method named Compressed Global Feature Conditioned Anomaly Detection (CCAD). CCAD synergizes the strengths of both paradigms by adapting global features as a new modality condition for the reconstruction model. Furthermore, we design an adaptive compression mechanism to enhance both generalization and training efficiency. Extensive experiments demonstrate that CCAD consistently outperforms state-of-the-art methods in terms of AUC while achieving faster convergence. In addition, we contribute a reorganized and re-annotated version of the DAGM 2007 dataset with new annotations to further validate our method's effectiveness. The code for reproducing main results is available at https://github.com/chloeqxq/CCAD.
表征(1篇)
【1】Videos are Sample-Efficient Supervisions: Behavior Cloning from Videos via Latent Representations
标题：视频是样本高效的监督：通过潜在代表从视频中克隆行为
链接：https://arxiv.org/abs/2512.21586
摘要：人类可以有效地从视频中提取知识和学习技能，只需几次尝试和错误。然而，由于视觉输入的复杂性，缺乏动作或奖励信号，以及交互步骤的限制，复制自主代理的学习过程是一个很大的挑战。在本文中，我们提出了一种新的，无监督的，样本效率高的框架，以实现模仿学习的视频（ILV），命名为行为克隆视频通过潜在的表示（BCV-LR）。BCV-LR通过自监督任务从高维视频输入中提取与动作相关的潜在特征，然后利用基于动态的无监督目标来预测连续帧之间的潜在动作。预先训练的潜在动作经过微调，并有效地与在线的真实动作空间（具有收集的交互）对齐，以进行策略行为克隆。克隆的策略反过来又丰富了代理体验，以进一步进行潜在动作微调，从而实现高度样本效率的迭代策略改进。 我们进行了广泛的实验，一组具有挑战性的视觉任务，包括离散控制和连续控制。BCV-LR仅需少量交互即可实现有效（甚至在某些任务上达到专家级别）的策略性能，在24/28任务的样本效率方面超过了最先进的ILV基线和强化学习方法（提供环境奖励）。据我们所知，这项工作首次证明，视频可以支持非常高效的样本视觉策略学习，而无需访问任何其他专家监督。
摘要 ：Humans can efficiently extract knowledge and learn skills from the videos within only a few trials and errors. However, it poses a big challenge to replicate this learning process for autonomous agents, due to the complexity of visual input, the absence of action or reward signals, and the limitations of interaction steps. In this paper, we propose a novel, unsupervised, and sample-efficient framework to achieve imitation learning from videos (ILV), named Behavior Cloning from Videos via Latent Representations (BCV-LR). BCV-LR extracts action-related latent features from high-dimensional video inputs through self-supervised tasks, and then leverages a dynamics-based unsupervised objective to predict latent actions between consecutive frames. The pre-trained latent actions are fine-tuned and efficiently aligned to the real action space online (with collected interactions) for policy behavior cloning. The cloned policy in turn enriches the agent experience for further latent action finetuning, resulting in an iterative policy improvement that is highly sample-efficient. We conduct extensive experiments on a set of challenging visual tasks, including both discrete control and continuous control. BCV-LR enables effective (even expert-level on some tasks) policy performance with only a few interactions, surpassing state-of-the-art ILV baselines and reinforcement learning methods (provided with environmental rewards) in terms of sample efficiency across 24/28 tasks. To the best of our knowledge, this work for the first time demonstrates that videos can support extremely sample-efficient visual policy learning, without the need to access any other expert supervision.
3D|3D重建等相关(1篇)
【1】BertsWin: Resolving Topological Sparsity in 3D Masked Autoencoders via Component-Balanced Structural Optimization
标题：BertsWin：通过均衡结构优化解决3D掩蔽自动编码器中的布局稀疏性
链接：https://arxiv.org/abs/2512.21769
备注：Code available at https://github.com/AlevLab-dev/BertsWinMAE and https://github.com/AlevLab-dev/GCond. Zenodo repository (DOI: 10.5281/zenodo.17916932) contains source images, training logs, trained models, and code
摘要：自监督学习（SSL）和Vision Transformers（ViTs）方法的应用在2D医学成像领域展示了有希望的结果，但是在3D体积图像上使用这些方法充满了困难。标准掩码自动编码器（MAE）是最先进的2D解决方案，很难捕获三维空间关系，特别是当75%的令牌在预训练期间被丢弃时。我们提出了BertsWin，一个混合架构，结合使用Swin Transformer窗口的全BERT风格的令牌掩蔽，以增强SSL预训练期间的3D空间上下文学习。与只处理可见区域的经典MAE不同，BertsWin引入了一个完整的3D令牌网格（掩蔽和可见），保留了空间拓扑。为了平滑ViT的二次复杂度，使用了单层局部Swin窗口。我们引入了一个结构优先损失函数，并评估颞下颌关节锥形束计算机断层扫描的结果。随后的评估包括3D CT扫描上的TMJ分割。我们证明了BertsWin架构，通过保持一个完整的三维空间拓扑结构，本质上加速语义收敛的5.8倍的标准ViT-MAE基线相比。此外，当与我们提出的BertsWin优化器相结合时，完整的BertsWin框架实现了达到最先进的重建保真度所需的训练时间减少了15倍（44 vs 660）。分析表明，BertsWin实现了这种加速，而没有通常与密集体积处理相关的计算代价。在规范输入分辨率下，该架构保持理论FLOP奇偶校验与稀疏ViT基线，从而由于更快的收敛而导致总计算资源的显著净减少。
摘要：The application of self-supervised learning (SSL) and Vision Transformers (ViTs) approaches demonstrates promising results in the field of 2D medical imaging, but the use of these methods on 3D volumetric images is fraught with difficulties. Standard Masked Autoencoders (MAE), which are state-of-the-art solution for 2D, have a hard time capturing three-dimensional spatial relationships, especially when 75% of tokens are discarded during pre-training. We propose BertsWin, a hybrid architecture combining full BERT-style token masking using Swin Transformer windows, to enhance spatial context learning in 3D during SSL pre-training. Unlike the classic MAE, which processes only visible areas, BertsWin introduces a complete 3D grid of tokens (masked and visible), preserving the spatial topology. And to smooth out the quadratic complexity of ViT, single-level local Swin windows are used. We introduce a structural priority loss function and evaluate the results of cone beam computed tomography of the temporomandibular joints. The subsequent assessment includes TMJ segmentation on 3D CT scans. We demonstrate that the BertsWin architecture, by maintaining a complete three-dimensional spatial topology, inherently accelerates semantic convergence by a factor of 5.8x compared to standard ViT-MAE baselines. Furthermore, when coupled with our proposed GradientConductor optimizer, the full BertsWin framework achieves a 15-fold reduction in training epochs (44 vs 660) required to reach state-of-the-art reconstruction fidelity. Analysis reveals that BertsWin achieves this acceleration without the computational penalty typically associated with dense volumetric processing. At canonical input resolutions, the architecture maintains theoretical FLOP parity with sparse ViT baselines, resulting in a significant net reduction in total computational resources due to faster convergence.
编码器(1篇)
【1】Prefill vs. Decode Bottlenecks: SRAM-Frequency Tradeoffs and the Memory-Bandwidth Ceiling
标题：预编码与解码瓶颈：RAM频率权衡和内存带宽上限
链接：https://arxiv.org/abs/2512.22066
摘要：能源消耗决定了部署大型语言模型的成本和环境影响。本文研究了片上SRAM大小和工作频率对LLM推理的能效和性能的影响，重点关注计算绑定预填充和存储器绑定解码阶段的不同行为。我们的模拟方法结合了OpenRAM的能量建模，LLMCompass的延迟模拟，和ScaleSIM的脉动阵列的操作强度。我们的研究结果表明，总能量使用主要取决于SRAM的大小在这两个阶段，与较大的缓冲区显着增加静态能量由于泄漏，这是不抵消相应的延迟的好处。我们定量探讨的内存带宽瓶颈，表明，虽然高工作频率降低预填充延迟，其对内存绑定的解码延迟的积极影响是由外部存储器带宽。与直觉相反，高计算频率可以通过减少执行时间来减少总能量，从而减少静态能量消耗而不是动态功率增加。我们确定了一个最佳的硬件配置的模拟工作负载：高工作频率（1200 MHz-1400 MHz）和一个小的本地缓冲区大小为32 KB到64 KB。这种组合实现了最佳的能量延迟乘积，平衡了低延迟与高能效。此外，我们还演示了内存带宽如何作为性能上限，并且增加计算频率只会在工作负载受到内存限制的情况下产生性能增益。该分析为设计节能的LLM加速器提供了具体的架构见解，特别是对于旨在最大限度地减少能源开销的制造商。
摘要：Energy consumption dictates the cost and environmental impact of deploying Large Language Models. This paper investigates the impact of on-chip SRAM size and operating frequency on the energy efficiency and performance of LLM inference, focusing on the distinct behaviors of the compute-bound prefill and memory-bound decode phases. Our simulation methodology combines OpenRAM for energy modeling, LLMCompass for latency simulation, and ScaleSIM for systolic array operational intensity. Our findings show that total energy use is predominantly determined by SRAM size in both phases, with larger buffers significantly increasing static energy due to leakage, which is not offset by corresponding latency benefits. We quantitatively explore the memory-bandwidth bottleneck, demonstrating that while high operating frequencies reduce prefill latency, their positive impact on memory-bound decode latency is capped by the external memory bandwidth. Counter-intuitively, high compute frequency can reduce total energy by reducing execution time and consequently decreasing static energy consumption more than the resulting dynamic power increase. We identify an optimal hardware configuration for the simulated workload: high operating frequencies (1200MHz-1400MHz) and a small local buffer size of 32KB to 64KB. This combination achieves the best energy-delay product, balancing low latency with high energy efficiency. Furthermore, we demonstrate how memory bandwidth acts as a performance ceiling, and that increasing compute frequency only yields performance gains up to the point where the workload becomes memory-bound. This analysis provides concrete architectural insights for designing energy-efficient LLM accelerators, especially for datacenters aiming to minimize their energy overhead.
优化|敛散性(1篇)
【1】A Frobenius-Optimal Projection for Enforcing Linear Conservation in Learned Dynamical Models
标题：在习得动力学模型中强制线性保守的Frobenius最优投影
链接：https://arxiv.org/abs/2512.22084
摘要：我们考虑在数据驱动的线性动力学模型中恢复线性守恒律的问题。给定一个学习的算子$\widehat{A}$和一个满秩约束矩阵$C$编码一个或多个不变量，我们证明了在Frobenius范数下最接近$\widehat{A}$并满足$C^\top A = 0$的矩阵是正交投影$A^\star = \widehat{A} - C（C^\top C）^{-1}C^\top \widehat{A}$。这个修正是唯一定义的，低秩的，完全由违反$C^\top \widehat{A}$决定。在单不变的情况下，它减少到一个秩一更新。我们证明，$A^\star$强制执行精确的守恒，同时最小扰动的动态，我们验证这些属性数值上的马尔可夫型的例子。投影提供了一个基本的和一般的机制，用于将精确不变量嵌入任何学习的线性模型。
摘要：We consider the problem of restoring linear conservation laws in data-driven linear dynamical models. Given a learned operator $\widehat{A}$ and a full-rank constraint matrix $C$ encoding one or more invariants, we show that the matrix closest to $\widehat{A}$ in the Frobenius norm and satisfying $C^\top A = 0$ is the orthogonal projection $A^\star = \widehat{A} - C(C^\top C)^{-1}C^\top \widehat{A}$. This correction is uniquely defined, low rank and fully determined by the violation $C^\top \widehat{A}$. In the single-invariant case it reduces to a rank-one update. We prove that $A^\star$ enforces exact conservation while minimally perturbing the dynamics, and we verify these properties numerically on a Markov-type example. The projection provides an elementary and general mechanism for embedding exact invariants into any learned linear model.
预测|估计(6篇)
【1】DuaDeep-SeqAffinity: Dual-Stream Deep Learning Framework for Sequence-Only Antigen-Antibody Affinity Prediction
标题：DuaDeep-SeqAffinity：用于纯序列抗原-抗体亲和力预测的双流深度学习框架
链接：https://arxiv.org/abs/2512.22007
摘要：预测抗原和抗体之间的结合亲和力是药物发现和疫苗开发的基础。传统的计算方法通常依赖于实验确定的3D结构，这是稀缺的，计算昂贵的获得。本文介绍了DuaDeep-SeqAffinity，这是一种新型的仅序列深度学习框架，它使用双流混合架构仅从氨基酸序列预测亲和力得分。我们的方法利用预先训练的ESM-2蛋白质语言模型嵌入，将用于局部基序检测的1D卷积神经网络（CNN）与用于全局上下文表示的Transformer编码器相结合。随后的融合模块集成这些多方面的特征，然后将其传递到一个完全连接的网络进行最终得分回归。实验结果表明，DuaDeep-SeqAffinity显着优于单个架构组件和现有的最先进的（SOTA）方法。DuaDeep实现了0.688的Pearson相关性，R^2为0.460，均方根误差（RMSE）为0.737，超过了单分支变体ESM-CNN和ESM-Transformer。值得注意的是，该模型的曲线下面积（AUC）为0.890，优于仅序列基准，甚至超过结构-序列混合模型。这些发现证明高保真序列嵌入可以捕获通常保留用于结构建模的基本结合模式。通过消除对3D结构的依赖，DuaDeep-SeqAffinity为大规模序列库的高通量筛选提供了高度可扩展和高效的解决方案，大大加快了治疗发现管道。
摘要：Predicting the binding affinity between antigens and antibodies is fundamental to drug discovery and vaccine development. Traditional computational approaches often rely on experimentally determined 3D structures, which are scarce and computationally expensive to obtain. This paper introduces DuaDeep-SeqAffinity, a novel sequence-only deep learning framework that predicts affinity scores solely from their amino acid sequences using a dual-stream hybrid architecture. Our approach leverages pre-trained ESM-2 protein language model embeddings, combining 1D Convolutional Neural Networks (CNNs) for local motif detection with Transformer encoders for global contextual representation. A subsequent fusion module integrates these multi-faceted features, which are then passed to a fully connected network for final score regression. Experimental results demonstrate that DuaDeep-SeqAffinity significantly outperforms individual architectural components and existing state-of-the-art (SOTA) methods. DuaDeep achieved a superior Pearson correlation of 0.688, an R^2 of 0.460, and a Root Mean Square Error (RMSE) of 0.737, surpassing single-branch variants ESM-CNN and ESM-Transformer. Notably, the model achieved an Area Under the Curve (AUC) of 0.890, outperforming sequence-only benchmarks and even surpassing structure-sequence hybrid models. These findings prove that high-fidelity sequence embeddings can capture essential binding patterns typically reserved for structural modeling. By eliminating the reliance on 3D structures, DuaDeep-SeqAffinity provides a highly scalable and efficient solution for high-throughput screening of vast sequence libraries, significantly accelerating the therapeutic discovery pipeline.
【2】Mechanical Strength Prediction of Steel-Polypropylene Fiber-based High-Performance Concrete Using Hybrid Machine Learning Algorithms
标题：使用混合机器学习算法预测钢-聚丙烯纤维基高性能混凝土的机械强度
链接：https://arxiv.org/abs/2512.21638
备注：28 pages
摘要：本研究开发和评估机器学习模型，以预测钢-聚丙烯纤维增强高性能混凝土（HPC）的力学性能。研究了三个模型家族：XGBoost的Extra Trees（ET-XGB），LightGBM的Random Forest（RF-LGBM）和XGBoost的Transformer（Transformer-XGB）。目标性能包括抗压强度（CS），抗弯强度（FS）和抗拉强度（TS），基于从已发表的实验研究中汇编的广泛数据集。模型训练包括k折交叉验证、超参数优化、Shapley加法解释（SHAP）和不确定性分析，以确保鲁棒性和可解释性。在测试的方法中，ET-XGB模型实现了最高的总体准确度，CS的测试R^2值为0.994，FS为0.944，TS为0.978，CS和TS的不确定性最低（分别约为13-16%和30.4%）。RF-LGBM模型对FS提供了最稳定和可靠的预测（R^2 0.977），对FS产生了最低的不确定性（约5-33%）。Transformer-XGB模型表现出较强的预测能力（TS的R^2为0.978，FS为0.967），但始终显示出最高的不确定性，表明泛化可靠性降低。SHAP分析进一步表明，纤维纵横比（AR 1和AR 2），硅灰（SFU），和钢纤维含量（SF）的强度是最有影响力的预测，而水含量（W）和水胶比（w/b）始终有负面影响。研究结果证实，机器学习模型可以提供准确，可解释和可推广的HPC机械性能预测。这些模型为优化混凝土配合比设计和提高工程应用中的结构性能评估提供了有价值的工具。
摘要：This research develops and evaluates machine learning models to predict the mechanical properties of steel-polypropylene fiber-reinforced high-performance concrete (HPC). Three model families were investigated: Extra Trees with XGBoost (ET-XGB), Random Forest with LightGBM (RF-LGBM), and Transformer with XGBoost (Transformer-XGB). The target properties included compressive strength (CS), flexural strength (FS), and tensile strength (TS), based on an extensive dataset compiled from published experimental studies. Model training involved k-fold cross-validation, hyperparameter optimization, Shapley additive explanations (SHAP), and uncertainty analysis to ensure both robustness and interpretability. Among the tested approaches, the ET-XGB model achieved the highest overall accuracy, with testing R^2 values of 0.994 for CS, 0.944 for FS, and 0.978 for TS and exhibited lowest uncertainty for CS and TS (approximately 13-16% and 30.4%, respectively). The RF-LGBM model provided the most stable and reliable predictions for FS (R^2 0.977), yielding the lowest uncertainty for FS (approximately 5-33%). The Transformer-XGB model demonstrated strong predictive capability (R^2 0.978 for TS and 0.967 for FS) but consistently showed the highest uncertainty, indicating reduced generalization reliability. SHAP analysis further indicated that fiber aspect ratios (AR1 and AR2), silica fume (Sfu), and steel fiber content (SF) were the most influential predictors of strength, whereas water content (W) and the water-binder ratio (w/b) consistently had negative effects. The findings confirm that machine learning models can provide accurate, interpretable, and generalizable predictions of HPC mechanical properties. These models offer valuable tools for optimizing concrete mix design and enhancing structural performance evaluation in engineering applications.
【3】A Data-Driven Multi-Objective Approach for Predicting Mechanical Performance, Flowability, and Porosity in Ultra-High-Performance Concrete (UHPC)
标题：超高性能混凝土（UHPC）力学性能、流动性和孔隙率预测的数据驱动多目标方法
链接：https://arxiv.org/abs/2512.21610
备注：20 pages
摘要：本研究提出了一种数据驱动的多目标方法来预测超高性能混凝土（UHPC）的力学性能，流动性和孔隙率。在测试的21种机器学习算法中，选择了5种高性能模型，其中XGBoost在使用随机搜索和K-Fold交叉验证进行超参数调整后显示出最佳精度。该框架遵循两个阶段的过程：使用原始数据构建初始XGBoost模型，一旦被选为最终模型，数据集将通过以下方式进行清理：（1）删除多共线特征，（2）使用隔离森林识别离群值，以及（3）使用SHAP分析选择重要特征。然后使用模型2的细化数据集重新训练XGBoost，从而在所有输出中实现高预测精度。一个图形用户界面（GUI）也被开发，以支持材料设计师。总体而言，建议的框架显着提高了预测精度，并最大限度地减少了在UHPC配合比设计的广泛的实验测试的需要。
摘要：This study presents a data-driven, multi-objective approach to predict the mechanical performance, flow ability, and porosity of Ultra-High-Performance Concrete (UHPC). Out of 21 machine learning algorithms tested, five high-performing models are selected, with XGBoost showing the best accuracy after hyperparameter tuning using Random Search and K-Fold Cross-Validation. The framework follows a two-stage process: the initial XGBoost model is built using raw data, and once selected as the final model, the dataset is cleaned by (1) removing multicollinear features, (2) identifying outliers with Isolation Forest, and (3) selecting important features using SHAP analysis. The refined dataset as model 2 is then used to retrain XGBoost, which achieves high prediction accuracy across all outputs. A graphical user interface (GUI) is also developed to support material designers. Overall, the proposed framework significantly improves the prediction accuracy and minimizes the need for extensive experimental testing in UHPC mix design.
【4】RefineBridge: Generative Bridge Models Improve Financial Forecasting by Foundation Models
标题：RefineBridge：生成式桥梁模型通过基础模型改进财务预测
链接：https://arxiv.org/abs/2512.21572
摘要：金融时间序列预测是特别具有挑战性的基于变压器的时间序列基础模型（TSFM），由于非平稳性，重尾分布和高频噪声存在于数据中。低秩自适应（LoRA）已经成为一种流行的参数有效的方法，用于将预训练的TSFM适应下游数据域。然而，它在财务数据方面仍然表现不佳，因为它保留了TSFM的网络架构和训练目标，而不是补充基础模型。为了进一步增强TSFMs，我们提出了一种新的细化模块，RefineBridge，建立在一个易于处理的薛定谔桥（SB）生成框架。考虑到TSFM的预测作为生成先验和观察到的地面实况作为目标，RefineBridge学习上下文条件随机传输图以改进TSFM预测，即使是低质量的先验也迭代地接近地面实况目标。对多个金融基准的模拟表明，RefineBridge在不同的预测范围内始终如一地提高了最先进的TSFM的性能。
摘要：Financial time series forecasting is particularly challenging for transformer-based time series foundation models (TSFMs) due to non-stationarity, heavy-tailed distributions, and high-frequency noise present in data. Low-rank adaptation (LoRA) has become a popular parameter-efficient method for adapting pre-trained TSFMs to downstream data domains. However, it still underperforms in financial data, as it preserves the network architecture and training objective of TSFMs rather than complementing the foundation model. To further enhance TSFMs, we propose a novel refinement module, RefineBridge, built upon a tractable Schrödinger Bridge (SB) generative framework. Given the forecasts of TSFM as generative prior and the observed ground truths as targets, RefineBridge learns context-conditioned stochastic transport maps to improve TSFM predictions, iteratively approaching the ground-truth target from even a low-quality prior. Simulations on multiple financial benchmarks demonstrate that RefineBridge consistently improves the performance of state-of-the-art TSFMs across different prediction horizons.
【5】DeepCQ: General-Purpose Deep-Surrogate Framework for Lossy Compression Quality Prediction
标题：DeepCQ：用于有损压缩质量预测的通用深度代理框架
链接：https://arxiv.org/abs/2512.21433
摘要：鉴于现代科学模拟和仪器产生的数据量不断增加，误差有限的有损压缩技术对于科学数据管理和分析至关重要。然而，由于度量计算的密集性质，评估压缩后的数据质量仍然在计算上昂贵。在这项工作中，我们提出了一个通用的深度代理框架有损压缩质量预测（DeepCQ），具有以下关键贡献：1）我们开发了一个代理模型的压缩质量预测，可推广到不同的错误有界有损压缩器，质量度量，和输入数据集; 2）我们采用了一种新颖的两阶段设计，将计算量大的特征提取阶段与轻量级指标预测相结合，从而实现了有效的训练和模块化推理; 3）我们使用专家混合设计优化模型在时间演化数据上的性能。这种设计增强了跨仿真时间步进行预测时的鲁棒性，特别是当训练和测试数据表现出显著变化时。我们验证了DeepCQ在四个现实科学应用中的有效性。我们的研究结果突出了该框架卓越的预测准确性，在大多数情况下，预测误差通常低于10%，显着优于现有的方法。我们的框架使科学用户能够根据他们偏好的数据质量对数据压缩做出明智的决定，从而显着减少科学数据分析中的I/O和计算开销。
摘要：Error-bounded lossy compression techniques have become vital for scientific data management and analytics, given the ever-increasing volume of data generated by modern scientific simulations and instruments. Nevertheless, assessing data quality post-compression remains computationally expensive due to the intensive nature of metric calculations. In this work, we present a general-purpose deep-surrogate framework for lossy compression quality prediction (DeepCQ), with the following key contributions: 1) We develop a surrogate model for compression quality prediction that is generalizable to different error-bounded lossy compressors, quality metrics, and input datasets; 2) We adopt a novel two-stage design that decouples the computationally expensive feature-extraction stage from the light-weight metrics prediction, enabling efficient training and modular inference; 3) We optimize the model performance on time-evolving data using a mixture-of-experts design. Such a design enhances the robustness when predicting across simulation timesteps, especially when the training and test data exhibit significant variation. We validate the effectiveness of DeepCQ on four real-world scientific applications. Our results highlight the framework's exceptional predictive accuracy, with prediction errors generally under 10\% across most settings, significantly outperforming existing methods. Our framework empowers scientific users to make informed decisions about data compression based on their preferred data quality, thereby significantly reducing I/O and computational overhead in scientific data analysis.
【6】Dynamic Attention (DynAttn): Interpretable High-Dimensional Spatio-Temporal Forecasting (with Application to Conflict Fatalities)
标题：动态注意力（DynAttn）：可解释的多维时空预测（应用于冲突死亡）
链接：https://arxiv.org/abs/2512.21435
摘要：由于暴力数据的稀疏性、突发性和高度非平稳性，预测与冲突有关的死亡人数仍然是政治学和政策分析的核心挑战。我们介绍DynAttn，一个可解释的动态注意力预测框架，用于高维时空计数过程。DynAttn结合了滚动窗口估计，共享弹性网络特征门控，紧凑的权重绑定自注意编码器和零膨胀负二项（ZINB）似然。该架构可对预期伤亡和伤亡概率进行校准的多水平预测，同时通过特征门、消融分析和弹性措施保持透明的诊断。 我们使用来自VIEWS预测系统的全球国家级和高分辨率PRIO网格级冲突数据来评估DynAttn，并将其与已建立的统计和机器学习方法进行基准测试，包括DynENet，LSTM，Prophet，PatchTST和官方VIEWS基线。在1至12个月的预测范围内，DynAttn始终实现了更高的预测准确性，在稀疏网格级别设置中，竞争模型往往变得不稳定或急剧下降。 除了预测性能，DynAttn还可以对区域冲突动态进行结构化解释。在我们的应用中，跨区域分析表明，短期冲突持续性和空间扩散形成了核心预测骨干，而气候压力则取决于冲突地区，既可以作为条件放大器，也可以作为主要驱动因素。
摘要 ：Forecasting conflict-related fatalities remains a central challenge in political science and policy analysis due to the sparse, bursty, and highly non-stationary nature of violence data. We introduce DynAttn, an interpretable dynamic-attention forecasting framework for high-dimensional spatio-temporal count processes. DynAttn combines rolling-window estimation, shared elastic-net feature gating, a compact weight-tied self-attention encoder, and a zero-inflated negative binomial (ZINB) likelihood. This architecture produces calibrated multi-horizon forecasts of expected casualties and exceedance probabilities, while retaining transparent diagnostics through feature gates, ablation analysis, and elasticity measures. We evaluate DynAttn using global country-level and high-resolution PRIO-grid-level conflict data from the VIEWS forecasting system, benchmarking it against established statistical and machine-learning approaches, including DynENet, LSTM, Prophet, PatchTST, and the official VIEWS baseline. Across forecast horizons from one to twelve months, DynAttn consistently achieves substantially higher predictive accuracy, with particularly large gains in sparse grid-level settings where competing models often become unstable or degrade sharply. Beyond predictive performance, DynAttn enables structured interpretation of regional conflict dynamics. In our application, cross-regional analyses show that short-run conflict persistence and spatial diffusion form the core predictive backbone, while climate stress acts either as a conditional amplifier or a primary driver depending on the conflict theater.
其他神经网络|深度学习|模型|建模(17篇)
【1】Why Smooth Stability Assumptions Fail for ReLU Learning
标题：为什么ReLU学习的平滑稳定性假设失败
链接：https://arxiv.org/abs/2512.22055
摘要：现代学习系统的稳定性分析经常是在光滑性假设下推导出来的，这些假设被ReLU型非线性所违反。在本文中，我们通过证明没有基于均匀平滑度的稳定性代理（如梯度Lipschitzness或Hessian控制）可以全局保持ReLU网络，即使在训练轨迹显示经验稳定的简单设置中，也可以隔离最小的障碍。我们给出了一个具体的反例，证明了经典的稳定性界限的失败，并确定了一个最小的广义导数条件下，稳定性报表可以有意义地恢复。结果澄清了为什么ReLU的平滑近似可能会误导并激励非平滑感知的稳定性框架。
摘要：Stability analyses of modern learning systems are frequently derived under smoothness assumptions that are violated by ReLU-type nonlinearities. In this note, we isolate a minimal obstruction by showing that no uniform smoothness-based stability proxy such as gradient Lipschitzness or Hessian control can hold globally for ReLU networks, even in simple settings where training trajectories appear empirically stable. We give a concrete counterexample demonstrating the failure of classical stability bounds and identify a minimal generalized derivative condition under which stability statements can be meaningfully restored. The result clarifies why smooth approximations of ReLU can be misleading and motivates nonsmooth-aware stability frameworks.
【2】LibContinual: A Comprehensive Library towards Realistic Continual Learning
标题：LibContinual：面向现实持续学习的综合图书馆
链接：https://arxiv.org/abs/2512.22029
摘要：持续学习（CL）的一个基本挑战是灾难性遗忘，适应新任务会降低先前任务的性能。虽然该领域已经发展了多种方法，但这种不同方法的快速激增最终导致了碎片化的研究景观。缺乏统一的框架，包括不一致的实现，相互冲突的依赖关系和不同的评估协议，使得公平的比较和可重复的研究越来越困难。为了应对这一挑战，我们提出了LibContinual，一个全面的和可重复的库，旨在作为一个基础平台，现实CL。LibContinual建立在一个高内聚、低耦合的模块化架构之上，它集成了五个主要方法类别中的19个代表性算法，提供了一个标准化的执行环境。同时，利用这个统一的框架，我们系统地识别和研究了主流评估中普遍存在的三个隐含假设：（1）离线数据可访问性，（2）不受管制的记忆资源，（3）任务内语义同质性。我们认为，这些假设往往高估了CL方法在现实世界中的适用性。通过我们的全面分析，使用严格的在线CL设置，一种新的统一内存预算协议，并提出了类别随机设置，我们发现显着的性能下降，在许多代表性的CL方法时，受到这些现实世界的限制。我们的研究强调了资源感知和语义强大的CL策略的必要性，并提供LibContinual作为未来现实的持续学习研究的基础工具包。源代码可从\href{https：//github.com/RL-VIG/LibContinual}{https：//github.com/RL-VIG/LibContinual}获得。
摘要：A fundamental challenge in Continual Learning (CL) is catastrophic forgetting, where adapting to new tasks degrades the performance on previous ones. While the field has evolved with diverse methods, this rapid surge in diverse methodologies has culminated in a fragmented research landscape. The lack of a unified framework, including inconsistent implementations, conflicting dependencies, and varying evaluation protocols, makes fair comparison and reproducible research increasingly difficult. To address this challenge, we propose LibContinual, a comprehensive and reproducible library designed to serve as a foundational platform for realistic CL. Built upon a high-cohesion, low-coupling modular architecture, LibContinual integrates 19 representative algorithms across five major methodological categories, providing a standardized execution environment. Meanwhile, leveraging this unified framework, we systematically identify and investigate three implicit assumptions prevalent in mainstream evaluation: (1) offline data accessibility, (2) unregulated memory resources, and (3) intra-task semantic homogeneity. We argue that these assumptions often overestimate the real-world applicability of CL methods. Through our comprehensive analysis using strict online CL settings, a novel unified memory budget protocol, and a proposed category-randomized setting, we reveal significant performance drops in many representative CL methods when subjected to these real-world constraints. Our study underscores the necessity of resource-aware and semantically robust CL strategies, and offers LibContinual as a foundational toolkit for future research in realistic continual learning. The source code is available from \href{https://github.com/RL-VIG/LibContinual}{https://github.com/RL-VIG/LibContinual}.
【3】HWL-HIN: A Hypergraph-Level Hypergraph Isomorphism Network as Powerful as the Hypergraph Weisfeiler-Lehman Test with Application to Higher-Order Network Robustness
标题：HWL-HIN：与超图Weisfeiler-Lehman测试一样强大的超图级超图同质网络及其在更高级网络鲁棒性中的应用
链接：https://arxiv.org/abs/2512.22014
摘要：复杂系统的鲁棒性具有重要的工程和经济意义。然而，传统的基于攻击的后验鲁棒性评估会导致过高的计算开销。最近，卷积神经网络（CNN）和图神经网络（GNN）等深度学习方法已被广泛用作快速鲁棒性预测的替代方法。然而，这些方法忽略了现实世界系统中普遍存在的复杂高阶相关性，这些系统自然地被建模为超图。虽然超图神经网络（HGNNs）已被广泛用于超图学习，但其拓扑表达能力尚未达到理论上的上限。为了解决这个问题，受图同构网络的启发，本文提出了一个超图级超图同构网络框架。理论上，该方法被证明具有严格等效于超图Weisfeiler-Lehman测试的表达能力，并被应用于预测超图的鲁棒性。实验结果表明，在保持训练和预测效率的同时，所提出的方法不仅优于现有的基于图的模型，而且在优先考虑拓扑结构表示的任务中显着优于传统的HGNN。
摘要：Robustness in complex systems is of significant engineering and economic importance. However, conventional attack-based a posteriori robustness assessments incur prohibitive computational overhead. Recently, deep learning methods, such as Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs), have been widely employed as surrogates for rapid robustness prediction. Nevertheless, these methods neglect the complex higher-order correlations prevalent in real-world systems, which are naturally modeled as hypergraphs. Although Hypergraph Neural Networks (HGNNs) have been widely adopted for hypergraph learning, their topological expressive power has not yet reached the theoretical upper bound. To address this limitation, inspired by Graph Isomorphism Networks, this paper proposes a hypergraph-level Hypergraph Isomorphism Network framework. Theoretically, this approach is proven to possess an expressive power strictly equivalent to the Hypergraph Weisfeiler-Lehman test and is applied to predict hypergraph robustness. Experimental results demonstrate that while maintaining superior efficiency in training and prediction, the proposed method not only outperforms existing graph-based models but also significantly surpasses conventional HGNNs in tasks that prioritize topological structure representation.
【4】GQ-VAE: A gated quantized VAE for learning variable length tokens
标题：GQ-VAE：一种用于学习变长标记的门控量化VAE
链接：https://arxiv.org/abs/2512.21913
摘要：虽然大多数前沿模型仍然使用确定性的基于频率的令牌化算法，如字节对编码（BPE），但最近有大量工作来设计学习神经令牌化器。然而，这些方案通常会增加底层语言模型的复杂性，并迫使架构发生重大变化，使其难以大规模实现。为了克服这些挑战，我们提出了门控量化变分自编码器（GQ-VAE），这是一种新的架构，可以独立地进行预训练，作为现有标记器的替代品。该架构的关键创新在于学习对可变长度离散令牌进行编码。与标准VQ-VAE分词器相比，GQ-VAE提高了压缩和语言建模性能，并接近BPE的压缩率和语言建模性能。有趣的是，如果我们使用具有较小词汇量的BPE，使得GQ-VAE和BPE之间的压缩是等效的，我们发现GQ-VAE改善了下游语言模型学习。最后，我们讨论了未来工作的几个令人兴奋的途径。代码可以在https://github.com/Theo-Datta-115/gq-vae上找到。
摘要：While most frontier models still use deterministic frequency-based tokenization algorithms such as byte-pair encoding (BPE), there has been significant recent work to design learned neural tokenizers. However, these schemes generally add to underlying language model complexity and force large changes to architecture, making them hard to implement at large scales. To overcome these challenges, we propose the gated quantized variational autoencoder (GQ-VAE), a novel architecture that can be independently pre-trained to serve as a drop-in replacement for existing tokenizers. The key innovation of the architecture is to learn to encode variable-length discrete tokens. GQ-VAE improves compression and language modeling performance over a standard VQ-VAE tokenizer, and approaches the compression rate and language modeling performance of BPE. Interestingly, if we use BPE with a smaller vocabulary, such that the compression is equivalent between GQ-VAE and BPE, we find that GQ-VAE improves downstream language model learning. We conclude with a discussion of several exciting avenues for future work. Code can be found at https://github.com/Theo-Datta-115/gq-vae.
【5】Balancing Accuracy and Efficiency: CNN Fusion Models for Diabetic Retinopathy Screening
标题：平衡准确性和效率：用于糖尿病视网膜病筛查的CNN融合模型
链接：https://arxiv.org/abs/2512.21861
摘要：糖尿病视网膜病变（DR）仍然是可预防失明的主要原因，但大规模筛查受到有限的专家可用性以及设备和人群之间的图像质量差异的限制。这项工作研究了互补卷积神经网络（CNN）主干的特征级融合是否可以在全球来源的眼底图像上提供准确和有效的二进制DR筛查。使用来自五个公共数据集（APTOS，EyePACS，IDRiD，Messidor和Oscillator）的11，156张图像，我们将DR检测视为二进制分类任务，并将三个预训练模型（ResNet 50，EfficientNet-B 0和DenseNet 121）与成对和三融合变体进行比较。在五次独立运行中，融合始终优于单一主干。EfficientNet-B 0 + DenseNet 121（Eff+Den）融合模型实现了最佳的总体平均性能（准确度：82.89%），对于正常（83.60%）和糖尿病（82.60%）病例具有平衡的类间F1分数。虽然三融合是有竞争力的，但它会导致更高的计算成本。推理分析突出了一个实际的权衡：EfficientNet-B 0是最快的（在批量大小为1000时约为1.16 ms/图像），而Eff+Den融合提供了有利的准确性-延迟平衡。这些发现表明，轻量级特征融合可以增强异构数据集的泛化能力，支持可扩展的二进制DR筛选工作流程，其中准确性和吞吐量都很关键。
摘要：Diabetic retinopathy (DR) remains a leading cause of preventable blindness, yet large-scale screening is constrained by limited specialist availability and variable image quality across devices and populations. This work investigates whether feature-level fusion of complementary convolutional neural network (CNN) backbones can deliver accurate and efficient binary DR screening on globally sourced fundus images. Using 11,156 images pooled from five public datasets (APTOS, EyePACS, IDRiD, Messidor, and ODIR), we frame DR detection as a binary classification task and compare three pretrained models (ResNet50, EfficientNet-B0, and DenseNet121) against pairwise and tri-fusion variants. Across five independent runs, fusion consistently outperforms single backbones. The EfficientNet-B0 + DenseNet121 (Eff+Den) fusion model achieves the best overall mean performance (accuracy: 82.89\%) with balanced class-wise F1-scores for normal (83.60\%) and diabetic (82.60\%) cases. While the tri-fusion is competitive, it incurs a substantially higher computational cost. Inference profiling highlights a practical trade-off: EfficientNet-B0 is the fastest (approximately 1.16 ms/image at batch size 1000), whereas the Eff+Den fusion offers a favorable accuracy--latency balance. These findings indicate that lightweight feature fusion can enhance generalization across heterogeneous datasets, supporting scalable binary DR screening workflows where both accuracy and throughput are critical.
【6】Scalable Class-Incremental Learning Based on Parametric Neural Collapse
标题：基于参数神经崩溃的可扩展类增量学习
链接：https://arxiv.org/abs/2512.21845
备注：42 pages, 8 figures, submitted to Pattern Recognition (PR)
摘要：增量学习经常遇到挑战，例如过度拟合新数据和灾难性地遗忘旧数据。现有的方法可以有效地扩展模型以适应新的任务，同时冻结旧模型的参数，但忽略了结构效率的必要性，导致模块之间的功能差异和类的不对齐，由于不断变化的类分布。为了解决这些问题，我们提出了基于参数神经崩溃（SCL-PNC）的可扩展类增量学习，通过适配层实现需求驱动的，最小成本的骨干扩展，并根据增量类将静态细化为动态参数等角紧框架（ETF）框架。该方法可以有效地处理现实场景中类别数量不断增加的模型扩展问题。此外，为了抵消串行扩展模型中的特征漂移，提出了并行扩展框架，并使用知识蒸馏算法来对齐扩展模块之间的特征。因此，SCL-PNC不仅可以设计一个动态的和可扩展的ETF分类器，以解决由于不断变化的类分布的类不对齐，但也确保了扩展模块之间的知识蒸馏的适配层的特征一致性。通过利用神经崩溃，SCL-PNC通过可扩展骨干、适配层和参数ETF分类器的结构化组合来诱导增量扩展模型的收敛。在标准基准测试上的实验证明了该方法的有效性和效率。我们的代码可以在https://github.com/zhangchuangxin71-cyber/dynamic_ ETF 2上找到。关键词：类增量学习;灾难性遗忘;神经崩溃;知识蒸馏;扩展模型。
摘要：Incremental learning often encounter challenges such as overfitting to new data and catastrophic forgetting of old data. Existing methods can effectively extend the model for new tasks while freezing the parameters of the old model, but ignore the necessity of structural efficiency to lead to the feature difference between modules and the class misalignment due to evolving class distributions. To address these issues, we propose scalable class-incremental learning based on parametric neural collapse (SCL-PNC) that enables demand-driven, minimal-cost backbone expansion by adapt-layer and refines the static into a dynamic parametric Equiangular Tight Frame (ETF) framework according to incremental class. This method can efficiently handle the model expansion question with the increasing number of categories in real-world scenarios. Additionally, to counteract feature drift in serial expansion models, the parallel expansion framework is presented with a knowledge distillation algorithm to align features across expansion modules. Therefore, SCL-PNC can not only design a dynamic and extensible ETF classifier to address class misalignment due to evolving class distributions, but also ensure feature consistency by an adapt-layer with knowledge distillation between extended modules. By leveraging neural collapse, SCL-PNC induces the convergence of the incremental expansion model through a structured combination of the expandable backbone, adapt-layer, and the parametric ETF classifier. Experiments on standard benchmarks demonstrate the effectiveness and efficiency of our proposed method. Our code is available at https://github.com/zhangchuangxin71-cyber/dynamic_ ETF2. Keywords: Class incremental learning; Catastrophic forgetting; Neural collapse;Knowledge distillation; Expanded model.
【7】Approximation Capabilities of Feedforward Neural Networks with GELU Activations
标题：具有GELU激活的前向神经网络的逼近能力
链接：https://arxiv.org/abs/2512.21749
备注 ：42 pages
摘要：我们推导出一个近似误差界，同时举行的功能和它的所有衍生物的任何规定的顺序。边界适用于初等函数，包括多元多项式，指数函数和倒数函数，并使用高斯误差线性单元（GELU）激活的前馈神经网络获得。此外，我们还报告了网络的大小，权重大小和无穷大时的行为。我们的分析开始于一个建设性的近似乘法，在那里我们证明了一个给定的近似的同时有效性的误差界域的大小不断增加。利用这一结果，我们获得了除法和指数函数的近似保证，确保所得近似器的所有高阶导数保持全局有界。
摘要：We derive an approximation error bound that holds simultaneously for a function and all its derivatives up to any prescribed order. The bounds apply to elementary functions, including multivariate polynomials, the exponential function, and the reciprocal function, and are obtained using feedforward neural networks with the Gaussian Error Linear Unit (GELU) activation. In addition, we report the network size, weight magnitudes, and behavior at infinity. Our analysis begins with a constructive approximation of multiplication, where we prove the simultaneous validity of error bounds over domains of increasing size for a given approximator. Leveraging this result, we obtain approximation guarantees for division and the exponential function, ensuring that all higher-order derivatives of the resulting approximators remain globally bounded.
【8】A Model of Causal Explanation on Neural Networks for Tabular Data
标题：表格数据神经网络因果解释模型
链接：https://arxiv.org/abs/2512.21746
备注：\c{opyright}2025. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
摘要：解释机器学习方法产生的结果的问题继续引起人们的关注。神经网络（NN）模型，以及梯度提升机，预计将被用于即使在具有高预测精度的表格数据。本研究针对NN预测器中表格数据的伪相关性、因果关系和组合原因等相关问题。我们提出了一个因果解释方法，CENNET，和一个新的解释力指数，使用熵的方法。CENNET为NN的预测提供了因果解释，并使用了与NN有效结合的结构因果模型（SCM），尽管SCM在预测准确性方面通常不单独用作预测模型。我们表明，CEN-NET提供了这样的解释，通过比较实验与现有的方法在分类任务的合成和准真实数据。
摘要：The problem of explaining the results produced by machine learning methods continues to attract attention. Neural network (NN) models, along with gradient boosting machines, are expected to be utilized even in tabular data with high prediction accuracy. This study addresses the related issues of pseudo-correlation, causality, and combinatorial reasons for tabular data in NN predictors. We propose a causal explanation method, CENNET, and a new explanation power index using entropy for the method. CENNET provides causal explanations for predictions by NNs and uses structural causal models (SCMs) effectively combined with the NNs although SCMs are usually not used as predictive models on their own in terms of predictive accuracy. We show that CEN-NET provides such explanations through comparative experiments with existing methods on both synthetic and quasi-real data in classification tasks.
【9】A Unified Definition of Hallucination, Or: It's the World Model, Stupid
标题：幻觉的统一定义，或者：这是世界模式，愚蠢
链接：https://arxiv.org/abs/2512.21577
摘要：尽管自神经语言模型诞生以来，人们多次尝试解决幻觉问题，但时至今日，即使是前沿的大型语言模型，它仍然是一个问题。为什么会这样呢？我们从历史的角度出发，通过文献中使用的幻觉定义，直到今天，并将它们折叠成幻觉的单一定义，其中不同的先前定义集中在我们定义的不同方面。在其核心，我们认为，幻觉只是不准确的（内部）世界建模，在一种形式，它是可观察到的用户（例如，陈述与知识库相矛盾的事实，或产生与已知来源相矛盾的摘要）。通过改变参考世界模型以及知识冲突策略（例如，知识库与上下文），我们得出了文献中存在的幻觉的不同现有定义。 我们认为，这种统一的观点是有用的，因为它迫使评估，以明确他们假设的“世界”或真理的来源，澄清什么应该和不应该被称为幻觉（而不是规划或奖励/激励相关的错误），并提供了一个共同的语言来比较基准和缓解技术。在此定义的基础上，我们概述了一系列基准测试的计划，在这些基准测试中，幻觉被定义为与不同环境中的合成但完全指定的世界模型不匹配，并概述了这些基准测试如何使用这些设置来进行压力测试和改进语言模型的世界建模组件。
摘要：Despite numerous attempts to solve the issue of hallucination since the inception of neural language models, it remains a problem in even frontier large language models today. Why is this the case? We walk through definitions of hallucination used in the literature from a historical perspective up to the current day, and fold them into a single definition of hallucination, wherein different prior definitions focus on different aspects of our definition. At its core, we argue that hallucination is simply inaccurate (internal) world modeling, in a form where it is observable to the user (e.g., stating a fact which contradicts a knowledge base, or producing a summary which contradicts a known source). By varying the reference world model as well as the knowledge conflict policy (e.g., knowledge base vs. in-context), we arrive at the different existing definitions of hallucination present in the literature. We argue that this unified view is useful because it forces evaluations to make clear their assumed "world" or source of truth, clarifies what should and should not be called hallucination (as opposed to planning or reward/incentive-related errors), and provides a common language to compare benchmarks and mitigation techniques. Building on this definition, we outline plans for a family of benchmarks in which hallucinations are defined as mismatches with synthetic but fully specified world models in different environments, and sketch out how these benchmarks can use such settings to stress-test and improve the world modeling components of language models.
【10】When Bayesian Tensor Completion Meets Multioutput Gaussian Processes: Functional Universality and Rank Learning
标题：当Bayesian张量完成遇到多输出高斯过程时：函数普遍性和等级学习
链接：https://arxiv.org/abs/2512.21486
摘要：函数张量分解可以用实值索引分析多维数据，为机器学习和信号处理中的应用铺平了道路。现有方法的一个局限性是假设张量秩-一个决定模型复杂性的关键参数-是已知的。然而，确定最佳秩是一个非确定性的多项式时间困难（NP-困难）的任务，并且对于连续信号的函数低秩张量模型的表达能力的理解有限。我们提出了一个秩揭示功能贝叶斯张量完成（RR-FBTC）方法。通过精心设计的多输出高斯过程对潜在函数进行建模，RR-FBTC处理具有实值索引的张量，同时在推理过程中实现自动张量秩确定。我们建立了该模型对连续多维信号的普适逼近性质，以简洁的格式展示了其表达能力。为了学习这个模型，我们采用变分推理框架，并推导出一个高效的算法与封闭形式的更新。在合成和真实世界数据集上的实验证明了RR-FBTC的有效性和优越性。该代码可在https://github.com/OceanSTARLab/RR-FBTC上获得。
摘要：Functional tensor decomposition can analyze multi-dimensional data with real-valued indices, paving the path for applications in machine learning and signal processing. A limitation of existing approaches is the assumption that the tensor rank-a critical parameter governing model complexity-is known. However, determining the optimal rank is a non-deterministic polynomial-time hard (NP-hard) task and there is a limited understanding regarding the expressive power of functional low-rank tensor models for continuous signals. We propose a rank-revealing functional Bayesian tensor completion (RR-FBTC) method. Modeling the latent functions through carefully designed multioutput Gaussian processes, RR-FBTC handles tensors with real-valued indices while enabling automatic tensor rank determination during the inference process. We establish the universal approximation property of the model for continuous multi-dimensional signals, demonstrating its expressive power in a concise format. To learn this model, we employ the variational inference framework and derive an efficient algorithm with closed-form updates. Experiments on both synthetic and real-world datasets demonstrate the effectiveness and superiority of the RR-FBTC over state-of-the-art approaches. The code is available at https://github.com/OceanSTARLab/RR-FBTC.
【11】Statistical vs. Deep Learning Models for Estimating Substance Overdose Excess Mortality in the US
标题：用于估计美国物质过量过量死亡率的统计与深度学习模型
链接：https://arxiv.org/abs/2512.21456
摘要：2023年，美国药物过量死亡人数超过80，000人，而COVID-19疫情通过医疗中断和行为变化加剧了现有趋势。估计超额死亡率（定义为根据大流行前的模式超过预期水平的死亡）对于了解大流行的影响和制定干预战略至关重要。然而，传统的统计方法，如SARIMA假设线性，平稳性和固定的季节性，这可能不成立下的结构性中断。我们使用国家CDC数据（2015-2019年用于训练/验证，2020-2023年用于预测），系统地比较了SARIMA与三种深度学习（DL）架构（LSTM，Seq 2Seq和Transformer）的反事实死亡率估计。我们提供了经验证据，证明LSTM在政权变化下进行预测时实现了更好的点估计（17.08% MAPE vs. 23.88% SARIMA）和更好的校准不确定性（68.8% vs. 47.9%预测区间覆盖率）。我们还证明，基于注意力的模型（Seq 2 Seq、Transformer）表现不佳是由于过度拟合历史均值而不是捕捉紧急趋势。我们的可重复管道结合了每个配置60多个试验的共形预测区间和收敛分析，我们提供了一个可与15个州卫生部门部署的开源框架。我们的研究结果表明，经过仔细验证的DL模型可以为公共卫生规划提供比传统方法更可靠的反事实估计，同时强调在高风险领域部署神经预测时需要校准技术。
摘要：Substance overdose mortality in the United States claimed over 80,000 lives in 2023, with the COVID-19 pandemic exacerbating existing trends through healthcare disruptions and behavioral changes. Estimating excess mortality, defined as deaths beyond expected levels based on pre-pandemic patterns, is essential for understanding pandemic impacts and informing intervention strategies. However, traditional statistical methods like SARIMA assume linearity, stationarity, and fixed seasonality, which may not hold under structural disruptions. We present a systematic comparison of SARIMA against three deep learning (DL) architectures (LSTM, Seq2Seq, and Transformer) for counterfactual mortality estimation using national CDC data (2015-2019 for training/validation, 2020-2023 for projection). We contribute empirical evidence that LSTM achieves superior point estimation (17.08% MAPE vs. 23.88% for SARIMA) and better-calibrated uncertainty (68.8% vs. 47.9% prediction interval coverage) when projecting under regime change. We also demonstrate that attention-based models (Seq2Seq, Transformer) underperform due to overfitting to historical means rather than capturing emergent trends. Ourreproducible pipeline incorporates conformal prediction intervals and convergence analysis across 60+ trials per configuration, and we provide an open-source framework deployable with 15 state health departments. Our findings establish that carefully validated DL models can provide more reliable counterfactual estimates than traditional methods for public health planning, while highlighting the need for calibration techniques when deploying neural forecasting in high-stakes domains.
【12】An Equivariance Toolbox for Learning Dynamics
标题：学习动态的等方差指标
链接：https://arxiv.org/abs/2512.21447
摘要：深度学习中的许多理论结果可以追溯到参数变换下神经网络的对称性或等变性。然而，现有的分析通常是针对具体问题的，并侧重于一阶的后果，如守恒定律，而对二阶结构的影响仍然不太了解。我们开发了一个通用的等方差工具箱，产生耦合的第一和第二阶的学习动态约束。该框架在三个方向上扩展了经典的Noether型分析：从梯度约束到Hessian约束，从对称到一般等方差，从连续到离散变换。在一阶，我们的框架统一的守恒定律和隐偏关系作为一个单一的身份的特殊情况。在二阶，它提供了关于曲率的结构预测：哪些方向是平坦的或尖锐的，梯度如何与Hessian特征空间对齐，以及损失景观几何如何反映底层的变换结构。我们通过几个应用程序来说明该框架，恢复已知结果，同时还推导出将变换结构与有关优化几何的现代经验观察联系起来的新特征。
摘要：Many theoretical results in deep learning can be traced to symmetry or equivariance of neural networks under parameter transformations. However, existing analyses are typically problem-specific and focus on first-order consequences such as conservation laws, while the implications for second-order structure remain less understood. We develop a general equivariance toolbox that yields coupled first- and second-order constraints on learning dynamics. The framework extends classical Noether-type analyses in three directions: from gradient constraints to Hessian constraints, from symmetry to general equivariance, and from continuous to discrete transformations. At the first order, our framework unifies conservation laws and implicit-bias relations as special cases of a single identity. At the second order, it provides structural predictions about curvature: which directions are flat or sharp, how the gradient aligns with Hessian eigenspaces, and how the loss landscape geometry reflects the underlying transformation structure. We illustrate the framework through several applications, recovering known results while also deriving new characterizations that connect transformation structure to modern empirical observations about optimization geometry.
【13】kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learning
标题：kooplarn：Scikit-Learn兼容的进化运算符学习算法库
链接：https://arxiv.org/abs/2512.21409
摘要：kooplearn是一个机器学习库，它实现了动态算子及其谱分解的线性、内核和深度学习估计器。kooplearn可以模拟离散时间演化算子（Koopman/Transfer）和连续时间无穷小生成器。通过学习这些算子，用户可以通过谱方法分析动力系统，导出数据驱动的降阶模型，并预测未来的状态和可观测量。kooplearn的界面符合scikit-learn API，便于其集成到现有的机器学习和数据科学工作流程中。此外，kooplearn还包括精心策划的基准数据集，以支持实验，可重复性和学习算法的公平比较。该软件可在https://github.com/Machine-Learning-Dynamical-Systems/kooplearn上获得。
摘要：kooplearn is a machine-learning library that implements linear, kernel, and deep-learning estimators of dynamical operators and their spectral decompositions. kooplearn can model both discrete-time evolution operators (Koopman/Transfer) and continuous-time infinitesimal generators. By learning these operators, users can analyze dynamical systems via spectral methods, derive data-driven reduced-order models, and forecast future states and observables. kooplearn's interface is compliant with the scikit-learn API, facilitating its integration into existing machine learning and data science workflows. Additionally, kooplearn includes curated benchmark datasets to support experimentation, reproducibility, and the fair comparison of learning algorithms. The software is available at https://github.com/Machine-Learning-Dynamical-Systems/kooplearn.
【14】Learning to Reconfigure: Using Device Status to Select the Right Constrained Coding Scheme
标题：学习重新配置：使用设备状态选择正确的约束编码方案
链接：https://arxiv.org/abs/2512.21396
备注：13 pages (double column), 4 figures, submitted to the IEEE Transactions on Communications (TCOM)
摘要 ：在数据革命时代，现代存储或传输系统通常需要不同级别的保护。例如，在现代存储系统中，当设备是新的时，用于强化数据的编码技术不能与设备老化时使用的编码技术相同。因此，提供可重新配置的编码方案和设计一种有效的方式来执行这种重新配置是延长设备寿命的关键。我们专注于新兴二维磁记录（TDMR）技术的约束编码方案。最近，我们已经设计了有效的字典有序约束（LOCO）编码方案的各个阶段的TDMR设备的寿命，专注于消除隔离模式，并通过使用它们表现出显着的收益。LOCO代码是自然可重构的，我们利用这一功能在我们的工作。业界采用的基于预定时间戳的重新配置忽略了实际设备状态。相反，我们提出了离线和在线学习方法来执行此任务的基础上的设备状态。在离线学习中，假设训练数据在整个感兴趣的时间范围内可用，而在在线学习中，我们只使用特定时间间隔的训练数据来做出相应的决策。我们将训练数据拟合成多项式方程，该多项式方程给出了TD密度方面的误码率，然后设计一个优化问题，以达到从一种编码方案切换到另一种编码方案的最佳重新配置决策。目标是最大化存储容量和/或最小化解码复杂度。这个问题归结为一个线性规划问题。我们表明，我们的解决方案是全局最优的问题特征的基础上，我们提供了各种实验结果，证明了我们的方法在TDMR系统的有效性。
摘要：In the age of data revolution, a modern storage~or transmission system typically requires different levels of protection. For example, the coding technique used to fortify data in a modern storage system when the device is fresh cannot be the same as that used when the device ages. Therefore, providing reconfigurable coding schemes and devising an effective way to perform this reconfiguration are key to extending the device lifetime. We focus on constrained coding schemes for the emerging two-dimensional magnetic recording (TDMR) technology. Recently, we have designed efficient lexicographically-ordered constrained (LOCO) coding schemes for various stages of the TDMR device lifetime, focusing on the elimination of isolation patterns, and demonstrated remarkable gains by using them. LOCO codes are naturally reconfigurable, and we exploit this feature in our work. Reconfiguration based on predetermined time stamps, which is what the industry adopts, neglects the actual device status. Instead, we propose offline and online learning methods to perform this task based on the device status. In offline learning, training data is assumed to be available throughout the time span of interest, while in online learning, we only use training data at specific time intervals to make consequential decisions. We fit the training data to polynomial equations that give the bit error rate in terms of TD density, then design an optimization problem in order to reach the optimal reconfiguration decisions to switch from a coding scheme to another. The objective is to maximize the storage capacity and/or minimize the decoding complexity. The problem reduces to a linear programming problem. We show that our solution is the global optimal based on problem characteristics, and we offer various experimental results that demonstrate the effectiveness of our approach in TDMR systems.
【15】Residual Prior Diffusion: A Probabilistic Framework Integrating Coarse Latent Priors with Diffusion Models
标题：剩余先验扩散：集成粗潜先验与扩散模型的概率框架
链接：https://arxiv.org/abs/2512.21593
备注：40 pages
摘要：扩散模型已成为深度生成建模的核心工具，但标准公式依赖于单个网络和单个扩散时间表将简单的先验（通常是标准正态分布）转换为目标数据分布。因此，该模型必须同时表示分布的全球结构及其细尺度局部变化，当这些尺度严重不匹配时，这变得困难。这个问题既出现在自然图像中，其中粗流形级结构和细纹理共存，也出现在具有高度集中的局部结构的低维分布中。为了解决这个问题，我们提出了残差先验扩散（RPD），这是一个两阶段的框架，其中粗先验模型首先捕获数据分布的大规模结构，然后训练扩散模型来表示先验和目标数据分布之间的残差。我们制定RPD作为一个明确的概率模型与一个易于处理的证据下限，其优化减少到熟悉的目标，噪声预测或速度预测。我们进一步引入辅助变量，利用先验模型的信息，并从理论上分析它们如何降低RPD预测问题的难度。在具有细粒度局部结构的合成数据集上的实验表明，标准扩散模型无法捕获局部细节，而RPD准确地捕获细尺度细节，同时保留分布的大尺度结构。在自然图像生成任务中，RPD实现了与代表性的基于扩散的基线相匹配或超过的生成质量，并且即使使用少量的推理步骤也保持了强大的性能。
摘要：Diffusion models have become a central tool in deep generative modeling, but standard formulations rely on a single network and a single diffusion schedule to transform a simple prior, typically a standard normal distribution, into the target data distribution. As a result, the model must simultaneously represent the global structure of the distribution and its fine-scale local variations, which becomes difficult when these scales are strongly mismatched. This issue arises both in natural images, where coarse manifold-level structure and fine textures coexist, and in low-dimensional distributions with highly concentrated local structure. To address this issue, we propose Residual Prior Diffusion (RPD), a two-stage framework in which a coarse prior model first captures the large-scale structure of the data distribution, and a diffusion model is then trained to represent the residual between the prior and the target data distribution. We formulate RPD as an explicit probabilistic model with a tractable evidence lower bound, whose optimization reduces to the familiar objectives of noise prediction or velocity prediction. We further introduce auxiliary variables that leverage information from the prior model and theoretically analyze how they reduce the difficulty of the prediction problem in RPD. Experiments on synthetic datasets with fine-grained local structure show that standard diffusion models fail to capture local details, whereas RPD accurately captures fine-scale detail while preserving the large-scale structure of the distribution. On natural image generation tasks, RPD achieved generation quality that matched or exceeded that of representative diffusion-based baselines and it maintained strong performance even with a small number of inference steps.
【16】Deep learning-enhanced dual-mode multiplexed optical sensor for point-of-care diagnostics of cardiovascular diseases
标题：深度学习增强型双模式多路传输光学传感器，用于心血管疾病的现场诊断
链接：https://arxiv.org/abs/2512.21389
备注：32 Pages, 6 Figures, 2 Tables
摘要：快速和可获得的心脏生物标志物检测对于心肌梗死（MI）和心力衰竭（HF）的及时诊断和风险评估至关重要，这两种相互关联的疾病经常共存，并导致复发性住院，死亡率高。然而，目前的实验室和即时检测系统受到周转时间长、所检测的生物标志物的动态范围窄以及无法捕获心血管疾病复杂性的单一分析物形式的限制。在这里，我们提出了一种深度学习增强的双模式多路复用垂直流分析（xVFA），具有便携式光学读取器和基于神经网络的量化管道。该光学传感器在单个纸基盒内集成了比色和荧光检测，以互补地覆盖低丰度和高丰度生物标志物的大动态范围（跨越约6个数量级），同时保持定量准确性。使用50 uL血清，光学传感器可在23分钟内同时定量心肌肌钙蛋白I（cTnI）、肌酸激酶MB（CK-MB）和N末端B型利钠肽原（NT-proBNP）。xVFA对cTnI的灵敏度低于pg/mL，对CK-MB和NT-proBNP的灵敏度低于ng/mL，涵盖了这些生物标志物的临床相关范围。在92个患者血清样品上训练和盲测的神经网络模型产生了稳健的定量性能（Pearson’s r > 0.96相对于参考测定）。通过将高灵敏度、多路复用和自动化结合在一个紧凑且具有成本效益的光学传感器格式中，双模xVFA能够在护理点进行快速和定量的心血管诊断。
摘要：Rapid and accessible cardiac biomarker testing is essential for the timely diagnosis and risk assessment of myocardial infarction (MI) and heart failure (HF), two interrelated conditions that frequently coexist and drive recurrent hospitalizations with high mortality. However, current laboratory and point-of-care testing systems are limited by long turnaround times, narrow dynamic ranges for the tested biomarkers, and single-analyte formats that fail to capture the complexity of cardiovascular disease. Here, we present a deep learning-enhanced dual-mode multiplexed vertical flow assay (xVFA) with a portable optical reader and a neural network-based quantification pipeline. This optical sensor integrates colorimetric and chemiluminescent detection within a single paper-based cartridge to complementarily cover a large dynamic range (spanning ~6 orders of magnitude) for both low- and high-abundance biomarkers, while maintaining quantitative accuracy. Using 50 uL of serum, the optical sensor simultaneously quantifies cardiac troponin I (cTnI), creatine kinase-MB (CK-MB), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) within 23 min. The xVFA achieves sub-pg/mL sensitivity for cTnI and sub-ng/mL sensitivity for CK-MB and NT-proBNP, spanning the clinically relevant ranges for these biomarkers. Neural network models trained and blindly tested on 92 patient serum samples yielded a robust quantification performance (Pearson's r > 0.96 vs. reference assays). By combining high sensitivity, multiplexing, and automation in a compact and cost-effective optical sensor format, the dual-mode xVFA enables rapid and quantitative cardiovascular diagnostics at the point of care.
【17】Atomistic Simulation Guided Convolutional Neural Networks for Thermal Modeling of Friction Stir Welding
标题：原子模拟引导卷积神经网络用于搅拌摩擦焊热建模
链接：https://arxiv.org/abs/2512.21344
备注：25 pages, 11 figures, 2 tables
摘要：准确预测搅拌摩擦焊过程中的温度场是理解搅拌摩擦焊热力学行为的基础。在这项研究中，分子动力学模拟进行了使用LAMMPS模拟铝搅拌摩擦焊在原子尺度上，捕捉材料流动，塑性变形，并在工具插入，横移和回缩过程中产生的热量。从模拟轨迹中提取原子的位置和速度，并将其转换为基于物理的二维空间网格。这些网格表示局部高度变化、速度分量、速度大小和原子密度，从而保持焊接区内的空间相关性。开发了一个二维卷积神经网络，直接从空间分辨的原子数据预测温度。进行超参数优化以确定适当的网络配置。训练后的模型表现出很强的预测能力，在看不见的测试数据上实现了0.9439的决定系数R平方，14.94 K的均方根误差和11.58 K的平均绝对误差。类激活图分析表明，该模型赋予更高的重要性，靠近工具材料界面，这是与强烈的变形和生热的分子动力学模拟的区域。结果表明，从原子模拟数据的空间学习可以准确地再现搅拌摩擦焊中的温度趋势，同时与原子尺度下观察到的物理变形和流动机制保持一致。
摘要：Accurate prediction of temperature evolution is essential for understanding thermomechanical behavior in friction stir welding. In this study, molecular dynamics simulations were performed using LAMMPS to model aluminum friction stir welding at the atomic scale, capturing material flow, plastic deformation, and heat generation during tool plunge, traverse, and retraction. Atomic positions and velocities were extracted from simulation trajectories and transformed into physics based two dimensional spatial grids. These grids represent local height variation, velocity components, velocity magnitude, and atomic density, preserving spatial correlations within the weld zone. A two-dimensional convolutional neural network was developed to predict temperature directly from the spatially resolved atomistic data. Hyperparameter optimization was carried out to determine an appropriate network configuration. The trained model demonstrates strong predictive capability, achieving a coefficient of determination R square of 0.9439, a root mean square error of 14.94 K, and a mean absolute error of 11.58 K on unseen test data. Class Activation Map analysis indicates that the model assigns higher importance to regions near the tool material interface, which are associated with intense deformation and heat generation in the molecular dynamics simulations. The results show that spatial learning from atomistic simulation data can accurately reproduce temperature trends in friction stir welding while remaining consistent with physical deformation and flow mechanisms observed at the atomic scale.
其他(21篇)
【1】Direction Finding with Sparse Arrays Based on Variable Window Size Spatial Smoothing
标题：基于可变窗口大小空间平滑的稀疏阵列探路
链接：https://arxiv.org/abs/2512.22024
备注：2 figures, 5 pages
摘要：在这项工作中，我们引入了一个可变窗口大小（VWS）的空间平滑框架，提高了稀疏线阵的波达方向（DOA）估计基于coarray-based。通过压缩平滑孔径，提出的VWS协阵MUSIC（VWS-CA-MUSIC）和VWS协阵root-MUSIC（VWS-CA-rMUSIC）算法用未扰动的低秩附加项代替平滑协阵数据中的部分扰动秩1外积，在保持信号子空间跨度的同时，增加了信号子空间和噪声子空间之间的分离度.我们还推导出的界限，保证可识别性，通过限制压缩参数可以假设的值。稀疏几何的仿真结果表明，相对于固定窗口协阵MUSIC方法，该方法具有显着的性能改善和复杂度节省。
摘要：In this work, we introduce a variable window size (VWS) spatial smoothing framework that enhances coarray-based direction of arrival (DOA) estimation for sparse linear arrays. By compressing the smoothing aperture, the proposed VWS Coarray MUSIC (VWS-CA-MUSIC) and VWS Coarray root-MUSIC (VWS-CA-rMUSIC) algorithms replace part of the perturbed rank-one outer products in the smoothed coarray data with unperturbed low-rank additional terms, increasing the separation between signal and noise subspaces, while preserving the signal subspace span. We also derive the bounds that guarantees identifiability, by limiting the values that can be assumed by the compression parameter. Simulations with sparse geometries reveal significant performance improvements and complexity savings relative to the fixed-window coarray MUSIC method.
【2】Hybrid Combinatorial Multi-armed Bandits with Probabilistically Triggered Arms
标题：具有概率触发手臂的混合组合多臂强盗
链接：https://arxiv.org/abs/2512.21925
摘要：具有概率触发武器的组合多武器土匪问题（CMAB-T）已被广泛研究。以前的工作主要集中在在线设置，其中代理通过迭代交互了解未知环境，或离线设置，其中仅从记录的数据中学习策略。然而，这些范例中的每一个都有固有的局限性：在线算法遭受高交互成本和缓慢的适应，而离线方法受到数据集质量和缺乏探索能力的限制。为了解决这些互补的弱点，我们提出了混合CMAB-T，一个新的框架，集成离线数据与在线互动的原则性的方式。我们提出的混合CUCB算法利用离线数据来指导探索和加速收敛，同时战略性地结合在线交互来减轻离线数据集的覆盖不足或分布偏差。我们提供了理论上的保证算法的遗憾，证明了混合CUCB显着优于纯在线方法时，高质量的离线数据，并有效地纠正了固有的偏差时，数据是有限的或不一致的离线方法。实验结果进一步证明了该算法的一致性优势。
摘要：The problem of combinatorial multi-armed bandits with probabilistically triggered arms (CMAB-T) has been extensively studied. Prior work primarily focuses on either the online setting where an agent learns about the unknown environment through iterative interactions, or the offline setting where a policy is learned solely from logged data. However, each of these paradigms has inherent limitations: online algorithms suffer from high interaction costs and slow adaptation, while offline methods are constrained by dataset quality and lack of exploration capabilities. To address these complementary weaknesses, we propose hybrid CMAB-T, a new framework that integrates offline data with online interaction in a principled manner. Our proposed hybrid CUCB algorithm leverages offline data to guide exploration and accelerate convergence, while strategically incorporating online interactions to mitigate the insufficient coverage or distributional bias of the offline dataset. We provide theoretical guarantees on the algorithm's regret, demonstrating that hybrid CUCB significantly outperforms purely online approaches when high-quality offline data is available, and effectively corrects the bias inherent in offline-only methods when the data is limited or misaligned. Empirical results further demonstrate the consistent advantage of our algorithm.
【3】Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
标题：利用自主目标进化代理加速科学发现
链接：https://arxiv.org/abs/2512.21782
摘要：人们对开发扩展科学发现边界的代理人产生了前所未有的兴趣，主要是通过优化科学家指定的定量目标函数。然而，对于科学的重大挑战来说，这些目标只是不完美的替代品。我们认为，自动化的目标函数设计是一个中心，但未满足的要求，科学发现代理。在这项工作中，我们引入了科学自主目标进化代理（SAGA）来修正这一挑战。SAGA采用双层架构，其中LLM代理的外循环分析优化结果，提出新的目标，并将其转换为可计算的评分函数，而内环在当前目标下执行解决方案优化。这种双层设计使得能够系统地探索目标空间及其权衡，而不是将它们视为固定的输入。我们通过广泛的应用程序，包括抗生素设计，无机材料设计，功能性DNA序列设计和化学工艺设计，展示了自动化的目标制定可以大大提高科学发现代理的有效性的框架。
摘要 ：There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by scientists. However, for grand challenges in science , these objectives are only imperfect proxies. We argue that automating objective function design is a central, yet unmet requirement for scientific discovery agents. In this work, we introduce the Scientific Autonomous Goal-evolving Agent (SAGA) to amend this challenge. SAGA employs a bi-level architecture in which an outer loop of LLM agents analyzes optimization outcomes, proposes new objectives, and converts them into computable scoring functions, while an inner loop performs solution optimization under the current objectives. This bi-level design enables systematic exploration of the space of objectives and their trade-offs, rather than treating them as fixed inputs. We demonstrate the framework through a broad spectrum of applications, including antibiotic design, inorganic materials design, functional DNA sequence design, and chemical process design, showing that automating objective formulation can substantially improve the effectiveness of scientific discovery agents.
【4】HELP: Hierarchical Embodied Language Planner for Household Tasks
标题：HELP：家庭任务的分层协调语言规划器
链接：https://arxiv.org/abs/2512.21723
摘要：负责复杂场景的智能体，无论是在真实环境还是模拟环境中，都严重依赖于强大的规划能力。当指令用自然语言表达时，配备了广泛语言知识的大型语言模型（LLM）可以发挥这一作用。然而，为了有效地利用这种模型的能力，以处理语言的歧义，从环境中检索信息，并根据现有的技能的代理，一个适当的架构必须设计。我们提出了一个层次化的语言规划器，称为帮助，由一组基于LLM的代理，每个致力于解决不同的子任务。我们评估所提出的方法上的家庭任务，并进行现实世界的实验与体现代理。我们还专注于使用具有相对较少参数的开源LLM，以实现自主部署。
摘要：Embodied agents tasked with complex scenarios, whether in real or simulated environments, rely heavily on robust planning capabilities. When instructions are formulated in natural language, large language models (LLMs) equipped with extensive linguistic knowledge can play this role. However, to effectively exploit the ability of such models to handle linguistic ambiguity, to retrieve information from the environment, and to be based on the available skills of an agent, an appropriate architecture must be designed. We propose a Hierarchical Embodied Language Planner, called HELP, consisting of a set of LLM-based agents, each dedicated to solving a different subtask. We evaluate the proposed approach on a household task and perform real-world experiments with an embodied agent. We also focus on the use of open source LLMs with a relatively small number of parameters, to enable autonomous deployment.
【5】An Information Theoretic Perspective on Agentic System Design
标题：统计系统设计的信息论视角
链接：https://arxiv.org/abs/2512.21720
摘要：学习语言模型（LM）系统为“深度研究”和“克劳德代码”等现代应用程序提供支持，并利用多LM架构来克服上下文限制。在它们明显的多样性之下，存在一种重复的模式：较小的“压缩器”LM（甚至可以在本地运行）将原始上下文提取为紧凑的文本，然后由较大的“预测器”LM使用。尽管它们很受欢迎，但压缩机-预测器系统的设计在很大程度上仍然是临时的，关于压缩机和预测器的选择如何塑造下游性能的指导很少。在实践中，将收益归因于压缩与预测需要昂贵的、特定于任务的成对扫描。我们认为，这些代理系统设计的问题，从根本上说，信息理论。将压缩器LM视为噪声信道，我们引入了上下文及其压缩之间的互信息的简单估计器，以独立于任务的方式量化压缩质量。我们发现，互信息强烈预测下游的性能，独立于任何特定的任务。通过信息理论框架，我们对五个数据集和三个模型家族进行了全面的实证分析。结果表明，更大的压缩器不仅更准确，而且更有效，每个令牌传递更多的信息位。例如，一个7 B的Qwen-2.5压缩器比它的1.5B兄弟压缩器更精确1.6\times $，更简洁4.6\times $，并且每个标记传递的互信息比特数多5.5\times $。跨数据集，缩放压缩器比缩放预测器更有效，使更大的设备上的压缩器能够与更小的云预测器配对。应用于深度研究系统，这些原则使当地压缩机小到3B参数，以恢复$26\%$的API成本的$99\%$的前沿LM的准确性。
摘要：Agentic language model (LM) systems power modern applications like "Deep Research" and "Claude Code," and leverage multi-LM architectures to overcome context limitations. Beneath their apparent diversity lies a recurring pattern: smaller "compressor" LMs (that can even run locally) distill raw context into compact text that is then consumed by larger "predictor" LMs. Despite their popularity, the design of compressor-predictor systems remains largely ad hoc, with little guidance on how compressor and predictor choices shape downstream performance. In practice, attributing gains to compression versus prediction requires costly, task-specific pairwise sweeps. We argue that these agentic system design questions are, at root, information-theoretic. Viewing the compressor LM as a noisy channel, we introduce a simple estimator of mutual information between the context and its compression to quantify compression quality in a task-independent way. We show that mutual information strongly predicts downstream performance, independent of any specific task. Through an information-theoretic framework, we perform a comprehensive empirical analysis across five datasets and three model families. Results reveal that larger compressors not only are more accurate, but also more token-efficient, conveying more bits of information per token. A 7B Qwen-2.5 compressor, for instance, is $1.6\times$ more accurate, $4.6\times$ more concise, and conveys $5.5\times$ more bits of mutual information per token than its 1.5B sibling. Across datasets, scaling compressors is substantially more effective than scaling predictors, enabling larger on-device compressors to pair with smaller cloud predictors. Applied to a Deep Research system, these principles enable local compressors as small as 3B parameters to recover $99\%$ of frontier-LM accuracy at $26\%$ of API costs.
【6】Multiconnectivity for SAGIN: Current Trends, Challenges, AI-driven Solutions, and Opportunities
标题：SAGIN的多连接性：当前趋势、挑战、人工智能驱动的解决方案和机遇
链接：https://arxiv.org/abs/2512.21717
摘要：空-空-地-综合网络（SAGIN）支持的多连接（MC）正在成为下一代网络的关键推动因素，使用户能够同时利用多层非地面网络（NTN）和多无线电接入技术（multi-RAT）地面网络（TN）上的多条链路。然而，TN和NTN的异构性引入了复杂的体系结构挑战，使MC实现复杂化。具体来说，链路类型的多样性（包括空对空、空对地和地对地通信）使最佳资源分配变得非常复杂。强化学习（RL）和代理人工智能（AI）的最新进展在复杂和动态环境中的最优决策方面表现出显着的有效性。在本文中，我们回顾了目前的发展SAGIN启用MC和概述与其实施相关的主要挑战。我们进一步强调了AI驱动的方法在异构SAGIN环境中进行资源优化的变革潜力。为此，我们提出了一个案例研究资源分配优化，使代理RL的SAGIN使能MC涉及不同的无线电接入技术（RAT）。结果表明，基于学习的方法可以有效地处理复杂的场景，并在延迟和容量方面大幅提高网络性能，同时作为可接受的权衡，会导致功耗适度增加。最后，开放的研究问题和未来的发展方向，以实现高效的SAGIN使MC。
摘要：Space-air-ground-integrated network (SAGIN)-enabled multiconnectivity (MC) is emerging as a key enabler for next-generation networks, enabling users to simultaneously utilize multiple links across multi-layer non-terrestrial networks (NTN) and multi-radio access technology (multi-RAT) terrestrial networks (TN). However, the heterogeneity of TN and NTN introduces complex architectural challenges that complicate MC implementation. Specifically, the diversity of link types, spanning air-to-air, air-to-space, space-to-space, space-to-ground, and ground-to-ground communications, renders optimal resource allocation highly complex. Recent advancements in reinforcement learning (RL) and agentic artificial intelligence (AI) have shown remarkable effectiveness in optimal decision-making in complex and dynamic environments. In this paper, we review the current developments in SAGIN-enabled MC and outline the key challenges associated with its implementation. We further highlight the transformative potential of AI-driven approaches for resource optimization in a heterogeneous SAGIN environment. To this end, we present a case study on resource allocation optimization enabled by agentic RL for SAGIN-enabled MC involving diverse radio access technologies (RATs). Results show that learning-based methods can effectively handle complex scenarios and substantially enhance network performance in terms of latency and capacity while incurring a moderate increase in power consumption as an acceptable tradeoff. Finally, open research problems and future directions are presented to realize efficient SAGIN-enabled MC.
【7】The Deepfake Detective: Interpreting Neural Forensics Through Sparse Features and Manifolds
标题：Deepfake Detective：通过稀疏特征和Manifolds解释神经取证
链接：https://arxiv.org/abs/2512.21670
备注：10 pages, 5 figures, Initial Work
摘要：Deepfake检测模型在识别合成媒体方面已经达到了很高的准确性，但它们的决策过程在很大程度上仍然不透明。在本文中，我们提出了一个适用于视觉语言模型的深度伪造检测的机械可解释性框架。我们的方法结合了稀疏自动编码器（SAE）分析内部网络表示与一种新的法医流形分析，探讨模型的功能如何响应受控的法医工件操作。我们证明了只有一小部分潜在特征在每一层中被积极使用，并且模型特征流形的几何属性，包括内在维度，曲率和特征选择性，随着不同类型的deepfake工件而系统地变化。这些见解为打开deepfake检测器的“黑匣子”迈出了第一步，使我们能够识别哪些学习的特征对应于特定的法医文物，并指导开发更具可解释性和鲁棒性的模型。
摘要：Deepfake detection models have achieved high accuracy in identifying synthetic media, but their decision processes remain largely opaque. In this paper we present a mechanistic interpretability framework for deepfake detection applied to a vision-language model. Our approach combines a sparse autoencoder (SAE) analysis of internal network representations with a novel forensic manifold analysis that probes how the model's features respond to controlled forensic artifact manipulations. We demonstrate that only a small fraction of latent features are actively used in each layer, and that the geometric properties of the model's feature manifold, including intrinsic dimensionality, curvature, and feature selectivity, vary systematically with different types of deepfake artifacts. These insights provide a first step toward opening the "black box" of deepfake detectors, allowing us to identify which learned features correspond to specific forensic artifacts and to guide the development of more interpretable and robust models.
【8】Semantic Codebooks as Effective Priors for Neural Speech Compression
标题：语义码本作为神经语音压缩的有效先验
链接：https://arxiv.org/abs/2512.21653
摘要：语音编解码器传统上是针对波形保真度进行优化的，分配比特以保留声学细节，即使其中大部分可以从语言结构中推断出来。这导致了低效率的压缩和下游识别任务的次优性能。我们提出SemDAC，语义感知神经音频编解码器，利用语义码本作为有效的语音压缩的先验。在SemDAC中，残差矢量量化（RVQ）堆栈中的第一个量化器从HuBERT特征中提取，以产生捕获语音内容的语义令牌，而后续量化器则对残差声学进行建模。FiLM条件解码器重构基于语义标记的音频，提高了声学码本的使用效率。尽管其简单性，但这种设计被证明是非常有效的：SemDAC在感知指标上优于DAC，并且在重构语音上运行Whisper时实现了较低的WER，所有这些都是在基本上较低的比特率下操作的（例如，0.95 kbps与DAC的2.5 kbps）。这些结果表明，语义码本提供了一个有效的神经语音压缩的归纳偏见，产生紧凑，但简化友好的表示。
摘要：Speech codecs are traditionally optimized for waveform fidelity, allocating bits to preserve acoustic detail even when much of it can be inferred from linguistic structure. This leads to inefficient compression and suboptimal performance on downstream recognition tasks. We propose SemDAC, a semantic-aware neural audio codec that leverages semantic codebooks as effective priors for speech compression. In SemDAC, the first quantizer in a residual vector quantization (RVQ) stack is distilled from HuBERT features to produce semantic tokens that capture phonetic content, while subsequent quantizers model residual acoustics. A FiLM-conditioned decoder reconstructs audio conditioned on the semantic tokens, improving efficiency in the use of acoustic codebooks. Despite its simplicity, this design proves highly effective: SemDAC outperforms DAC across perceptual metrics and achieves lower WER when running Whisper on reconstructed speech, all while operating at substantially lower bitrates (e.g., 0.95 kbps vs. 2.5 kbps for DAC). These results demonstrate that semantic codebooks provide an effective inductive bias for neural speech compression, producing compact yet recognition-friendly representations.
【9】Variance-Aware Prior-Based Tree Policies for Monte Carlo Tree Search
标题：蒙特卡罗树搜索的方差感知基于优先级的树策略
链接：https://arxiv.org/abs/2512.21648
摘要：蒙特卡洛树搜索（MCTS）通过在需要长时间推理的任务中集成规划和学习，深刻地影响了强化学习（RL），以AlphaZero系列算法为例。MCTS的核心是搜索策略，由基于应用于树（UCT）的置信上限（UCB）的树策略管理。AlphaZero成功的一个关键因素是在基于UCB 1的树策略PUCT中引入了先验项，这提高了探索效率，从而加快了训练速度。虽然存在许多比UCB 1具有更强理论保证的替代UCB，但将它们扩展到基于先验的UCT一直具有挑战性，因为PUCT是根据经验而不是根据第一原理推导的。最近的工作回顾性地证明了PUCT的框架MCTS作为一个正则化的策略优化（RPO）问题。基于这一观点，我们引入了逆RPO，一种通用的方法，系统地从任何先验的UCB中推导出基于先验的UCT。将此方法应用于方差感知的UCB-V，我们得到了两个新的基于先验的树策略，将方差估计到搜索中。实验表明，这些方差感知先验的UCT优于PUCT在多个基准测试，而不会产生额外的计算成本。我们还提供了一个扩展的mctx库支持方差感知的UCT，表明所需的代码更改是最小的，旨在促进进一步研究原则性的先验UCT。代码：github.com/Max-We/inverse-rpo。
摘要：Monte Carlo Tree Search (MCTS) has profoundly influenced reinforcement learning (RL) by integrating planning and learning in tasks requiring long-horizon reasoning, exemplified by the AlphaZero family of algorithms. Central to MCTS is the search strategy, governed by a tree policy based on an upper confidence bound (UCB) applied to trees (UCT). A key factor in the success of AlphaZero is the introduction of a prior term in the UCB1-based tree policy PUCT, which improves exploration efficiency and thus accelerates training. While many alternative UCBs with stronger theoretical guarantees than UCB1 exist, extending them to prior-based UCTs has been challenging, since PUCT was derived empirically rather than from first principles. Recent work retrospectively justified PUCT by framing MCTS as a regularized policy optimization (RPO) problem. Building on this perspective, we introduce Inverse-RPO, a general methodology that systematically derives prior-based UCTs from any prior-free UCB. Applying this method to the variance-aware UCB-V, we obtain two new prior-based tree policies that incorporate variance estimates into the search. Experiments indicate that these variance-aware prior-based UCTs outperform PUCT across multiple benchmarks without incurring additional computational cost. We also provide an extension of the mctx library supporting variance-aware UCTs, showing that the required code changes are minimal and intended to facilitate further research on principled prior-based UCTs. Code: github.com/Max-We/inverse-rpo.
【10】Quantitative Verification of Omega-regular Properties in Probabilistic Programming
标题：概率规划中Omega-正规性质的定量验证
链接：https://arxiv.org/abs/2512.21596
摘要：概率编程提供了一个高级框架，用于将统计模型指定为具有内置随机性和条件的可执行程序。然而，现有的推理技术，通常计算后验分布在固定的时间点上的程序状态，最常见的是在终止，从而无法捕捉概率行为的时间演变。我们引入时间后验推理（TPI），一个新的框架，统一的概率编程与时间逻辑计算后验分布的执行轨迹，满足欧米茄定期规格，条件可能的时间观测。为了获得严格的定量保证，我们开发了一种新的方法来计算满足概率的上界和下界的ω-正则性质。我们的方法分解拉宾验收条件为持久性和复发组件，并构造随机屏障证书，健全绑定每个组件。我们实现了我们的方法在一个原型工具，TPInfer，并评估它的一套基准测试，证明有效和高效的推理丰富的时间属性的概率模型。
摘要 ：Probabilistic programming provides a high-level framework for specifying statistical models as executable programs with built-in randomness and conditioning. Existing inference techniques, however, typically compute posterior distributions over program states at fixed time points, most often at termination, thereby failing to capture the temporal evolution of probabilistic behaviors. We introduce temporal posterior inference (TPI), a new framework that unifies probabilistic programming with temporal logic by computing posterior distributions over execution traces that satisfy omega-regular specifications, conditioned on possibly temporal observations. To obtain rigorous quantitative guarantees, we develop a new method for computing upper and lower bounds on the satisfaction probabilities of omega-regular properties. Our approach decomposes Rabin acceptance conditions into persistence and recurrence components and constructs stochastic barrier certificates that soundly bound each component. We implement our approach in a prototype tool, TPInfer, and evaluate it on a suite of benchmarks, demonstrating effective and efficient inference over rich temporal properties in probabilistic models.
【11】Discovering Sparse Recovery Algorithms Using Neural Architecture Search
标题：利用神经结构搜索发现稀疏恢复算法
链接：https://arxiv.org/abs/2512.21563
备注：Presented at the 59th Asilomar Conference on Signals, Systems, and Computers
摘要：在信号处理中，设计新的反问题算法是一项非常困难、复杂且耗时的任务。在这篇简短的论文中，我们通过元学习工具（如神经结构搜索（NAS））在信号处理环境中自动发现算法的想法。具体来说，我们研究的迭代收缩保持算法（ISTA）和其加速快速ISTA（FISTA）的变种作为候选人的算法重新发现。我们开发了一个元学习框架，当给定超过50，000个变量的搜索空间时，该框架能够重新发现上述两个算法（的几个关键要素）。然后，我们将展示我们的框架如何适用于各种数据分布和算法，除了ISTA/FISTA。
摘要：The design of novel algorithms for solving inverse problems in signal processing is an incredibly difficult, heuristic-driven, and time-consuming task. In this short paper, we the idea of automated algorithm discovery in the signal processing context through meta-learning tools such as Neural Architecture Search (NAS). Specifically, we examine the Iterative Shrinkage Thresholding Algorithm (ISTA) and its accelerated Fast ISTA (FISTA) variant as candidates for algorithm rediscovery. We develop a meta-learning framework which is capable of rediscovering (several key elements of) the two aforementioned algorithms when given a search space of over 50,000 variables. We then show how our framework can apply to various data distributions and algorithms besides ISTA/FISTA.
【12】Generative Actor Critic
标题：一代演员评论家
链接：https://arxiv.org/abs/2512.21527
摘要：传统的强化学习（RL）算法通常专注于估计或最大化预期收益，在使用在线经验改进离线预训练模型时面临挑战。本文介绍了生成演员评论（GAC），一种新的框架，通过重新构建\texit {政策评估}作为学习轨迹和回报的联合分布的生成模型，$p（τ，y）$，以及\texit {政策改进}作为对这个学习模型进行通用推理，来实现顺序决策。为了使GAC可操作化，我们引入了一个基于潜变量模型的特定实例，该模型具有连续的潜计划向量。我们开发了新的推理策略，为\textit{剥削}，通过优化潜在的计划，以最大限度地提高预期收益，和\textit{勘探}，通过采样潜在的计划条件下动态调整的目标回报。在Gym-MuJoCo和Maze 2D基准测试上的实验表明，与最先进的方法相比，即使在没有逐步奖励的情况下，GAC也具有强大的离线性能和显著增强的离线到在线改进。
摘要：Conventional Reinforcement Learning (RL) algorithms, typically focused on estimating or maximizing expected returns, face challenges when refining offline pretrained models with online experiences. This paper introduces Generative Actor Critic (GAC), a novel framework that decouples sequential decision-making by reframing \textit{policy evaluation} as learning a generative model of the joint distribution over trajectories and returns, $p(τ, y)$, and \textit{policy improvement} as performing versatile inference on this learned model. To operationalize GAC, we introduce a specific instantiation based on a latent variable model that features continuous latent plan vectors. We develop novel inference strategies for both \textit{exploitation}, by optimizing latent plans to maximize expected returns, and \textit{exploration}, by sampling latent plans conditioned on dynamically adjusted target returns. Experiments on Gym-MuJoCo and Maze2D benchmarks demonstrate GAC's strong offline performance and significantly enhanced offline-to-online improvement compared to state-of-the-art methods, even in absence of step-wise rewards.
【13】First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions
标题：实用私人FL的第一个可证明的保证：超越限制性假设
链接：https://arxiv.org/abs/2512.21521
摘要：联邦学习（FL）支持对分散数据进行协作训练。差分隐私（DP）对于FL至关重要，但当前的隐私方法通常依赖于不切实际的假设（例如，有界梯度或异质性），阻碍了实际应用。现有的作品，放松这些假设通常忽略实际FL功能，包括多个本地更新和部分客户端的参与。我们介绍美联储-$α$-NormEC，第一个差分私人FL框架提供可证明的收敛性和DP保证标准假设下，同时完全支持这些实际功能。Fed-$α$-NormE集成了本地更新（完整和增量梯度步长），单独的服务器和客户端步长，以及至关重要的部分客户端参与，这对于真实世界的部署至关重要，对于隐私放大至关重要。我们的理论保证得到了私人深度学习任务实验的证实。
摘要：Federated Learning (FL) enables collaborative training on decentralized data. Differential privacy (DP) is crucial for FL, but current private methods often rely on unrealistic assumptions (e.g., bounded gradients or heterogeneity), hindering practical application. Existing works that relax these assumptions typically neglect practical FL features, including multiple local updates and partial client participation. We introduce Fed-$α$-NormEC, the first differentially private FL framework providing provable convergence and DP guarantees under standard assumptions while fully supporting these practical features. Fed-$α$-NormE integrates local updates (full and incremental gradient steps), separate server and client stepsizes, and, crucially, partial client participation, which is essential for real-world deployment and vital for privacy amplification. Our theoretical guarantees are corroborated by experiments on private deep learning tasks.
【14】Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training
标题：困惑感知数据缩放定律：困惑景观预测连续预训练的性能
链接：https://arxiv.org/abs/2512.21515
摘要：持续预训练（CPT）是使基础模型适应特定领域应用程序的基本方法。用于预训练的缩放律定义了数据集大小与LLM的测试损失之间的幂律关系。然而，简单地增加CPT数据的边际收益会迅速减少，导致数据利用率不佳和训练效率低下。为了解决这一挑战，我们提出了一种新的困惑意识的数据缩放法，以建立特定领域的数据和测试损失的困惑景观之间的预测关系。我们的方法利用来自领域数据的预训练模型的困惑作为估计知识差距的代理，有效地量化候选训练样本的信息困惑景观。通过在不同的困惑机制中拟合这种比例律，我们可以自适应地选择高效用的数据子集，优先考虑最大化知识吸收的内容，同时最大限度地减少冗余和噪声。大量的实验表明，我们的方法始终识别接近最佳的训练子集，并在医疗和一般领域的基准测试中取得了优异的性能。
摘要 ：Continual Pre-training (CPT) serves as a fundamental approach for adapting foundation models to domain-specific applications. Scaling laws for pre-training define a power-law relationship between dataset size and the test loss of an LLM. However, the marginal gains from simply increasing data for CPT diminish rapidly, yielding suboptimal data utilization and inefficient training. To address this challenge, we propose a novel perplexity-aware data scaling law to establish a predictive relationship between the perplexity landscape of domain-specific data and the test loss. Our approach leverages the perplexity derived from the pre-trained model on domain data as a proxy for estimating the knowledge gap, effectively quantifying the informational perplexity landscape of candidate training samples. By fitting this scaling law across diverse perplexity regimes, we enable adaptive selection of high-utility data subsets, prioritizing content that maximizes knowledge absorption while minimizing redundancy and noise. Extensive experiments demonstrate that our method consistently identifies near-optimal training subsets and achieves superior performance on both medical and general-domain benchmarks.
【15】RLLaVA: An RL-central Framework for Language and Vision Assistants
标题：RLLaVA：以RL为中心的语言和视觉助理框架
链接：https://arxiv.org/abs/2512.21450
备注：The code is available at https://github.com/TinyLoopX/RLLaVA
摘要：我们提出了一个RL为中心的框架语言和视觉助理（RLLaVA）的马尔可夫决策过程（MDP）的制定。RLLaVA将RL算法逻辑从模型架构和分布式执行中分离出来，支持研究人员用最少的代码实现新的RL算法，并插入广泛的RL方法和视觉语言模型（VLM），同时保持对特定训练和推理引擎的不可知性。RLLaVA使1B-7 B模型的资源高效训练在普通GPU上变得可行;值得注意的是，4 B规模的模型可以在单个24 GB GPU上进行端到端的全参数更新训练。多模态和代理任务上的实验表明，RLLaVA具有任务可扩展性，并且使用它训练的模型始终提高了基础模型的性能，与其他专门设计的RL框架相比具有竞争力。该代码可在https://github.com/TinyLoopX/RLLaVA上获得。
摘要：We present an RL-central framework for Language and Vision Assistants (RLLaVA) with its formulation of Markov decision process (MDP). RLLaVA decouples RL algorithmic logic from model architecture and distributed execution, supporting researchers in implementing new RL algorithms with minimal code, and to plug in a broad family of RL methods and vision-language models (VLMs) while remaining agnostic to specific training and inference engines. RLLaVA makes resource-efficient training of 1B--7B models feasible on common GPUs; notably, 4B-scale models can be trained end-to-end with full-parameter updates on a single 24GB GPU. Experiments on multi-modal and agentic tasks demonstrate that RLLaVA has task extensibility, and the models trained with it consistently improve performance over base models, competitive with other specially engineered RL frameworks. The code is available at https://github.com/TinyLoopX/RLLaVA.
【16】Physics-Informed Neural Solvers for Periodic Quantum Eigenproblems
标题：周期性量子本征问题的物理知情神经求解器
链接：https://arxiv.org/abs/2512.21349
备注：Master's thesis
摘要：本论文提出了一个基于物理学的机器学习框架，用于解决与二维周期势中的粒子相关的Floquet-Bloch本征值问题，重点是蜂窝晶格几何，因为它具有独特的Dirac点带拓扑结构及其与石墨烯等材料的相关性。通过利用神经网络同时学习复杂的布洛赫函数及其相关的特征值（能量），我们开发了一个无网格求解器，通过一个没有监督的复合损失函数来执行薛定谔方程、布洛赫周期和归一化约束。该模型在布里渊区进行训练，以恢复带结构和布洛赫模式，并对传统的平面波展开方法进行数值验证。我们进一步探索了迁移学习技术，以使求解器从接近自由的电子势适应于强烈变化的势，证明了其捕获能带结构拓扑变化的能力。这项工作有助于不断增长的量子本征问题的物理信息机器学习领域，为对称性，能带结构和神经架构之间的相互作用提供了见解。
摘要：This thesis presents a physics-informed machine learning framework for solving the Floquet-Bloch eigenvalue problem associated with particles in two-dimensional periodic potentials, with a focus on honeycomb lattice geometry, due to its distinctive band topology featuring Dirac points and its relevance to materials such as graphene. By leveraging neural networks to learn complex Bloch functions and their associated eigenvalues (energies) simultaneously, we develop a mesh-free solver enforcing the governing Schrödinger equation, Bloch periodicity, and normalization constraints through a composite loss function without supervision. The model is trained over the Brillouin zone to recover band structures and Bloch modes, with numerical validation against traditional plane-wave expansion methods. We further explore transfer learning techniques to adapt the solver from nearly-free electron potentials to strongly varying potentials, demonstrating its ability to capture changes in band structure topology. This work contributes to the growing field of physics-informed machine learning for quantum eigenproblems, providing insights into the interplay between symmetry, band structure, and neural architectures.
【17】Harnessing Data Spaces to Build Intelligent Smart City Infrastructures Across the Cloud-Edge Continuum
标题：利用数据空间在云边缘连续体中构建智能智慧城市基础设施
链接：https://arxiv.org/abs/2512.21340
摘要：智慧城市越来越多地采用以数据为中心的架构，以提高城市服务的效率、可持续性和弹性。
摘要：Smart cities are increasingly adopting data-centric architectures to enhance the efficiency, sustainability, and resilience of urban services.
【18】Tilt Matching for Scalable Sampling and Fine-Tuning
标题：可扩展采样和微调的收件箱匹配
链接：https://arxiv.org/abs/2512.21829
摘要：我们提出了一个简单的，可扩展的算法，使用随机插值采样从非归一化密度和微调生成模型。该方法，匹配，产生于一个动力学方程的流量匹配速度的一个目标相同的分布倾斜的奖励，隐式地解决了随机最优控制问题。新的速度继承了随机内插传输的规律性，同时也是一个严格低于流量匹配本身的方差的目标的最小化。对速度场的更新可以解释为随机插值的所有联合累积量和奖励的副本的总和，并且一阶是它们的协方差。该算法不需要任何访问梯度的回报或反向传播通过轨迹的流动或扩散。我们经验验证，该方法是有效的，高度可扩展的，提供最先进的结果采样下的Lennard-Jones潜力和微调稳定扩散的竞争力，而不需要奖励乘数。它也可以直接应用于倾斜的几步流图模型。
摘要：We propose a simple, scalable algorithm for using stochastic interpolants to sample from unnormalized densities and for fine-tuning generative models. The approach, Tilt Matching, arises from a dynamical equation relating the flow matching velocity to one targeting the same distribution tilted by a reward, implicitly solving a stochastic optimal control problem. The new velocity inherits the regularity of stochastic interpolant transports while also being the minimizer of an objective with strictly lower variance than flow matching itself. The update to the velocity field can be interpreted as the sum of all joint cumulants of the stochastic interpolant and copies of the reward, and to first order is their covariance. The algorithms do not require any access to gradients of the reward or backpropagating through trajectories of the flow or diffusion. We empirically verify that the approach is efficient and highly scalable, providing state-of-the-art results on sampling under Lennard-Jones potentials and is competitive on fine-tuning Stable Diffusion, without requiring reward multipliers. It can also be straightforwardly applied to tilting few-step flow map models.
【19】Incorporating rank-free coupling and external field via an amplitude-only modulated spatial photonic Ising machine
标题：通过纯幅度调制空间量子伊辛机吸收无等级耦合和外场
链接：https://arxiv.org/abs/2512.21587
备注：4 pages, 3 figures
摘要：伊辛机已经成为组合优化问题的有效解决方案，例如NP难题，机器学习和金融建模。最近的空间光子伊辛机（SPIM）擅长多节点优化和自旋玻璃模拟，利用其大规模和完全连接的特性。然而，现有的基于激光衍射的SPIM通常牺牲时间效率或自旋计数来编码高阶自旋-自旋耦合和外部场，限制了它们在现实世界应用中的可扩展性。在这里，我们展示了一个振幅调制的无秩空间光子伊辛机（AR-SPIM）与每秒200次迭代。通过将任意伊辛哈密顿量重新表示为阿达玛乘积的和，然后将相应的矩阵/矢量加载到对齐的振幅空间光调制器和数字微镜器件上，我们直接将具有外场（近9位精度，-255到255）的797自旋伊辛模型映射到非相干光场，消除了重复和辅助操作的需要。作为编码准确性度量，测量的光强度和伊辛哈密顿之间的线性决定系数和皮尔逊相关系数超过0.9800，全球范围内的值超过0.9997。AR-SPIM实现了小于0.3%的错误率为基态搜索的偏置最大切割问题与任意的排名和权重，使复杂的相变观测，并有利于可扩展的自旋计数稀疏伊辛问题，通过删除零值阿达玛产品条款。这种可重新配置的AR-SPIM可以进一步开发，以支持大规模机器学习训练，并部署在离散优化和量子多体模拟的实际应用中。
摘要：Ising machines have emerged as effective solvers for combinatorial optimization problems, such as NP-hard problems, machine learning, and financial modeling. Recent spatial photonic Ising machines (SPIMs) excel in multi-node optimization and spin glass simulations, leveraging their large-scale and fully connected characteristics. However, existing laser diffraction-based SPIMs usually sacrifice time efficiency or spin count to encode high-rank spin-spin coupling and external fields, limiting their scalability for real-world applications. Here, we demonstrate an amplitude-only modulated rank-free spatial photonic Ising machine (AR-SPIM) with 200 iterations per second. By re-formulating an arbitrary Ising Hamiltonian as the sum of Hadamard products, followed by loading the corresponding matrices/vectors onto an aligned amplitude spatial light modulator and digital micro-mirrors device, we directly map a 797-spin Ising model with external fields (nearly 9-bit precision, -255 to 255) into an incoherent light field, eliminating the need for repeated and auxiliary operations. Serving as encoding accuracy metrics, the linear coefficient of determination and Pearson correlation coefficient between measured light intensities and Ising Hamiltonians exceed 0.9800, with values exceed 0.9997 globally. The AR-SPIM achieves less than 0.3% error rate for ground-state search of biased Max-cut problems with arbitrary ranks and weights, enables complex phase transition observations, and facilitates scalable spin counts for sparse Ising problems via removing zero-valued Hadamard product terms. This reconfigurable AR-SPIM can be further developed to support large-scale machine-learning training and deployed for practical applications in discrete optimization and quantum many-body simulations.
【20】Quantum Nondecimated Wavelet Transform: Theory, Circuits, and Applications
标题：量子非抽取子波变换：理论、电路和应用
链接：https://arxiv.org/abs/2512.21478
摘要：非抽取小波变换（NDWT）是经典多尺度信号分析的重要工具，具有稳定性、冗余性和平移不变性等优点。本文开发了两个互补的量子公式的NDWT嵌入这些经典性质相干到量子计算。第一个配方是基于对NDWT的ε抽取解释，并通过将移位索引提升到量子寄存器并应用受控循环移位，然后通过小波分析酉来同时实现所有循环移位小波变换。由此产生的建设产生一个明确的，完全酉量子表示的冗余小波系数，并支持相干后处理，包括量子收缩通过辅助驱动的完全正的跟踪保持地图。第二个公式是基于阿达玛测试和对角相位算子探测尺度位移小波结构通过干扰，提供直接访问平移不变的能量尺度图和多尺度谱，而无需显式系数重建。总之，这两种方法表明，冗余和平移不变性可以利用，而不是避免在量子设置。去噪，特征提取和光谱缩放的应用程序说明了量子NDWT如何为多尺度量子信号处理提供灵活且具有物理意义的基础。
摘要：The nondecimated or translation-invariant wavelet transform (NDWT) is a central tool in classical multiscale signal analysis, valued for its stability, redundancy, and shift invariance. This paper develops two complementary quantum formulations of the NDWT that embed these classical properties coherently into quantum computation. The first formulation is based on the epsilon-decimated interpretation of the NDWT and realizes all circularly shifted wavelet transforms simultaneously by promoting the shift index to a quantum register and applying controlled circular shifts followed by a wavelet analysis unitary. The resulting construction yields an explicit, fully unitary quantum representation of redundant wavelet coefficients and supports coherent postprocessing, including quantum shrinkage via ancilla-driven completely positive trace preserving maps. The second formulation is based on the Hadamard test and uses diagonal phase operators to probe scale-shift wavelet structure through interference, providing direct access to shift-invariant energy scalograms and multiscale spectra without explicit coefficient reconstruction. Together, these two approaches demonstrate that redundancy and translation invariance can be exploited rather than avoided in the quantum setting. Applications to denoising, feature extraction, and spectral scaling illustrate how quantum NDWTs provide a flexible and physically meaningful foundation for multiscale quantum signal processing.
【21】An approach to Fisher-Rao metric for infinite dimensional non-parametric information geometry
标题：无限维非参数信息几何的Fisher-Rao度量方法
链接：https://arxiv.org/abs/2512.21451
摘要：由于非参数信息几何是无穷维的，Fisher-Rao度量是一个泛函，难以定义它的逆，因此非参数信息几何一直面临着一个“难解的障碍”.本文介绍了一种新的框架来解决棘手的切线空间的正交分解（$T_fM=S \oplus S^{\perp}$），其中S表示一个可观察的协变量子空间。通过分解，我们得到协变量Fisher信息矩阵（cnc），表示为$G_f$，这是一个有限维的和可计算的代表信息提取的流形的几何。事实上，通过证明迹定理：$H_G（f）=\text{Tr}（G_f）$，我们为之前引入的G-熵建立了严格的基础，从而将其不仅识别为基于梯度的正则化子，而且识别为基本几何不变量，表示与模型相关的概率分布捕获的总的可解释统计信息。此外，我们建立了$G_f$和KL-散度的二阶导数（即曲率）之间的联系，导致协变量Cramér-Rao下界（CRLB）的概念。我们证明了$G_f$是全等的有效Fisher信息矩阵，从而提供了半参数估计方差的基本限制。最后，我们应用我们的几何框架的流形假设，提升后者从一个启发式的假设到一个可测试的条件内的candle秩亏。通过定义信息捕获率，我们提供了一个严格的方法来估计高维数据的内在维数。简而言之，我们的工作弥合了抽象信息几何与可解释人工智能需求之间的差距，为揭示非参数模型的统计覆盖率和效率提供了一条易于处理的路径。
摘要 ：Being infinite dimensional, non-parametric information geometry has long faced an "intractability barrier" due to the fact that the Fisher-Rao metric is now a functional incurring difficulties in defining its inverse. This paper introduces a novel framework to resolve the intractability with an Orthogonal Decomposition of the Tangent Space ($T_fM=S \oplus S^{\perp}$), where S represents an observable covariate subspace. Through the decomposition, we derive the Covariate Fisher Information Matrix (cFIM), denoted as $G_f$, which is a finite-dimensional and computable representative of information extractable from the manifold's geometry. Indeed, by proving the Trace Theorem: $H_G(f)=\text{Tr}(G_f)$, we establish a rigorous foundation for the G-entropy previously introduced by us, thereby identifying it not merely as a gradient-based regularizer, but also as a fundamental geometric invariant representing the total explainable statistical information captured by the probability distribution associated with the model. Furthermore, we establish a link between $G_f$ and the second-order derivative (i.e. the curvature) of the KL-divergence, leading to the notion of Covariate Cramér-Rao Lower Bound(CRLB). We demonstrate that $G_f$ is congruent to the Efficient Fisher Information Matrix, thereby providing fundamental limits of variance for semi-parametric estimators. Finally, we apply our geometric framework to the Manifold Hypothesis, lifting the latter from a heuristic assumption into a testable condition of rank-deficiency within the cFIM. By defining the Information Capture Ratio, we provide a rigorous method for estimating intrinsic dimensionality in high-dimensional data. In short, our work bridges the gap between abstract information geometry and the demand of explainable AI, by providing a tractable path for revealing the statistical coverage and the efficiency of non-parametric models.
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