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cs.LG 方向，今日共计124篇
大模型相关(15篇)
【1】Explainable Mapper: Charting LLM Embedding Spaces Using Perturbation-Based Explanation and Verification Agents
标题：可解释映射器：使用基于扰动的解释和验证代理绘制LLM嵌入空间图表
链接：https://arxiv.org/abs/2507.18607
摘要：大型语言模型（LLM）产生高维嵌入，捕获单词，句子和概念之间丰富的语义和句法关系。通过映射图研究LLM嵌入空间的拓扑结构，使我们能够理解它们的底层结构。具体来说，映射图总结了嵌入空间的拓扑结构，其中每个节点表示一个拓扑邻域（包含一个嵌入簇），如果两个节点的相应邻域重叠，则边连接两个节点。然而，手动探索这些嵌入空间以揭示编码的语言特性需要相当大的人力。为了解决这一挑战，我们引入了一个框架，这些嵌入属性的半自动注释。为了组织探索过程，我们首先在映射图中定义可探索元素的分类，例如节点，边缘，路径，组件和轨迹。这些元素的注释通过两种类型的可定制的基于LLM的代理执行，这些代理采用扰动技术进行可扩展和自动化分析。这些代理有助于探索和解释映射器元素的特征，并验证生成的解释的鲁棒性。我们在一个可视化的分析工作空间内实例化的框架，并通过案例研究证明其有效性。特别是，我们复制的研究结果，从以前的研究BERT的嵌入性能在其架构的各个层，并提供进一步的观察到的拓扑邻域的语言特性。
摘要：Large language models (LLMs) produce high-dimensional embeddings that capture rich semantic and syntactic relationships between words, sentences, and concepts. Investigating the topological structures of LLM embedding spaces via mapper graphs enables us to understand their underlying structures. Specifically, a mapper graph summarizes the topological structure of the embedding space, where each node represents a topological neighborhood (containing a cluster of embeddings), and an edge connects two nodes if their corresponding neighborhoods overlap. However, manually exploring these embedding spaces to uncover encoded linguistic properties requires considerable human effort. To address this challenge, we introduce a framework for semi-automatic annotation of these embedding properties. To organize the exploration process, we first define a taxonomy of explorable elements within a mapper graph such as nodes, edges, paths, components, and trajectories. The annotation of these elements is executed through two types of customizable LLM-based agents that employ perturbation techniques for scalable and automated analysis. These agents help to explore and explain the characteristics of mapper elements and verify the robustness of the generated explanations. We instantiate the framework within a visual analytics workspace and demonstrate its effectiveness through case studies. In particular, we replicate findings from prior research on BERT's embedding properties across various layers of its architecture and provide further observations into the linguistic properties of topological neighborhoods.
【2】The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm
标题：LLM量化的几何结构：GPTQ作为Babai最近平面算法
链接：https://arxiv.org/abs/2507.18553
摘要：将大型语言模型（LLM）的权重从16位量化到更低的位宽，是将大型Transformers部署到更经济实惠的加速器上的事实上的方法。GPTQ成为LLM尺度下一次性训练后量化的标准方法之一。然而，它的内部工作被描述为一系列特殊的代数更新，模糊了任何几何意义或最坏情况的保证。在这项工作中，我们表明，当执行从后到前（从最后一个到第一个维度）的线性层，GPTQ是数学上相同的Babai的最近平面算法的经典最近向量问题（CVP）上定义的Hessian矩阵层的输入格。这种等价性基于复杂的数学论证，并且具有两个分析结果：（i）GPTQ误差传播步骤获得直观的几何解释;（ii）GPTQ继承了Babai算法在无裁剪条件下的误差上限。总而言之，这些结果将GPTQ置于坚实的理论基础上，并为引入数十年来在晶格算法方面取得的进展打开了大门，这些进展将用于设计未来的十亿参数模型量化算法。
摘要：Quantizing the weights of large language models (LLMs) from 16-bit to lower bitwidth is the de facto approach to deploy massive transformers onto more affordable accelerators. GPTQ emerged as one of the standard methods for one-shot post-training quantization at LLM scale. Yet, its inner workings are described as a sequence of ad-hoc algebraic updates that obscure any geometric meaning or worst-case guarantees. In this work, we show that, when executed back-to-front (from the last to first dimension) for a linear layer, GPTQ is mathematically identical to Babai's nearest plane algorithm for the classical closest vector problem (CVP) on a lattice defined by the Hessian matrix of the layer's inputs. This equivalence is based on a sophisticated mathematical argument, and has two analytical consequences: (i) the GPTQ error propagation step gains an intuitive geometric interpretation; (ii) GPTQ inherits the error upper bound of Babai's algorithm under the no-clipping condition. Taken together, these results place GPTQ on firm theoretical footing and open the door to importing decades of progress in lattice algorithms towards the design of future quantization algorithms for billion-parameter models.
【3】The Moral Gap of Large Language Models
标题：大型语言模型的道德差距
链接：https://arxiv.org/abs/2507.18523
备注：preprint
摘要：道德基础检测对于分析社会话语和开发符合道德的人工智能系统至关重要。虽然大型语言模型在不同的任务中表现出色，但它们在专业道德推理方面的表现仍不清楚。 这项研究使用ROC、PR和DET曲线分析，首次在Twitter和Reddit数据集上对最先进的LLM和微调Transformers进行了全面比较。 结果显示，大量的性能差距，与法学硕士表现出高的假阴性率和系统的道德内容检测不足，尽管及时的工程努力。这些发现表明，特定任务的微调仍然优于道德推理应用程序的提示。
摘要：Moral foundation detection is crucial for analyzing social discourse and developing ethically-aligned AI systems. While large language models excel across diverse tasks, their performance on specialized moral reasoning remains unclear. This study provides the first comprehensive comparison between state-of-the-art LLMs and fine-tuned transformers across Twitter and Reddit datasets using ROC, PR, and DET curve analysis. Results reveal substantial performance gaps, with LLMs exhibiting high false negative rates and systematic under-detection of moral content despite prompt engineering efforts. These findings demonstrate that task-specific fine-tuning remains superior to prompting for moral reasoning applications.
【4】Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
标题：并非所有功能都值得关注：使用语言模型生成表格数据的图形引导依赖性学习
链接：https://arxiv.org/abs/2507.18504
摘要 ：大型语言模型（LLM）通过对文本化特征值对进行建模，显示出强大的表格数据生成潜力。然而，表格数据固有地表现出稀疏的特征级依赖性，其中许多特征交互在结构上是无关紧要的。这造成了根本的不匹配，因为LLM的自我注意力机制不可避免地将焦点分布在所有对上，稀释了对关键关系的注意力，特别是在具有复杂依赖关系或语义模糊特征的数据集中。为了解决这个问题，我们提出了Grade（Graph-Guided Dependency Learning），这是一种将稀疏依赖图明确集成到LLM注意力机制中的新方法。Grade采用了一个轻量级的动态图学习模块，由外部提取的函数依赖关系指导，优先考虑关键特征交互，同时抑制不相关的交互。我们在不同的真实世界数据集上的实验表明，Grade在复杂数据集上的性能比现有的基于LLM的方法高出12%，同时在合成数据质量方面与最先进的方法取得了竞争性的结果。我们的方法是最低限度的侵入性，但有效的，提供了一个实用的解决方案，结构感知的表格数据建模与LLM。
摘要：Large Language Models (LLMs) have shown strong potential for tabular data generation by modeling textualized feature-value pairs. However, tabular data inherently exhibits sparse feature-level dependencies, where many feature interactions are structurally insignificant. This creates a fundamental mismatch as LLMs' self-attention mechanism inevitably distributes focus across all pairs, diluting attention on critical relationships, particularly in datasets with complex dependencies or semantically ambiguous features. To address this limitation, we propose GraDe (Graph-Guided Dependency Learning), a novel method that explicitly integrates sparse dependency graphs into LLMs' attention mechanism. GraDe employs a lightweight dynamic graph learning module guided by externally extracted functional dependencies, prioritizing key feature interactions while suppressing irrelevant ones. Our experiments across diverse real-world datasets demonstrate that GraDe outperforms existing LLM-based approaches by up to 12% on complex datasets while achieving competitive results with state-of-the-art approaches in synthetic data quality. Our method is minimally intrusive yet effective, offering a practical solution for structure-aware tabular data modeling with LLMs.
【5】Restoring Rhythm: Punctuation Restoration Using Transformer Models for Bangla, a Low-Resource Language
标题：恢复节奏：使用低资源语言孟加拉语的Transformer模型恢复标点符号
链接：https://arxiv.org/abs/2507.18448
摘要：标点恢复增强了文本的可读性，对于自动语音识别（ASR）中的后处理任务至关重要，特别是对于孟加拉语这样的低资源语言。在这项研究中，我们探讨了基于transformer的模型，特别是XLM-RoBERTA-large，自动恢复标点符号在unpunctuated孟加拉语文本的应用。我们专注于预测四个标点符号：句号，逗号，问号和感叹号在不同的文本域。为了解决注释资源的稀缺性，我们构建了一个大型的，各种各样的训练语料库，并应用数据增强技术。我们的性能最好的模型，使用alpha = 0.20%的增强因子进行训练，在新闻测试集上达到了97.1%的准确率，在参考集上达到了91.2%，在ASR集上达到了90.2%。 结果表明，强大的泛化参考和ASR成绩单，证明了该模型在现实世界中的有效性，嘈杂的场景。这项工作为孟加拉语标点符号的恢复建立了一个强大的基线，并提供了公开的数据集和代码，以支持未来在低资源NLP方面的研究。
摘要：Punctuation restoration enhances the readability of text and is critical for post-processing tasks in Automatic Speech Recognition (ASR), especially for low-resource languages like Bangla. In this study, we explore the application of transformer-based models, specifically XLM-RoBERTa-large, to automatically restore punctuation in unpunctuated Bangla text. We focus on predicting four punctuation marks: period, comma, question mark, and exclamation mark across diverse text domains. To address the scarcity of annotated resources, we constructed a large, varied training corpus and applied data augmentation techniques. Our best-performing model, trained with an augmentation factor of alpha = 0.20%, achieves an accuracy of 97.1% on the News test set, 91.2% on the Reference set, and 90.2% on the ASR set. Results show strong generalization to reference and ASR transcripts, demonstrating the model's effectiveness in real-world, noisy scenarios. This work establishes a strong baseline for Bangla punctuation restoration and contributes publicly available datasets and code to support future research in low-resource NLP.
【6】FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs
标题：FinDPO：通过LLM偏好优化进行数学交易的财务情绪分析
链接：https://arxiv.org/abs/2507.18417
摘要：在线金融相关文本数据中表达的观点对交易决策和市场走势产生越来越深远的影响。这一趋势凸显了情绪分析作为量化此类意见性质和强度的工具的重要作用。随着生成式人工智能（GenAI）的快速发展，监督微调（SFT）大型语言模型（LLM）已成为金融情绪分析的事实标准。然而，SFT范式可能导致对训练数据的记忆，并且通常无法推广到看不见的样本。这是金融领域的一个关键限制，模型必须适应以前未观察到的事件和细致入微的金融领域特定语言。为此，我们引入了FinDPO，这是第一个基于直接偏好优化（DPO）的训练后人类偏好对齐的金融特定LLM框架。拟议的FinDPO在标准情感分类基准上实现了最先进的性能，平均比现有的监督微调模型高出11%。独特的是，FinDPO框架能够通过新颖的“logit-to-score”转换将微调的因果LLM整合到现实的投资组合策略中，该转换将离散的情绪预测转换为连续的，可排名的情绪得分（概率）。通过这种方式，模拟表明，FinDPO是第一种基于情绪的方法，即使在5个基点的实际交易成本下，也能保持67%的年正回报率和强劲的风险调整后业绩，如夏普比率2.0所示。
摘要：Opinions expressed in online finance-related textual data are having an increasingly profound impact on trading decisions and market movements. This trend highlights the vital role of sentiment analysis as a tool for quantifying the nature and strength of such opinions. With the rapid development of Generative AI (GenAI), supervised fine-tuned (SFT) large language models (LLMs) have become the de facto standard for financial sentiment analysis. However, the SFT paradigm can lead to memorization of the training data and often fails to generalize to unseen samples. This is a critical limitation in financial domains, where models must adapt to previously unobserved events and the nuanced, domain-specific language of finance. To this end, we introduce FinDPO, the first finance-specific LLM framework based on post-training human preference alignment via Direct Preference Optimization (DPO). The proposed FinDPO achieves state-of-the-art performance on standard sentiment classification benchmarks, outperforming existing supervised fine-tuned models by 11% on the average. Uniquely, the FinDPO framework enables the integration of a fine-tuned causal LLM into realistic portfolio strategies through a novel 'logit-to-score' conversion, which transforms discrete sentiment predictions into continuous, rankable sentiment scores (probabilities). In this way, simulations demonstrate that FinDPO is the first sentiment-based approach to maintain substantial positive returns of 67% annually and strong risk-adjusted performance, as indicated by a Sharpe ratio of 2.0, even under realistic transaction costs of 5 basis points (bps).
【7】CLEAR: Error Analysis via LLM-as-a-Judge Made Easy
标题：清晰：通过LLM作为法官进行错误分析变得简单
链接：https://arxiv.org/abs/2507.18392
摘要：大型语言模型（LLM）的评估越来越依赖于其他LLM充当法官。然而，目前的评估范式通常只给出一个分数或排名，回答哪个模型更好，但没有回答为什么。虽然对基准测试至关重要，但这些顶级分数掩盖了模型性能背后的具体可操作原因。为了弥合这一差距，我们引入了CLEAR，这是一个用于基于LLM的错误分析的交互式开源软件包。CLEAR首先生成每个实例的文本反馈，然后创建一组系统级错误问题，并量化每个识别问题的流行程度。我们的软件包还为用户提供了一个交互式仪表板，允许通过聚合可视化进行全面的错误分析，应用交互式过滤器来隔离特定的问题或分数范围，并深入到描述特定行为模式的各个实例。我们演示了RAG和数学基准的CLEAR分析，并通过用户案例研究展示其实用性。
摘要 ：The evaluation of Large Language Models (LLMs) increasingly relies on other LLMs acting as judges. However, current evaluation paradigms typically yield a single score or ranking, answering which model is better but not why. While essential for benchmarking, these top-level scores obscure the specific, actionable reasons behind a model's performance. To bridge this gap, we introduce CLEAR, an interactive, open-source package for LLM-based error analysis. CLEAR first generates per-instance textual feedback, then it creates a set of system-level error issues, and quantifies the prevalence of each identified issue. Our package also provides users with an interactive dashboard that allows for a comprehensive error analysis through aggregate visualizations, applies interactive filters to isolate specific issues or score ranges, and drills down to the individual instances that exemplify a particular behavioral pattern. We demonstrate CLEAR analysis for RAG and Math benchmarks, and showcase its utility through a user case study.
【8】Efficient Uncertainty in LLMs through Evidential Knowledge Distillation
标题：通过证据知识提炼实现LLM的有效不确定性
链接：https://arxiv.org/abs/2507.18366
摘要：准确的不确定性量化仍然是标准LLM的关键挑战，促使采用贝叶斯和基于集合的方法。然而，这样的方法通常需要计算上昂贵的采样，涉及多个前向通道以有效地估计预测不确定性。 在本文中，我们介绍了一种新的方法，使高效和有效的不确定性估计LLM不牺牲性能。具体来说，我们将不确定性感知的教师模型（最初需要多个正向传递）提取为共享相同架构但使用低秩自适应（LoRA）进行微调的紧凑学生模型。我们比较了两种不同的蒸馏策略：一种是学生采用传统的基于softmax的输出，另一种是学生利用Dirichlet分布的输出，通过证据学习来明确地建模认识的不确定性。 对分类数据集的实证评估表明，这些学生可以实现与教师模型相当或更好的预测和不确定性量化性能，同时只需要一个向前传递。据我们所知，这是第一次证明，即时和强大的不确定性量化可以实现在LLM通过证据蒸馏。
摘要：Accurate uncertainty quantification remains a key challenge for standard LLMs, prompting the adoption of Bayesian and ensemble-based methods. However, such methods typically necessitate computationally expensive sampling, involving multiple forward passes to effectively estimate predictive uncertainty. In this paper, we introduce a novel approach enabling efficient and effective uncertainty estimation in LLMs without sacrificing performance. Specifically, we distill uncertainty-aware teacher models - originally requiring multiple forward passes - into compact student models sharing the same architecture but fine-tuned using Low-Rank Adaptation (LoRA). We compare two distinct distillation strategies: one in which the student employs traditional softmax-based outputs, and another in which the student leverages Dirichlet-distributed outputs to explicitly model epistemic uncertainty via evidential learning. Empirical evaluations on classification datasets demonstrate that such students can achieve comparable or superior predictive and uncertainty quantification performance relative to their teacher models, while critically requiring only a single forward pass. To our knowledge, this is the first demonstration that immediate and robust uncertainty quantification can be achieved in LLMs through evidential distillation.
【9】When Noisy Labels Meet Class Imbalance on Graphs: A Graph Augmentation Method with LLM and Pseudo Label
标题：当有噪标签遇到图上的类不平衡时：一种具有LLM和伪标签的图增强方法
链接：https://arxiv.org/abs/2507.18153
摘要：类不平衡图节点分类是一个实用但尚未充分研究的研究问题。虽然最近的研究试图解决这个问题，他们通常假设干净和可靠的标签时，处理类不平衡图。这个假设经常违反真实世界图的性质，其中标签经常包含噪声。鉴于这一差距，本文系统地研究了具有噪声标签的类不平衡图的鲁棒节点分类。我们提出了GraphALP，一种基于大型语言模型（LLM）和伪标记技术的新型图增强框架。具体来说，我们设计了一个基于LLM的过采样方法来生成合成少数节点，产生标签准确的少数节点，以减轻类不平衡。基于类平衡图，我们提出了一种动态加权的伪标签方法，以获得高置信度的伪标签，以降低标签噪声比。此外，我们还实现了一个二级LLM引导的过采样机制，以减轻由伪标签引起的潜在类分布偏斜。实验结果表明，GraphALP在具有噪声标签的类不平衡图上实现了优于现有方法的性能。
摘要：Class-imbalanced graph node classification is a practical yet underexplored research problem. Although recent studies have attempted to address this issue, they typically assume clean and reliable labels when processing class-imbalanced graphs. This assumption often violates the nature of real-world graphs, where labels frequently contain noise. Given this gap, this paper systematically investigates robust node classification for class-imbalanced graphs with noisy labels. We propose GraphALP, a novel Graph Augmentation framework based on Large language models (LLMs) and Pseudo-labeling techniques. Specifically, we design an LLM-based oversampling method to generate synthetic minority nodes, producing label-accurate minority nodes to alleviate class imbalance. Based on the class-balanced graphs, we develop a dynamically weighted pseudo-labeling method to obtain high-confidence pseudo labels to reduce label noise ratio. Additionally, we implement a secondary LLM-guided oversampling mechanism to mitigate potential class distribution skew caused by pseudo labels. Experimental results show that GraphALP achieves superior performance over state-of-the-art methods on class-imbalanced graphs with noisy labels.
【10】Policy Disruption in Reinforcement Learning:Adversarial Attack with Large Language Models and Critical State Identification
标题：强化学习中的政策破坏：大型语言模型和关键状态识别的对抗性攻击
链接：https://arxiv.org/abs/2507.18113
摘要：强化学习（RL）在机器人和自动驾驶等领域取得了显著的成功，但旨在误导RL系统的对抗性攻击仍然具有挑战性。现有的方法往往依赖于改变环境或政策，限制了其实用性。本文提出了一种对抗性攻击方法，其中环境中现有的代理引导目标策略输出次优动作，而不改变环境。我们提出了一个奖励迭代优化框架，该框架利用大型语言模型（LLM）来生成明确针对目标代理漏洞的对抗性奖励，从而提高诱导目标代理做出次优决策的有效性。此外，一个关键状态识别算法的目的是查明目标代理的最脆弱的状态，从受害者的次优行为导致整体性能显着下降。在不同环境下的实验结果表明，我们的方法优于现有的方法。
摘要：Reinforcement learning (RL) has achieved remarkable success in fields like robotics and autonomous driving, but adversarial attacks designed to mislead RL systems remain challenging. Existing approaches often rely on modifying the environment or policy, limiting their practicality. This paper proposes an adversarial attack method in which existing agents in the environment guide the target policy to output suboptimal actions without altering the environment. We propose a reward iteration optimization framework that leverages large language models (LLMs) to generate adversarial rewards explicitly tailored to the vulnerabilities of the target agent, thereby enhancing the effectiveness of inducing the target agent toward suboptimal decision-making. Additionally, a critical state identification algorithm is designed to pinpoint the target agent's most vulnerable states, where suboptimal behavior from the victim leads to significant degradation in overall performance. Experimental results in diverse environments demonstrate the superiority of our method over existing approaches.
【11】Squeeze10-LLM: Squeezing LLMs' Weights by 10 Times via a Staged Mixed-Precision Quantization Method
标题：Squeeze 10-LLM：通过分阶段混合精度量化方法将LLM的权重压缩10倍
链接：https://arxiv.org/abs/2507.18073
摘要：部署大型语言模型（LLM）是具有挑战性的，因为它们具有大量的参数和高计算成本。超低比特量化可以显著减少存储并加速推理，但是极端压缩（即，平均比特宽度<= 2）经常导致严重的性能下降。为了解决这个问题，我们提出了Squeeze 10-LLM，有效地将16位LLM的权重“压缩”10倍。具体来说，Squeeze 10-LLM是一个分级混合精度训练后量化（PTQ）框架，通过将80%的权重量化为1位，20%量化为4位，实现了平均每个权重1.6位。Squeeze 10 LLM具有两个关键创新：后二值化激活鲁棒性（PBAR）和全信息激活监督（FIAS）。PBAR是一种改进的权重重要性度量，它考虑了量化对激活的影响，提高了低位设置的准确性。FIAS是一种在量化期间保留完整激活信息以减轻跨层累积误差传播的策略。在LLaMA和LLaMA 2上的实验表明，Squeeze 10-LLM实现了最先进的子2位仅加权量化性能，在六个zero-shot分类任务上将平均准确率从43%提高到56%-与现有的PTQ方法相比有了显着的提高。我们的代码将在发布后发布。
摘要 ：Deploying large language models (LLMs) is challenging due to their massive parameters and high computational costs. Ultra low-bit quantization can significantly reduce storage and accelerate inference, but extreme compression (i.e., mean bit-width <= 2) often leads to severe performance degradation. To address this, we propose Squeeze10-LLM, effectively "squeezing" 16-bit LLMs' weights by 10 times. Specifically, Squeeze10-LLM is a staged mixed-precision post-training quantization (PTQ) framework and achieves an average of 1.6 bits per weight by quantizing 80% of the weights to 1 bit and 20% to 4 bits. We introduce Squeeze10LLM with two key innovations: Post-Binarization Activation Robustness (PBAR) and Full Information Activation Supervision (FIAS). PBAR is a refined weight significance metric that accounts for the impact of quantization on activations, improving accuracy in low-bit settings. FIAS is a strategy that preserves full activation information during quantization to mitigate cumulative error propagation across layers. Experiments on LLaMA and LLaMA2 show that Squeeze10-LLM achieves state-of-the-art performance for sub-2bit weight-only quantization, improving average accuracy from 43% to 56% on six zero-shot classification tasks--a significant boost over existing PTQ methods. Our code will be released upon publication.
【12】Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs
标题：使用LLM以多样化写作风格生成隐私保护综合评论
链接：https://arxiv.org/abs/2507.18055
摘要：越来越多地使用由大型语言模型（LLM）生成的合成数据为数据驱动的应用程序带来了机遇和挑战。虽然合成数据为现实世界数据提供了一种具有成本效益、可扩展的替代方案，以促进模型训练，但其多样性和隐私风险仍未得到充分研究。专注于基于文本的合成数据，我们提出了一套全面的指标来定量评估多样性（即，语言表达、情感和用户视角），以及隐私（即，重新识别风险和风格离群值）的合成数据集生成的几个国家的最先进的LLM。实验结果显示，LLM在生成多样化和隐私保护的合成数据方面的能力存在显着局限性。在评价结果的指导下，提出了一种基于人工智能的方法，以提高合成评论的多样性，同时保护评论者的隐私。
摘要：The increasing use of synthetic data generated by Large Language Models (LLMs) presents both opportunities and challenges in data-driven applications. While synthetic data provides a cost-effective, scalable alternative to real-world data to facilitate model training, its diversity and privacy risks remain underexplored. Focusing on text-based synthetic data, we propose a comprehensive set of metrics to quantitatively assess the diversity (i.e., linguistic expression, sentiment, and user perspective), and privacy (i.e., re-identification risk and stylistic outliers) of synthetic datasets generated by several state-of-the-art LLMs. Experiment results reveal significant limitations in LLMs' capabilities in generating diverse and privacy-preserving synthetic data. Guided by the evaluation results, a prompt-based approach is proposed to enhance the diversity of synthetic reviews while preserving reviewer privacy.
【13】ViGText: Deepfake Image Detection with Vision-Language Model Explanations and Graph Neural Networks
标题：ViGtext：利用视觉语言模型解析和图神经网络进行Deepfake图像检测
链接：https://arxiv.org/abs/2507.18031
摘要：deepfake技术的迅速崛起，产生了逼真但具有欺诈性的数字内容，威胁到媒体的真实性。传统的deepfake检测方法通常难以应对复杂的定制deepfake，特别是在针对恶意攻击的泛化和鲁棒性方面。本文介绍了ViGText，这是一种新的方法，它将图像与基于图的框架内的视觉大语言模型（VLLM）文本解释集成在一起，以提高deepfake检测。ViGText的新颖之处在于它将详细的解释与视觉数据相结合，因为它提供了比标题更具上下文意识的分析，而标题往往缺乏特异性，无法揭示微妙的不一致之处。ViGText系统地将图像划分为补丁，构建图像和文本图，并使用图神经网络（GNN）将它们集成进行分析，以识别deepfake。通过使用跨空间和频率域的多级特征提取，ViGText可以捕获细节，增强其检测复杂深度伪造的鲁棒性和准确性。大量的实验表明，ViGText显着增强了泛化能力，并在检测用户自定义的deepfake时实现了显着的性能提升。具体来说，在泛化评估下，平均F1分数从72.45%上升到98.32%，反映了该模型泛化到稳定扩散模型的不可见、微调变化的卓越能力。至于鲁棒性，与其他deepfake检测方法相比，ViGText的召回率提高了11.1%。当面临利用其基于图的架构的有针对性的攻击时，ViGText将分类性能下降限制在4%以下。ViGText使用详细的视觉和文本分析为检测deepfake设定了新标准，有助于确保媒体真实性和信息完整性。
摘要：The rapid rise of deepfake technology, which produces realistic but fraudulent digital content, threatens the authenticity of media. Traditional deepfake detection approaches often struggle with sophisticated, customized deepfakes, especially in terms of generalization and robustness against malicious attacks. This paper introduces ViGText, a novel approach that integrates images with Vision Large Language Model (VLLM) Text explanations within a Graph-based framework to improve deepfake detection. The novelty of ViGText lies in its integration of detailed explanations with visual data, as it provides a more context-aware analysis than captions, which often lack specificity and fail to reveal subtle inconsistencies. ViGText systematically divides images into patches, constructs image and text graphs, and integrates them for analysis using Graph Neural Networks (GNNs) to identify deepfakes. Through the use of multi-level feature extraction across spatial and frequency domains, ViGText captures details that enhance its robustness and accuracy to detect sophisticated deepfakes. Extensive experiments demonstrate that ViGText significantly enhances generalization and achieves a notable performance boost when it detects user-customized deepfakes. Specifically, average F1 scores rise from 72.45% to 98.32% under generalization evaluation, and reflects the model's superior ability to generalize to unseen, fine-tuned variations of stable diffusion models. As for robustness, ViGText achieves an increase of 11.1% in recall compared to other deepfake detection approaches. When facing targeted attacks that exploit its graph-based architecture, ViGText limits classification performance degradation to less than 4%. ViGText uses detailed visual and textual analysis to set a new standard for detecting deepfakes, helping ensure media authenticity and information integrity.
【14】LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction
标题：LSTM：用于服务级移动流量预测的LLM增强时空扩散模型
链接：https://arxiv.org/abs/2507.17795
备注：14 pages, 9 figures
摘要：针对个人用户的服务级移动流量预测对于提高网络效率和服务质量至关重要。然而，目前的预测方法是有限的，在不同的城市环境中的适应性，并产生不准确的结果，由于个人交通模式的高度不确定性，缺乏详细的环境背景下，不同的网络服务之间的复杂的依赖关系。这些挑战需要先进的建模技术，可以捕捉动态交通分布和丰富的环境特征。受最近扩散模型在分布建模和大语言模型（LLM）在上下文理解中的成功的启发，我们提出了一个LLM增强时空扩散模型（LSDM）。LSDM将扩散模型的生成能力与Transformers的自适应学习能力相结合，并通过捕获多模态环境信息以建模服务级别模式和动态的能力来增强。在实际服务水平数据集上的广泛评估表明，该模型在流量使用预测方面表现出色，具有出色的泛化能力和适应性。通过LLM整合上下文信息后，确定系数的性能至少提高了2.83%。与类似类型的模型（如CSDI）相比，均方根误差至少可以减少8.29%。代码和数据集将在https://github.com/SoftYuaneR/LSDM上提供。
摘要 ：Service-level mobile traffic prediction for individual users is essential for network efficiency and quality of service enhancement. However, current prediction methods are limited in their adaptability across different urban environments and produce inaccurate results due to the high uncertainty in personal traffic patterns, the lack of detailed environmental context, and the complex dependencies among different network services. These challenges demand advanced modeling techniques that can capture dynamic traffic distributions and rich environmental features. Inspired by the recent success of diffusion models in distribution modeling and Large Language Models (LLMs) in contextual understanding, we propose an LLM-Enhanced Spatio-temporal Diffusion Model (LSDM). LSDM integrates the generative power of diffusion models with the adaptive learning capabilities of transformers, augmented by the ability to capture multimodal environmental information for modeling service-level patterns and dynamics. Extensive evaluations on real-world service-level datasets demonstrate that the model excels in traffic usage predictions, showing outstanding generalization and adaptability. After incorporating contextual information via LLM, the performance improves by at least 2.83% in terms of the coefficient of determination. Compared to models of a similar type, such as CSDI, the root mean squared error can be reduced by at least 8.29%. The code and dataset will be available at: https://github.com/SoftYuaneR/LSDM.
【15】Adaptive Repetition for Mitigating Position Bias in LLM-Based Ranking
标题：自适应重复以减轻基于LLM的排名中的位置偏差
链接：https://arxiv.org/abs/2507.17788
摘要：当使用LLM根据给定的标准对项目进行排名或评估答案时，候选项目的顺序可能会影响模型的最终决策。这种对LLM提示中项目位置的敏感性称为位置偏差。先前的研究表明，即使在大型模型中也存在这种偏见，尽管其严重程度因模型和任务而异。除了位置偏差之外，LLM还表现出不同程度的低重复一致性，其中以相同的候选排序重复LLM调用可能导致不同的排名。为了解决这两种不一致性，一种常见的方法是使用不同的候选人排序多次提示模型，并通过多数投票汇总结果。然而，这种重复策略显著增加了计算成本。扩展先前的发现，我们观察到，方向-有利于在提示中的较早或较晚的候选人-以及实例之间的位置偏差的大小变化很大，即使在单个数据集内。这一观察结果强调了对每个实例的缓解策略的需要。为此，我们引入了一个动态的早期停止方法，自适应地确定每个实例所需的重复次数。在三个不同大小的LLM和两个任务（即重新排序和对齐）上评估我们的方法，我们证明了过渡到动态重复策略可以将LLM调用的数量平均减少81%，同时保持准确性。此外，我们提出了一种基于置信度的适应我们的早期停止方法，与静态重复相比，平均减少了87%的LLM调用，相对于我们最初的早期停止方法，只有轻微的准确性权衡。
摘要：When using LLMs to rank items based on given criteria, or evaluate answers, the order of candidate items can influence the model's final decision. This sensitivity to item positioning in a LLM's prompt is known as position bias. Prior research shows that this bias exists even in large models, though its severity varies across models and tasks. In addition to position bias, LLMs also exhibit varying degrees of low repetition consistency, where repeating the LLM call with the same candidate ordering can lead to different rankings. To address both inconsistencies, a common approach is to prompt the model multiple times with different candidate orderings and aggregate the results via majority voting. However, this repetition strategy, significantly increases computational costs. Extending prior findings, we observe that both the direction -- favoring either the earlier or later candidate in the prompt -- and magnitude of position bias across instances vary substantially, even within a single dataset. This observation highlights the need for a per-instance mitigation strategy. To this end, we introduce a dynamic early-stopping method that adaptively determines the number of repetitions required for each instance. Evaluating our approach across three LLMs of varying sizes and on two tasks, namely re-ranking and alignment, we demonstrate that transitioning to a dynamic repetition strategy reduces the number of LLM calls by an average of 81%, while preserving the accuracy. Furthermore, we propose a confidence-based adaptation to our early-stopping method, reducing LLM calls by an average of 87% compared to static repetition, with only a slight accuracy trade-off relative to our original early-stopping method.
Graph相关(图学习|图神经网络|图优化等)(3篇)
【1】GLANCE: Graph Logic Attention Network with Cluster Enhancement for Heterophilous Graph Representation Learning
标题：GLAANCE：具有集群增强的图逻辑注意力网络，用于异类图表示学习
链接：https://arxiv.org/abs/2507.18521
摘要：图神经网络（GNN）在从图结构数据中学习方面取得了巨大的成功，但通常在异质图上挣扎，其中连接的节点在特征或类别标签上不同。这种限制是由于不加选择的邻居聚集和高阶结构模式的不充分结合。为了解决这些挑战，我们提出了一个新的框架，即GLANCE（Graph Logic Attention Network with Cluster Enhancement），它集成了逻辑引导推理、动态图细化和自适应聚类，以增强图表示学习。GLANCE结合了用于可解释和结构化嵌入的逻辑层、用于去噪图结构的基于多头注意力的边缘修剪以及用于捕获全局模式的聚类机制。在基准数据集（包括康奈尔大学，得克萨斯州和威斯康星州）的实验结果表明，GLANCE实现了具有竞争力的性能，为异质图形场景提供了强大且可解释的解决方案。所提出的框架是轻量级的，适应性强，唯一适合于异形图的挑战。
摘要：Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data but often struggle on heterophilous graphs, where connected nodes differ in features or class labels. This limitation arises from indiscriminate neighbor aggregation and insufficient incorporation of higher-order structural patterns. To address these challenges, we propose GLANCE (Graph Logic Attention Network with Cluster Enhancement), a novel framework that integrates logic-guided reasoning, dynamic graph refinement, and adaptive clustering to enhance graph representation learning. GLANCE combines a logic layer for interpretable and structured embeddings, multi-head attention-based edge pruning for denoising graph structures, and clustering mechanisms for capturing global patterns. Experimental results in benchmark datasets, including Cornell, Texas, and Wisconsin, demonstrate that GLANCE achieves competitive performance, offering robust and interpretable solutions for heterophilous graph scenarios. The proposed framework is lightweight, adaptable, and uniquely suited to the challenges of heterophilous graphs.
【2】FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting
标题：FedSA-GCL：一个具有个性化聚合和机器人感知广播的半同步联邦图学习框架
链接：https://arxiv.org/abs/2507.18219
摘要：联邦图学习（Federated Graph Learning，FGL）是一种分布式学习范式，可以在位于多个本地系统上的大规模子图上进行协作训练。然而，大多数现有的FGL方法都依赖于同步通信，这会导致效率低下，并且在现实部署中通常不切实际。同时，目前的异步联邦学习（AFL）方法主要是为传统的任务，如图像分类和自然语言处理，没有考虑到图数据的独特拓扑属性。直接将这些方法应用于图学习可能会导致全局模型中的语义漂移和表示不一致。为了解决这些挑战，我们提出了FedSA-GCL，一个半异步的联邦框架，利用客户端之间的标签分布发散和图形拓扑特征，通过一种新的quartterCast机制进行有效的训练。我们使用Louvain和Metis分裂算法在多个真实世界的图数据集上评估FedSA-GCL，并将其与9个基线进行比较。大量的实验表明，我们的方法具有很强的鲁棒性和出色的效率，超过基线的平均2.92%的鲁汶和3.4%的梅蒂斯。
摘要：Federated Graph Learning (FGL) is a distributed learning paradigm that enables collaborative training over large-scale subgraphs located on multiple local systems. However, most existing FGL approaches rely on synchronous communication, which leads to inefficiencies and is often impractical in real-world deployments. Meanwhile, current asynchronous federated learning (AFL) methods are primarily designed for conventional tasks such as image classification and natural language processing, without accounting for the unique topological properties of graph data. Directly applying these methods to graph learning can possibly result in semantic drift and representational inconsistency in the global model. To address these challenges, we propose FedSA-GCL, a semi-asynchronous federated framework that leverages both inter-client label distribution divergence and graph topological characteristics through a novel ClusterCast mechanism for efficient training. We evaluate FedSA-GCL on multiple real-world graph datasets using the Louvain and Metis split algorithms, and compare it against 9 baselines. Extensive experiments demonstrate that our method achieves strong robustness and outstanding efficiency, outperforming the baselines by an average of 2.92% with the Louvain and by 3.4% with the Metis.
【3】Explainable Graph Neural Networks via Structural Externalities
标题
：通过结构外部性的可解释图神经网络
链接：https://arxiv.org/abs/2507.17848
摘要：图神经网络（GNN）在广泛的图相关任务中取得了出色的性能。然而，它们的“黑箱”性质对它们的可解释性提出了重大挑战，现有的方法往往无法有效地捕捉网络内节点之间复杂的交互模式。在这项工作中，我们提出了一个新的可解释性框架，GraphEXT，它利用合作博弈论和社会外部性的概念。GraphEXT将图节点划分为联盟，将原始图分解为独立的子图。通过将图结构集成为外部性并将Shapley值纳入外部性，GraphEXT通过节点在联盟之间过渡时对GNN预测的边际贡献来量化节点重要性。与传统的基于Shapley值的方法主要关注节点属性不同，我们的GraphEXT更加强调节点之间的相互作用以及结构变化对GNN预测的影响。对合成和真实世界数据集的实验研究表明，GraphEXT在不同GNN架构的保真度方面优于现有的基线方法，显着增强了GNN模型的可解释性。
摘要：Graph Neural Networks (GNNs) have achieved outstanding performance across a wide range of graph-related tasks. However, their "black-box" nature poses significant challenges to their explainability, and existing methods often fail to effectively capture the intricate interaction patterns among nodes within the network. In this work, we propose a novel explainability framework, GraphEXT, which leverages cooperative game theory and the concept of social externalities. GraphEXT partitions graph nodes into coalitions, decomposing the original graph into independent subgraphs. By integrating graph structure as an externality and incorporating the Shapley value under externalities, GraphEXT quantifies node importance through their marginal contributions to GNN predictions as the nodes transition between coalitions. Unlike traditional Shapley value-based methods that primarily focus on node attributes, our GraphEXT places greater emphasis on the interactions among nodes and the impact of structural changes on GNN predictions. Experimental studies on both synthetic and real-world datasets show that GraphEXT outperforms existing baseline methods in terms of fidelity across diverse GNN architectures , significantly enhancing the explainability of GNN models.
Transformer(2篇)
【1】Iwin Transformer: Hierarchical Vision Transformer using Interleaved Windows
标题：Iwin Transformer：使用交错Windows的分层视觉转换器
链接：https://arxiv.org/abs/2507.18405
备注：14 pages, 10 figures, Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence
摘要：我们介绍了Iwin Transformer，一种新颖的无位置嵌入的分层Vision Transformer，它可以通过创新的交错窗口注意力和深度可分离卷积的协作，直接从低分辨率到高分辨率进行微调。这种方法使用注意力来连接遥远的令牌，并应用卷积来连接相邻的令牌，从而在单个模块内实现全局信息交换，克服了Swin Transformer需要两个连续块来近似全局注意力的限制。在视觉基准测试上的大量实验表明，Iwin Transformer在图像分类（ImageNet-1 K上的87.4 top-1准确率）、语义分割和视频动作识别等任务中表现出强大的竞争力。我们还验证了Iwin中的核心组件作为一个独立模块的有效性，该模块可以无缝地取代类条件图像生成中的自我注意模块。Iwin Transformer引入的概念和方法有可能激发未来的研究，例如视频生成中的Iwin 3D Attention。代码和型号可在https://github.com/cominder/Iwin-Transformer上获得。
摘要：We introduce Iwin Transformer, a novel position-embedding-free hierarchical vision transformer, which can be fine-tuned directly from low to high resolution, through the collaboration of innovative interleaved window attention and depthwise separable convolution. This approach uses attention to connect distant tokens and applies convolution to link neighboring tokens, enabling global information exchange within a single module, overcoming Swin Transformer's limitation of requiring two consecutive blocks to approximate global attention. Extensive experiments on visual benchmarks demonstrate that Iwin Transformer exhibits strong competitiveness in tasks such as image classification (87.4 top-1 accuracy on ImageNet-1K), semantic segmentation and video action recognition. We also validate the effectiveness of the core component in Iwin as a standalone module that can seamlessly replace the self-attention module in class-conditional image generation. The concepts and methods introduced by the Iwin Transformer have the potential to inspire future research, like Iwin 3D Attention in video generation. The code and models are available at https://github.com/cominder/Iwin-Transformer.
【2】State of Health Estimation of Batteries Using a Time-Informed Dynamic Sequence-Inverted Transformer
标题：基于时间信息动态时序Transformer的电池健康状态估计
链接：https://arxiv.org/abs/2507.18320
备注：11 pages, 3 figures
摘要：在过去十年中，电池驱动的汽车和储能系统的快速采用使得电池健康监测变得越来越重要。电池在这些系统的效率和安全性方面发挥着核心作用，但由于重复的充放电循环，它们不可避免地会随着时间的推移而退化。这种退化导致能源效率降低和潜在的过热，造成严重的安全问题。因此，准确估计电池的健康状态（SoH）对于确保操作可靠性和安全性是至关重要的。已经提出了几种机器学习架构，例如LSTM，Transformers和基于编码器的模型，以从放电循环数据中估计SoH。然而，这些模型与现实世界测量中固有的不规则性作斗争：放电读数通常以不均匀的间隔记录，放电周期的长度变化很大。为了解决这个问题，大多数现有的方法从序列中提取特征，而不是完整地处理它们，这引入了信息丢失并损害了准确性。为了克服这些挑战，我们提出了一种新的架构：时间通知动态序列反转Transformer（TIDSIT）。TIDSIT采用连续时间嵌入来有效地表示不规则采样数据，并利用具有时间注意力机制的填充序列来管理可变长度的输入，而不会丢弃序列信息。在NASA电池退化数据集上的实验结果表明，TIDSIT的性能明显优于现有模型，预测误差降低了50%以上，SoH预测误差保持在0.58%以下。此外，该体系结构是可推广的，并在涉及不规则时间序列数据的健康监测任务中具有更广泛的应用前景。
摘要：The rapid adoption of battery-powered vehicles and energy storage systems over the past decade has made battery health monitoring increasingly critical. Batteries play a central role in the efficiency and safety of these systems, yet they inevitably degrade over time due to repeated charge-discharge cycles. This degradation leads to reduced energy efficiency and potential overheating, posing significant safety concerns. Accurate estimation of a State of Health (SoH) of battery is therefore essential for ensuring operational reliability and safety. Several machine learning architectures, such as LSTMs, transformers, and encoder-based models, have been proposed to estimate SoH from discharge cycle data. However, these models struggle with the irregularities inherent in real-world measurements: discharge readings are often recorded at non-uniform intervals, and the lengths of discharge cycles vary significantly. To address this, most existing approaches extract features from the sequences rather than processing them in full, which introduces information loss and compromises accuracy. To overcome these challenges, we propose a novel architecture: Time-Informed Dynamic Sequence Inverted Transformer (TIDSIT). TIDSIT incorporates continuous time embeddings to effectively represent irregularly sampled data and utilizes padded sequences with temporal attention mechanisms to manage variable-length inputs without discarding sequence information. Experimental results on the NASA battery degradation dataset show that TIDSIT significantly outperforms existing models, achieving over 50% reduction in prediction error and maintaining an SoH prediction error below 0.58%. Furthermore, the architecture is generalizable and holds promise for broader applications in health monitoring tasks involving irregular time-series data.
GAN|对抗|攻击|生成相关(4篇)
【1】Demystify Protein Generation with Hierarchical Conditional Diffusion Models
标题：用分层条件扩散模型揭开蛋白质生成的神秘面纱
链接：https://arxiv.org/abs/2507.18603
摘要：产生新的和功能性的蛋白质序列对于生物学中的广泛应用是至关重要的。条件扩散模型的最新进展在蛋白质生成任务中表现出令人印象深刻的经验性能。然而，在从头蛋白质设计中，可靠的蛋白质生成仍然是一个开放的研究问题，特别是在条件扩散模型中。考虑到蛋白质的生物学功能是由多层次结构决定的，本文提出了一种新的多层次条件扩散模型，该模型综合了基于序列和基于结构的信息，能够有效地进行基于特定功能的端到端蛋白质设计。通过同时在不同层次上生成表示，我们的框架可以有效地模拟不同层次之间的固有层次关系，从而对所生成的蛋白质进行信息化和区分性表示。我们还提出了一个蛋白质MMD，一个新的可靠的评价指标，以评估质量生成的蛋白质与条件扩散模型。我们的新指标能够捕获真实和生成的蛋白质序列之间的分布和功能相似性，同时确保条件一致性。我们在基准数据集上进行了实验，在条件蛋白质生成任务上的结果证明了所提出的生成框架和评价指标的有效性。
摘要：Generating novel and functional protein sequences is critical to a wide range of applications in biology. Recent advancements in conditional diffusion models have shown impressive empirical performance in protein generation tasks. However, reliable generations of protein remain an open research question in de novo protein design, especially when it comes to conditional diffusion models. Considering the biological function of a protein is determined by multi-level structures, we propose a novel multi-level conditional diffusion model that integrates both sequence-based and structure-based information for efficient end-to-end protein design guided by specified functions. By generating representations at different levels simultaneously, our framework can effectively model the inherent hierarchical relations between different levels, resulting in an informative and discriminative representation of the generated protein. We also propose a Protein-MMD, a new reliable evaluation metric, to evaluate the quality of generated protein with conditional diffusion models. Our new metric is able to capture both distributional and functional similarities between real and generated protein sequences while ensuring conditional consistency. We experiment with the benchmark datasets, and the results on conditional protein generation tasks demonstrate the efficacy of the proposed generation framework and evaluation metric.
【2】Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based Image Synthesis
标题：通过基于GenAI的图像合成促进基于AI的皮肤病变分类器的公平性评估
链接：https://arxiv.org/abs/2507.17860
摘要：深度学习及其边缘应用的最新进展为黑色素瘤等皮肤癌的常规筛查革命提供了巨大的潜力。除了这项技术的预期好处，潜在的危险来自不可预见的和固有的偏见。因此，评估和改善这种制度的公平性至关重要。公平性评估的一个关键挑战是确保评估数据集充分代表不同的个人身份信息（PII）（性别，年龄和种族）和其他少数群体。在这一挑战的背景下，本研究利用最先进的生成AI（GenAI）LightningDiT模型来评估公开可用的黑色素瘤分类器的公平性。结果表明，使用高度真实的合成数据进行公平性评估是一个有希望的方向。然而，我们的研究结果表明，当用于评估的黑色素瘤检测模型在与支撑合成图像的数据集不同的数据上训练时，验证公平性变得困难。尽管如此，我们认为我们的方法为利用合成数据来衡量和增强医学成像GenAI系统的公平性提供了一个有价值的新途径。
摘要：Recent advancements in Deep Learning and its application on the edge hold great potential for the revolution of routine screenings for skin cancers like Melanoma. Along with the anticipated benefits of this technology, potential dangers arise from unforseen and inherent biases. Thus, assessing and improving the fairness of such systems is of utmost importance. A key challenge in fairness assessment is to ensure that the evaluation dataset is sufficiently representative of different Personal Identifiable Information (PII) (sex, age, and race) and other minority groups. Against the backdrop of this challenge, this study leverages the state-of-the-art Generative AI (GenAI) LightningDiT model to assess the fairness of publicly available melanoma classifiers. The results suggest that fairness assessment using highly realistic synthetic data is a promising direction. Yet, our findings indicate that verifying fairness becomes difficult when the melanoma-detection model used for evaluation is trained on data that differ from the dataset underpinning the synthetic images. Nonetheless, we propose that our approach offers a valuable new avenue for employing synthetic data to gauge and enhance fairness in medical-imaging GenAI systems.
【3】Wasserstein GAN-Based Precipitation Downscaling with Optimal Transport for Enhancing Perceptual Realism
标题：Wasserstein GAN基于降水量的最佳传输以增强感知现实主义
链接：https://arxiv.org/abs/2507.17798
摘要：高分辨率（HR）降水预测是必不可少的，以减少固定和局部强降雨的损害;然而，HR降水预报使用过程驱动的数值天气预报模型仍然具有挑战性。本研究提出使用Wasserstein生成对抗网络（WGAN）以最佳运输成本进行降水降尺度。与用均方误差训练的传统神经网络相比，WGAN生成了具有精细尺度结构的视觉逼真的降水场，即使WGAN在传统评估指标上表现出略低的性能。WGAN的博学批评家与人类感知现实主义有很好的相关性。基于案例的分析表明，评论家评分的巨大差异有助于识别不切实际的WGAN输出和参考数据中的潜在伪影。这些研究结果表明，WGAN框架不仅提高了降水降尺度的感知现实主义，而且为评估和质量控制降水数据集提供了一个新的视角。
摘要：High-resolution (HR) precipitation prediction is essential for reducing damage from stationary and localized heavy rainfall; however, HR precipitation forecasts using process-driven numerical weather prediction models remains challenging. This study proposes using Wasserstein Generative Adversarial Network (WGAN) to perform precipitation downscaling with an optimal transport cost. In contrast to a conventional neural network trained with mean squared error, the WGAN generated visually realistic precipitation fields with fine-scale structures even though the WGAN exhibited slightly lower performance on conventional evaluation metrics. The learned critic of WGAN correlated well with human perceptual realism. Case-based analysis revealed that large discrepancies in critic scores can help identify both unrealistic WGAN outputs and potential artifacts in the reference data. These findings suggest that the WGAN framework not only improves perceptual realism in precipitation downscaling but also offers a new perspective for evaluating and quality-controlling precipitation datasets.
【4】MultiKernelBench: A Multi-Platform Benchmark for Kernel Generation
标题：MultiKernelBench：内核生成的多平台基准
链接：https://arxiv.org/abs/2507.17773
摘要：使用大型语言模型（LLM）自动生成深度学习（DL）内核已成为一种很有前途的方法，可以减少编写高性能运算符实现所需的手动工作和特定于硬件的专业知识。然而，现有的基准评估LLM在这一领域遭受有限的硬件支持，粗粒度的内核分类，和不平衡的任务覆盖。为了解决这些限制，我们引入了MultiKernelBench，这是第一个全面的，多平台的基于LLM的DL内核生成基准。MultiKernelBench跨越14个定义良好的内核类别的285个任务，并支持三大硬件平台：Nvidia GPU，华为NPU和Google TPU。为了实现未来的可扩展性，我们设计了一个模块化的后端抽象层，它从核心基准测试基础设施中提取特定于平台的逻辑，从而可以轻松集成新的硬件平台。我们进一步提出了一个简单而有效的类别感知的一次性提示方法，通过提供类别内样本来提高生成质量。通过对七个最先进的LLM的系统评估，我们发现任务难度的显着变化，对训练暴露较少的平台的泛化能力差，以及有针对性的提示策略的有效性。MultiKernelBench可在https://github.com/wzzll123/MultiKernelBench上公开获取。
摘要 ：The automatic generation of deep learning (DL) kernels using large language models (LLMs) has emerged as a promising approach to reduce the manual effort and hardware-specific expertise required for writing high-performance operator implementations. However, existing benchmarks for evaluating LLMs in this domain suffer from limited hardware support, coarse-grained kernel categorization, and imbalanced task coverage. To address these limitations, we introduce MultiKernelBench, the first comprehensive, multi-platform benchmark for LLM-based DL kernel generation. MultiKernelBench spans 285 tasks across 14 well-defined kernel categories and supports three major hardware platforms: Nvidia GPUs, Huawei NPUs, and Google TPUs. To enable future extensibility, we design a modular backend abstraction layer that decouples platform-specific logic from the core benchmarking infrastructure, allowing easy integration of new hardware platforms. We further propose a simple yet effective category-aware one-shot prompting method that improves generation quality by providing in-category exemplars. Through systematic evaluations of seven state-of-the-art LLMs, we reveal significant variation in task difficulty, poor generalization to platforms with less training exposure, and the effectiveness of targeted prompting strategies. MultiKernelBench is publicly available at https://github.com/wzzll123/MultiKernelBench.
半/弱/无/有监督|不确定性|主动学习(5篇)
【1】A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation
标题：心电图描绘中半监督语义分割的多数据集基准
链接：https://arxiv.org/abs/2507.18323
备注：6 pages, 2 figures
摘要：心电图（ECG）描绘，即有意义的波形特征的分割，对于临床诊断至关重要。尽管最近使用深度学习取得了进展，但由于缺乏公开的注释数据集，进展受到限制。半监督学习通过利用丰富的未标记心电图数据提供了一种有前途的解决方案。在这项研究中，我们提出了第一个系统的基准半监督语义分割（SemiSeg）在心电图描绘。我们策划和统一了多个公共数据集，包括以前未充分使用的数据源，以支持强大而多样化的评估。我们采用了计算机视觉中五种代表性的SemiSeg算法，在两种不同的架构上实现它们：卷积网络和Transformer，并在两种不同的设置中进行评估：域内和跨域。此外，我们提出了ECG特定的培训配置和增强策略，并引入了标准化的评估框架。我们的研究结果表明，Transformer在半监督ECG描绘方面优于卷积网络。我们预计，我们的基准将作为推进半监督心电图描绘方法的基础，并将促进在这一领域的进一步研究。
摘要：Electrocardiogram (ECG) delineation, the segmentation of meaningful waveform features, is critical for clinical diagnosis. Despite recent advances using deep learning, progress has been limited by the scarcity of publicly available annotated datasets. Semi-supervised learning presents a promising solution by leveraging abundant unlabeled ECG data. In this study, we present the first systematic benchmark for semi-supervised semantic segmentation (SemiSeg) in ECG delineation. We curated and unified multiple public datasets, including previously underused sources, to support robust and diverse evaluation. We adopted five representative SemiSeg algorithms from computer vision, implemented them on two different architectures: the convolutional network and the transformer, and evaluated them in two different settings: in-domain and cross-domain. Additionally, we propose ECG-specific training configurations and augmentation strategies and introduce a standardized evaluation framework. Our results show that the transformer outperforms the convolutional network in semi-supervised ECG delineation. We anticipate that our benchmark will serve as a foundation for advancing semi-supervised ECG delineation methods and will facilitate further research in this domain.
【2】Self-Supervised Coarsening of Unstructured Grid with Automatic Differentiation
标题：非结构网格自监督自动微分粗化
链接：https://arxiv.org/abs/2507.18297
备注：None
摘要：由于现代数值模拟的高计算负荷，需要在保持合理精度的同时减小离散问题的大小的方法。在这项工作中，我们提出了一个原始的算法粗化的非结构化网格的基础上的概念，可微物理。我们实现这一点，采用k-均值聚类，自分化和随机最小化算法。我们证明了两个偏微分方程的设计算法的性能：一个线性抛物方程，管理轻微的可压缩流体在多孔介质中的流动和波动方程。我们的研究结果表明，在所考虑的情况下，我们减少了网格点的数量高达10倍，同时保留模型的变量动态的兴趣点。所提出的方法可以应用于演化偏微分方程描述的任意系统的仿真。
摘要：Due to the high computational load of modern numerical simulation, there is a demand for approaches that would reduce the size of discrete problems while keeping the accuracy reasonable. In this work, we present an original algorithm to coarsen an unstructured grid based on the concepts of differentiable physics. We achieve this by employing k-means clustering, autodifferentiation and stochastic minimization algorithms. We demonstrate performance of the designed algorithm on two PDEs: a linear parabolic equation which governs slightly compressible fluid flow in porous media and the wave equation. Our results show that in the considered scenarios, we reduced the number of grid points up to 10 times while preserving the modeled variable dynamics in the points of interest. The proposed approach can be applied to the simulation of an arbitrary system described by evolutionary partial differential equations.
【3】Learning from Hard Labels with Additional Supervision on Non-Hard-Labeled Classes
标题：向硬标签学习，并对非硬标签班级进行额外监督
链接：https://arxiv.org/abs/2507.18098
备注：32 pages, 11 figures
摘要：在由于观察成本或数据稀缺而导致训练数据有限的情况下，丰富与每个实例相关的标签信息对于构建高准确度分类模型至关重要。在这种情况下，通常不仅可以获得硬标签，还可以获得{\it additional supervision}，例如硬标签的置信度。这种设置自然会提出一些基本问题：什么样的额外监督在本质上是有益的？那么它们是如何提高泛化性能的呢？为了解决这些问题，我们提出了一个理论框架，将硬标签和额外监督都视为概率分布，并通过它们的仿射组合构建软标签。我们的理论分析表明，额外的监督的基本组成部分是不是分配的硬标签的置信度得分，而是分布在非硬标签类的信息。此外，我们证明了额外的监督和混合系数有助于完善的软标签的互补作用。直观地说，在概率单纯形中，额外的监督决定了代表硬标签的确定性分布应该向真实标签分布调整的方向，而混合系数控制着沿着该方向的步长。通过广义误差分析，我们从理论上刻画了附加监督及其混合系数对误差界的收敛速度和渐近值的影响。最后，我们通过实验证明，根据我们的理论，设计额外的监督可以提高分类精度，即使在一个简单的方式使用。
摘要：In scenarios where training data is limited due to observation costs or data scarcity, enriching the label information associated with each instance becomes crucial for building high-accuracy classification models. In such contexts, it is often feasible to obtain not only hard labels but also {\it additional supervision}, such as the confidences for the hard labels. This setting naturally raises fundamental questions: {\it What kinds of additional supervision are intrinsically beneficial?} And {\it how do they contribute to improved generalization performance?} To address these questions, we propose a theoretical framework that treats both hard labels and additional supervision as probability distributions, and constructs soft labels through their affine combination. Our theoretical analysis reveals that the essential component of additional supervision is not the confidence score of the assigned hard label, but rather the information of the distribution over the non-hard-labeled classes. Moreover, we demonstrate that the additional supervision and the mixing coefficient contribute to the refinement of soft labels in complementary roles. Intuitively, in the probability simplex, the additional supervision determines the direction in which the deterministic distribution representing the hard label should be adjusted toward the true label distribution, while the mixing coefficient controls the step size along that direction. Through generalization error analysis, we theoretically characterize how the additional supervision and its mixing coefficient affect both the convergence rate and asymptotic value of the error bound. Finally, we experimentally demonstrate that, based on our theory, designing additional supervision can lead to improved classification accuracy, even when utilized in a simple manner.
【4】A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry
标题：用于大功率射频设备和加速器组件中多动器故障预测的受监督机器学习框架：平面几何领域的案例研究
链接：https://arxiv.org/abs/2507.17881
摘要：微电子倍增效应是一种非线性电子雪崩现象，它会严重影响高功率射频器件和加速器系统的性能。准确预测不同材料和操作制度的二次电子倍增器的敏感性仍然是加速器组件设计和RF工程中的一个关键但计算密集的挑战。这项研究提出了监督机器学习（ML）的第一个应用程序，用于预测在两个表面的平面几何形状的二次电子碰撞敏感性。一个模拟衍生的数据集跨越六个不同的二次电子产额（SEY）材料的配置文件是用来训练回归模型-包括随机森林（RF），额外的树（ET），极端梯度提升（XGBoost），和漏斗结构的多层感知器（MLP）-预测的时间平均电子增长率，${\delta}_{avg}$。性能使用交集对并集（IoU），结构相似性指数（SSIM）和皮尔逊相关系数进行评估。基于树的模型在不相交的材料域中的泛化能力始终优于MLP。使用标量化目标函数训练的MLP在贝叶斯超参数优化期间结合了IoU和SSIM，具有5倍交叉验证，优于使用单目标损失函数训练的MLP。主成分分析表明，某些材料的性能下降源于不相交的特征空间分布，强调了更广泛的数据集覆盖的必要性。这项研究展示了基于ML的二次电子倍增器预测的前景和局限性，并为高级RF和加速器系统设计中的加速数据驱动建模奠定了基础。
摘要：Multipactor is a nonlinear electron avalanche phenomenon that can severely impair the performance of high-power radio frequency (RF) devices and accelerator systems. Accurate prediction of multipactor susceptibility across different materials and operational regimes remains a critical yet computationally intensive challenge in accelerator component design and RF engineering. This study presents the first application of supervised machine learning (ML) for predicting multipactor susceptibility in two-surface planar geometries. A simulation-derived dataset spanning six distinct secondary electron yield (SEY) material profiles is used to train regression models - including Random Forest (RF), Extra Trees (ET), Extreme Gradient Boosting (XGBoost), and funnel-structured Multilayer Perceptrons (MLPs) - to predict the time-averaged electron growth rate, ${\delta}_{avg}$. Performance is evaluated using Intersection over Union (IoU), Structural Similarity Index (SSIM), and Pearson correlation coefficient. Tree-based models consistently outperform MLPs in generalizing across disjoint material domains. MLPs trained using a scalarized objective function that combines IoU and SSIM during Bayesian hyperparameter optimization with 5-fold cross-validation outperform those trained with single-objective loss functions. Principal Component Analysis reveals that performance degradation for certain materials stems from disjoint feature-space distributions, underscoring the need for broader dataset coverage. This study demonstrates both the promise and limitations of ML-based multipactor prediction and lays the groundwork for accelerated, data-driven modeling in advanced RF and accelerator system design.
【5】CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography
标题：CM-UNet：X射线血管造影中冠状动脉分割的一种基于自我监督学习的模型
链接：https://arxiv.org/abs/2507.17779
备注：IEEE EMBC 2025, 7 pages, 6 figures
摘要：冠状动脉的准确分割仍然是临床实践中的一个重大挑战，阻碍了有效诊断和管理冠状动脉疾病的能力。缺乏用于模型训练的大型注释数据集加剧了这个问题，限制了可以帮助放射科医生的自动化工具的开发。为了解决这个问题，我们引入了CM-UNet，它利用未注释数据集的自我监督预训练和有限注释数据的转移学习，实现准确的疾病检测，同时最大限度地减少对大量手动注释的需求。微调CM-UNet，只有18个注释图像而不是500个，导致Dice得分下降15.2%，而没有预训练的基线模型下降46.5%。这表明自监督学习可以提高分割性能，减少对大型数据集的依赖。这是第一个强调自我监督学习在改善X射线血管造影冠状动脉分割中的重要性的研究之一，对提高临床实践中的诊断准确性具有潜在意义。通过提高X射线血管造影图像的分割准确性，所提出的方法旨在改善临床工作流程，减少放射科医生的工作量，并加快疾病检测，最终有助于改善患者的预后。源代码可在https://github.com/CamilleChallier/Contrastive-Masked-UNet上公开获取。
摘要：Accurate segmentation of coronary arteries remains a significant challenge in clinical practice, hindering the ability to effectively diagnose and manage coronary artery disease. The lack of large, annotated datasets for model training exacerbates this issue, limiting the development of automated tools that could assist radiologists. To address this, we introduce CM-UNet, which leverages self-supervised pre-training on unannotated datasets and transfer learning on limited annotated data, enabling accurate disease detection while minimizing the need for extensive manual annotations. Fine-tuning CM-UNet with only 18 annotated images instead of 500 resulted in a 15.2% decrease in Dice score, compared to a 46.5% drop in baseline models without pre-training. This demonstrates that self-supervised learning can enhance segmentation performance and reduce dependence on large datasets. This is one of the first studies to highlight the importance of self-supervised learning in improving coronary artery segmentation from X-ray angiography, with potential implications for advancing diagnostic accuracy in clinical practice. By enhancing segmentation accuracy in X-ray angiography images, the proposed approach aims to improve clinical workflows, reduce radiologists' workload, and accelerate disease detection, ultimately contributing to better patient outcomes. The source code is publicly available at https://github.com/CamilleChallier/Contrastive-Masked-UNet.
迁移|Zero/Few/One-Shot|自适应(7篇)
【1】SIDA: Synthetic Image Driven Zero-shot Domain Adaptation
标题：SIDA：合成图像驱动的Zero-Shot域自适应
链接：https://arxiv.org/abs/2507.18632
备注：Accepted to ACM MM 2025
摘要：Zero-shot域自适应是一种用于在不利用目标域图像数据的情况下使模型适应于目标域的方法。为了实现无目标图像的自适应，已有的研究利用CLIP的嵌入空间和文本描述来模拟类目标风格特征。尽管之前在zero-shot域自适应方面取得了成就，但我们观察到这些文本驱动的方法难以捕获复杂的现实变化，并且由于其对齐过程而显著增加了自适应时间。我们不依赖于文本描述，而是探索利用图像数据的解决方案，图像数据提供了多样化和更细粒度的风格线索。在这项工作中，我们提出了SIDA，一种新的和有效的zero-shot域适应方法，利用合成图像。为了生成合成图像，我们首先创建详细的、类似源的图像，并应用图像平移来反映目标域的风格。然后，我们利用这些合成图像的风格特征作为目标域的代理。基于这些特性，我们引入了领域混合和补丁风格转换模块，它们可以对真实世界的变化进行有效建模。特别是，“域混合”（Domain Mix）可混合多种样式以扩展域内表示，“面片样式传递”（Patch Style Transfer）可为各个面片指定不同的样式。我们证明了我们的方法的有效性，显示国家的最先进的性能在不同的zero-shot适应的情况下，特别是在具有挑战性的领域。此外，我们的方法通过显着减少整体适应时间来实现高效率。
摘要：Zero-shot domain adaptation is a method for adapting a model to a target domain without utilizing target domain image data. To enable adaptation without target images, existing studies utilize CLIP's embedding space and text description to simulate target-like style features. Despite the previous achievements in zero-shot domain adaptation, we observe that these text-driven methods struggle to capture complex real-world variations and significantly increase adaptation time due to their alignment process. Instead of relying on text descriptions, we explore solutions leveraging image data, which provides diverse and more fine-grained style cues. In this work, we propose SIDA, a novel and efficient zero-shot domain adaptation method leveraging synthetic images. To generate synthetic images, we first create detailed, source-like images and apply image translation to reflect the style of the target domain. We then utilize the style features of these synthetic images as a proxy for the target domain. Based on these features, we introduce Domain Mix and Patch Style Transfer modules, which enable effective modeling of real-world variations. In particular, Domain Mix blends multiple styles to expand the intra-domain representations, and Patch Style Transfer assigns different styles to individual patches. We demonstrate the effectiveness of our method by showing state-of-the-art performance in diverse zero-shot adaptation scenarios, particularly in challenging domains. Moreover, our approach achieves high efficiency by significantly reducing the overall adaptation time.
【2】SynC: Synthetic Image Caption Dataset Refinement with One-to-many Mapping for Zero-shot Image Captioning
标题
：SynC：通过一对多映射优化合成图像字幕数据集，实现Zero-Shot图像字幕
链接：https://arxiv.org/abs/2507.18616
备注：Accepted to ACM Multimedia 2025
摘要：Zero-shot图像字幕（ZIC）越来越多地利用由文本到图像（T2 I）模型生成的合成数据集，以减轻对昂贵的手动注释的需求。然而，这些T2 I模型通常会产生与其相应的输入字幕表现出语义不一致的图像（例如，缺少对象、不正确的属性），导致可能妨碍模型训练的嘈杂的合成图像-字幕对。现有的数据集修剪技术主要是为了去除网络抓取数据中的噪声文本。然而，这些方法不适合合成数据的独特挑战，其中字幕通常格式良好，但图像可能是不准确的表示。为了解决这个问题，我们引入了SynC，这是一个专门为ZIC改进合成图像标题数据集而设计的新框架。与传统的过滤或重新生成不同，SynC专注于将字幕重新分配给合成图像池中已经存在的语义上最对齐的图像。我们的方法采用了一个一对多的映射策略，最初检索多个相关的候选图像为每个标题。然后，我们应用一个周期一致性启发对齐评分器，通过验证其通过图像到文本检索检索原始标题的能力来选择最佳图像。广泛的评估表明，SynC在标准基准测试（MS-COCO、Flickr 30 k、NoCaps）上持续显著地提高了各种ZIC模型的性能，在多个场景中实现了最先进的结果。SynC提供了一种有效的策略来管理精细的合成数据，以增强ZIC。
摘要：Zero-shot Image Captioning (ZIC) increasingly utilizes synthetic datasets generated by text-to-image (T2I) models to mitigate the need for costly manual annotation. However, these T2I models often produce images that exhibit semantic misalignments with their corresponding input captions (e.g., missing objects, incorrect attributes), resulting in noisy synthetic image-caption pairs that can hinder model training. Existing dataset pruning techniques are largely designed for removing noisy text in web-crawled data. However, these methods are ill-suited for the distinct challenges of synthetic data, where captions are typically well-formed, but images may be inaccurate representations. To address this gap, we introduce SynC, a novel framework specifically designed to refine synthetic image-caption datasets for ZIC. Instead of conventional filtering or regeneration, SynC focuses on reassigning captions to the most semantically aligned images already present within the synthetic image pool. Our approach employs a one-to-many mapping strategy by initially retrieving multiple relevant candidate images for each caption. We then apply a cycle-consistency-inspired alignment scorer that selects the best image by verifying its ability to retrieve the original caption via image-to-text retrieval. Extensive evaluations demonstrate that SynC consistently and significantly improves performance across various ZIC models on standard benchmarks (MS-COCO, Flickr30k, NoCaps), achieving state-of-the-art results in several scenarios. SynC offers an effective strategy for curating refined synthetic data to enhance ZIC.
【3】Low-rank adaptive physics-informed HyperDeepONets for solving differential equations
标题：用于求解微分方程的低秩自适应物理信息HyperDeepONets
链接：https://arxiv.org/abs/2507.18346
备注：14 pages, 6 figures, 5 tables
摘要：Lee，Cho和Hwang [ICLR，2023]引入了HyperDeepONets作为算子学习的替代架构，其中超网络为DeepONet的主干网络生成权重。虽然这提高了表现力，但由于需要大量的输出参数，它会导致高内存和计算成本。在这项工作中，我们在物理信息机器学习设置中引入了一种变体PI-LoRA-HyperDeepONets，它利用低秩自适应（LoRA）通过将超网络的输出层权重矩阵分解为两个较小的低秩矩阵来降低复杂性。这减少了可训练参数的数量，同时引入了主干网络权重的额外正则化。通过对普通方程和偏微分方程的广泛实验，我们表明PI-LoRA-HyperDeepONets的参数减少了高达70%，并且在预测准确性和泛化方面始终优于常规HyperDeepONets。
摘要：HyperDeepONets were introduced in Lee, Cho and Hwang [ICLR, 2023] as an alternative architecture for operator learning, in which a hypernetwork generates the weights for the trunk net of a DeepONet. While this improves expressivity, it incurs high memory and computational costs due to the large number of output parameters required. In this work we introduce, in the physics-informed machine learning setting, a variation, PI-LoRA-HyperDeepONets, which leverage low-rank adaptation (LoRA) to reduce complexity by decomposing the hypernetwork's output layer weight matrix into two smaller low-rank matrices. This reduces the number of trainable parameters while introducing an extra regularization of the trunk networks' weights. Through extensive experiments on both ordinary and partial differential equations we show that PI-LoRA-HyperDeepONets achieve up to 70\% reduction in parameters and consistently outperform regular HyperDeepONets in terms of predictive accuracy and generalization.
【4】GeoAvatar: Adaptive Geometrical Gaussian Splatting for 3D Head Avatar
标题：GeoAvatar：3D头部化身的自适应几何高斯飞溅
链接：https://arxiv.org/abs/2507.18155
备注：ICCV 2025, Project page: this https URL
摘要：尽管最近在3D头部化身生成方面取得了进展，但平衡身份保留，即，重建，具有新颖的姿势和表情，即，动画，仍然是一个挑战。现有的方法很难使高斯适应面部区域的不同几何偏差，导致质量不佳。为了解决这个问题，我们提出了GeoAvatar，一个自适应几何高斯溅射的框架。GeoAvatar利用自适应预分配阶段（APS），这是一种无监督方法，可将高斯分割为刚性和柔性集合，以进行自适应偏移正则化。然后，基于嘴部解剖学和动力学，我们引入了一种新的嘴部结构和局部变形策略，以提高嘴部动画的逼真度。最后，我们提出了一个正则化损失高斯和3DMM面之间的精确操纵。此外，我们还发布了DynamicFace，这是一个具有高度表现力的面部运动的视频数据集。大量的实验表明，GeoAvatar的优越性相比，国家的最先进的方法在重建和新颖的动画场景。
摘要：Despite recent progress in 3D head avatar generation, balancing identity preservation, i.e., reconstruction, with novel poses and expressions, i.e., animation, remains a challenge. Existing methods struggle to adapt Gaussians to varying geometrical deviations across facial regions, resulting in suboptimal quality. To address this, we propose GeoAvatar, a framework for adaptive geometrical Gaussian Splatting. GeoAvatar leverages Adaptive Pre-allocation Stage (APS), an unsupervised method that segments Gaussians into rigid and flexible sets for adaptive offset regularization. Then, based on mouth anatomy and dynamics, we introduce a novel mouth structure and the part-wise deformation strategy to enhance the animation fidelity of the mouth. Finally, we propose a regularization loss for precise rigging between Gaussians and 3DMM faces. Moreover, we release DynamicFace, a video dataset with highly expressive facial motions. Extensive experiments show the superiority of GeoAvatar compared to state-of-the-art methods in reconstruction and novel animation scenarios.
【5】Zero-Shot Dynamic Concept Personalization with Grid-Based LoRA
标题：利用基于网格的LoRA实现Zero-Shot动态概念个性化
链接：https://arxiv.org/abs/2507.17963
备注：Project Page and Video : this https URL
摘要：文本到视频生成的最新进展使得能够从文本和图像提示进行高质量的合成。虽然从单个视频中捕获特定于主题的外观和运动的动态概念的个性化现在是可行的，但大多数现有方法需要对每个实例进行微调，限制了可扩展性。我们介绍了一个完全zero-shot框架的动态概念个性化的文本到视频模型。我们的方法利用结构化的2x2视频网格，在空间上组织输入和输出对，从而能够训练轻量级Grid-LoRA适配器，以便在这些网格中进行编辑和合成。在推理时，专用的Grid Fill模块完成部分观察到的布局，产生时间上连贯和身份保留的输出。一旦经过训练，整个系统就可以在一次向前传递中运行，从而推广到以前看不见的动态概念，而无需任何测试时间优化。广泛的实验证明了高质量和一致的结果，在广泛的主题超出了训练的概念和编辑场景。
摘要 ：Recent advances in text-to-video generation have enabled high-quality synthesis from text and image prompts. While the personalization of dynamic concepts, which capture subject-specific appearance and motion from a single video, is now feasible, most existing methods require per-instance fine-tuning, limiting scalability. We introduce a fully zero-shot framework for dynamic concept personalization in text-to-video models. Our method leverages structured 2x2 video grids that spatially organize input and output pairs, enabling the training of lightweight Grid-LoRA adapters for editing and composition within these grids. At inference, a dedicated Grid Fill module completes partially observed layouts, producing temporally coherent and identity preserving outputs. Once trained, the entire system operates in a single forward pass, generalizing to previously unseen dynamic concepts without any test-time optimization. Extensive experiments demonstrate high-quality and consistent results across a wide range of subjects beyond trained concepts and editing scenarios.
【6】UrbanPulse: A Cross-City Deep Learning Framework for Ultra-Fine-Grained Population Transfer Prediction
标题：UrbanPulse：用于超细粒度人口转移预测的跨城市深度学习框架
链接：https://arxiv.org/abs/2507.17924
摘要：准确的人口流动预测对于城市规划、交通管理和公共卫生至关重要。然而，现有的方法面临着关键的限制：传统模型依赖于静态空间假设，深度学习模型难以进行跨城市泛化，大型语言模型（LLM）在无法捕获空间结构的同时会产生很高的计算成本。此外，许多方法通过聚类兴趣点（POI）或将覆盖范围限制在子区域来牺牲分辨率，从而限制了它们在城市范围内分析的实用性。我们介绍了UrbanPulse，这是一个可扩展的深度学习框架，通过将每个POI视为单个节点，提供超细粒度的全市OD流量预测。它结合了时间图卷积编码器和基于变换的解码器来模拟多尺度时空依赖性。为了确保在城市环境中的鲁棒泛化，UrbanPulse采用了三个阶段的迁移学习策略：大规模城市图的预训练，冷启动适应和强化学习微调。在加利福尼亚州三个大都市地区超过1.03亿条清洁GPS记录的评估中，UrbanPulse实现了最先进的准确性和可扩展性。通过有效的迁移学习，UrbanPulse朝着在不同城市的实践中部署高分辨率、人工智能驱动的城市预测迈出了关键一步。
摘要：Accurate population flow prediction is essential for urban planning, transportation management, and public health. Yet existing methods face key limitations: traditional models rely on static spatial assumptions, deep learning models struggle with cross-city generalization, and Large Language Models (LLMs) incur high computational costs while failing to capture spatial structure. Moreover, many approaches sacrifice resolution by clustering Points of Interest (POIs) or restricting coverage to subregions, limiting their utility for city-wide analytics. We introduce UrbanPulse, a scalable deep learning framework that delivers ultra-fine-grained, city-wide OD flow predictions by treating each POI as an individual node. It combines a temporal graph convolutional encoder with a transformer-based decoder to model multi-scale spatiotemporal dependencies. To ensure robust generalization across urban contexts, UrbanPulse employs a three-stage transfer learning strategy: pretraining on large-scale urban graphs, cold-start adaptation, and reinforcement learning fine-tuning.Evaluated on over 103 million cleaned GPS records from three metropolitan areas in California, UrbanPulse achieves state-of-the-art accuracy and scalability. Through efficient transfer learning, UrbanPulse takes a key step toward making high-resolution, AI-powered urban forecasting deployable in practice across diverse cities.
【7】ARBoids: Adaptive Residual Reinforcement Learning With Boids Model for Cooperative Multi-USV Target Defense
标题：ARBoids：采用Boids模型的自适应剩余强化学习用于协作多USV目标防御
链接：https://arxiv.org/abs/2502.18549
摘要：无人水面航行器的目标防御问题涉及在敌方无人水面航行器突破指定目标区域之前使用一个或多个无人水面航行器进行拦截。一个特别具有挑战性的情况下出现时，攻击者表现出优越的机动性相比，防御者，显着复杂的有效拦截。为了应对这一挑战，这封信介绍了ARBoids，这是一种新型的自适应残差强化学习框架，它将深度强化学习（DRL）与生物启发的基于力的Boids模型相集成。在此框架内，Boids模型作为多智能体协调的计算效率基线策略，而DRL学习剩余策略以自适应地改进和优化防御者的行动。所提出的方法在高保真Gazebo仿真环境中进行了验证，表现出优于传统拦截策略的性能，包括纯基于力的方法和香草DRL策略。此外，学习的政策表现出较强的适应性，攻击者具有不同的机动性配置文件，突出了其鲁棒性和泛化能力。ARBoids的代码将在接受本函后发布。
摘要：The target defense problem (TDP) for unmanned surface vehicles (USVs) concerns intercepting an adversarial USV before it breaches a designated target region, using one or more defending USVs. A particularly challenging scenario arises when the attacker exhibits superior maneuverability compared to the defenders, significantly complicating effective interception. To tackle this challenge, this letter introduces ARBoids, a novel adaptive residual reinforcement learning framework that integrates deep reinforcement learning (DRL) with the biologically inspired, force-based Boids model. Within this framework, the Boids model serves as a computationally efficient baseline policy for multi-agent coordination, while DRL learns a residual policy to adaptively refine and optimize the defenders' actions. The proposed approach is validated in a high-fidelity Gazebo simulation environment, demonstrating superior performance over traditional interception strategies, including pure force-based approaches and vanilla DRL policies. Furthermore, the learned policy exhibits strong adaptability to attackers with diverse maneuverability profiles, highlighting its robustness and generalization capability. The code of ARBoids will be released upon acceptance of this letter.
强化学习(5篇)
【1】Revisiting Bisimulation Metric for Robust Representations in Reinforcement Learning
标题：重新审视强化学习中鲁棒表示的互模拟度量
链接：https://arxiv.org/abs/2507.18519
摘要：互模拟度量一直被认为是各种强化学习任务中一种有效的控制相关表征学习技术。然而，在本文中，我们确定了传统互模拟度量的两个主要问题：1）无法表示某些独特的场景，以及2）依赖于递归更新期间奖励和后续状态差异的预定义权重。我们发现，第一个问题源于对奖励差距的不精确定义，而第二个问题源于忽视了不同训练阶段和任务设置中奖励差异和下一状态差异的不同重要性。为了解决这些问题，通过引入状态-动作对的度量，我们提出了一种修正的互模拟度量，该度量具有更精确的奖励间隙定义和具有自适应系数的新型更新算子。我们还提供了理论上的保证，我们提出的度量和改进的代表性的独特性的收敛。除了严格的理论分析外，我们还对DeepMind Control和Meta-World这两个具有代表性的基准测试进行了广泛的实验，证明了我们方法的有效性。
摘要：Bisimulation metric has long been regarded as an effective control-related representation learning technique in various reinforcement learning tasks. However, in this paper, we identify two main issues with the conventional bisimulation metric: 1) an inability to represent certain distinctive scenarios, and 2) a reliance on predefined weights for differences in rewards and subsequent states during recursive updates. We find that the first issue arises from an imprecise definition of the reward gap, whereas the second issue stems from overlooking the varying importance of reward difference and next-state distinctions across different training stages and task settings. To address these issues, by introducing a measure for state-action pairs, we propose a revised bisimulation metric that features a more precise definition of reward gap and novel update operators with adaptive coefficient. We also offer theoretical guarantees of convergence for our proposed metric and its improved representation distinctiveness. In addition to our rigorous theoretical analysis, we conduct extensive experiments on two representative benchmarks, DeepMind Control and Meta-World, demonstrating the effectiveness of our approach.
【2】Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN
标题
：O-RAN中基于百分位数的深度强化学习和基于奖励的个性化延迟感知RAN切片
链接：https://arxiv.org/abs/2507.18111
摘要：在本文中，我们解决了无线电接入网络（RAN）切片在一个开放的RAN（O-RAN）架构的挑战。我们的重点集中在一个网络，其中包括多个移动虚拟网络运营商（MVNO）竞争的物理资源块（PRB）的目标，满足概率延迟上限约束，同时最大限度地减少PRB的利用率。首先，我们根据大数定律（LLN）推导出一个奖励函数，然后进行实际修改，使其适应真实世界的实验场景。然后，我们提出了我们的解决方案，基于百分位数的延迟感知深度强化学习（PDA-DRL），通过实现平均延迟减少38%，证明了其优于几个基线，包括针对平均延迟约束优化的DRL模型。此外，我们深入研究了多个MVNO之间的模型权重共享问题，以开发一个强大的个性化模型。我们引入了一个基于奖励的个性化方法，每个代理优先考虑其他代理的模型权重的基础上，他们的表现。这种技术超越了传统的聚合方法，如联邦平均，以及依赖于流量模式和模型权重距离相似性的策略。
摘要：In this paper, we tackle the challenge of radio access network (RAN) slicing within an open RAN (O-RAN) architecture. Our focus centers on a network that includes multiple mobile virtual network operators (MVNOs) competing for physical resource blocks (PRBs) with the goal of meeting probabilistic delay upper bound constraints for their clients while minimizing PRB utilization. Initially, we derive a reward function based on the law of large numbers (LLN), then implement practical modifications to adapt it for real-world experimental scenarios. We then propose our solution, the Percentile-based Delay-Aware Deep Reinforcement Learning (PDA-DRL), which demonstrates its superiority over several baselines, including DRL models optimized for average delay constraints, by achieving a 38\% reduction in resultant average delay. Furthermore, we delve into the issue of model weight sharing among multiple MVNOs to develop a robust personalized model. We introduce a reward-based personalization method where each agent prioritizes other agents' model weights based on their performance. This technique surpasses traditional aggregation methods, such as federated averaging, and strategies reliant on traffic patterns and model weight distance similarities.
【3】Action-List Reinforcement Learning Syndrome Decoding for Binary Linear Block Codes
标题：二元线性分组码的列表强化学习综合症解码
链接：https://arxiv.org/abs/2507.17893
摘要：本文探讨了强化学习技术的应用，以提高性能的线性分组码的解码的基础上翻转位，并找到最佳的决定。我们描述了映射到马尔可夫决策过程（MDP）的迭代解码过程的方法，并提出了不同的方法来减少在MDP的状态数。提出了一种截断的MDP，通过学习码字周围具有指定半径的汉明球来减少MDP中的状态数。然后，我们提出了一个通用的方案，基于强化学习的解码器适用于任何类别的代码，以提高解码器的性能。我们称这个方案为动作列表解码。我们基于Deep-Q网络值设计了一个动作列表解码器，大大提高了性能。我们还利用代码的自同构群进一步提高代码的性能。此外，我们提出了一种基于反馈的方法，通过在现有解码器之后应用强化学习算法来利用和增强现有高性能解码器的性能。这些方法有效地降低了强化学习块的复杂性。最后，我们提出了低密度奇偶校验码（LDPC）在二进制对称信道（BSC）的实验结果来证明所提出的方法的效率。
摘要：This paper explores the application of reinforcement learning techniques to enhance the performance of decoding of linear block codes based on flipping bits and finding optimal decisions. We describe the methodology for mapping the iterative decoding process into Markov Decision Processes (MDPs) and propose different methods to reduce the number of states in the MDP. A truncated MDP is proposed to reduce the number of states in the MDP by learning a Hamming ball with a specified radius around codewords. We then propose a general scheme for reinforcement learning based decoders applicable to any class of codes to improve the performance of decoders. We call this scheme an action-list decoding. We design an action-list decoder based on the Deep-Q network values that substantially enhance performance. We also get benefit of automorphism group of code to further improve the code performance. Additionally, we propose a feedback-based method to exploit and enhance the performance of existing high-performing decoders by applying reinforcement learning algorithms after the existing decoders. These approaches effectively reduces the complexity of the reinforcement learning block. Finally, we present experimental results for the Low-Density Parity Check (LDPC) codes over the Binary Symmetric Channel (BSC) to demonstrate the efficiency of the proposed methods.
【4】Reinforcement Learning for Accelerated Aerodynamic Shape Optimisation
标题：加速气动形状优化的强化学习
链接：https://arxiv.org/abs/2507.17786
摘要：提出了一种基于强化学习（RL）的自适应优化算法，用于气动外形的降维优化。RL在这里应用的形式是基于代理的，行动者-批评者策略评估MCMC方法，允许暂时“冻结”一些待优化的参数。我们的目标是最大限度地减少计算工作量，并使用所观察到的优化结果来解释所发现的极值在实现所需的流场方面的作用。 通过将中间CFD模拟周围的一系列局部优化参数变化作为基础事实，如果（a）变化参数必须驻留的参数的局部邻域足够大以与网格大小的步长及其大量模拟竞争，以及（b）对于良好的逐步参数自适应所必需的这些邻域的回报和成本的估计是足够准确的。我们给出了一个简单的流体动力学问题的一个例子，该方法允许解释的意义上的特征重要性评分。
摘要：We introduce a reinforcement learning (RL) based adaptive optimization algorithm for aerodynamic shape optimization focused on dimensionality reduction. The form in which RL is applied here is that of a surrogate-based, actor-critic policy evaluation MCMC approach allowing for temporal 'freezing' of some of the parameters to be optimized. The goals are to minimize computational effort, and to use the observed optimization results for interpretation of the discovered extrema in terms of their role in achieving the desired flow-field. By a sequence of local optimized parameter changes around intermediate CFD simulations acting as ground truth, it is possible to speed up the global optimization if (a) the local neighbourhoods of the parameters in which the changed parameters must reside are sufficiently large to compete with the grid-sized steps and its large number of simulations, and (b) the estimates of the rewards and costs on these neighbourhoods necessary for a good step-wise parameter adaption are sufficiently accurate. We give an example of a simple fluid-dynamical problem on which the method allows interpretation in the sense of a feature importance scoring.
【5】Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
标题：用于高效量子电路合成的混合奖励驱动强化学习
链接：https://arxiv.org/abs/2507.16641
备注：13 pages, 4 figures, color figures
摘要：介绍了一种强化学习（RL）框架，用于从固定的初始状态生成指定目标量子态的量子电路的有效合成，解决了NISQ时代和未来容错量子计算的核心挑战。该方法利用基于动作序列的表格Q学习，在离散化的量子状态空间内，有效地管理空间维度的指数增长。该框架引入了一种混合奖励机制，结合了静态的，域信息的奖励，引导代理向目标状态与可定制的动态惩罚，阻止低效的电路结构，如门拥塞和冗余状态重访。通过利用稀疏矩阵表示和状态空间离散化，该方法能够在最小化计算开销的同时实现高维环境的可扩展导航。在多达7个量子位的图形状态准备任务上进行基准测试，我们证明了该算法始终发现具有优化门数的最小深度电路。此外，将框架扩展到任意量子态的通用门集，它仍然产生最小深度电路，突出了算法的鲁棒性和适应性。结果表明，这种RL驱动的方法有效地探索了复杂的量子态空间，并合成了接近最优的量子电路，为量子电路优化提供了资源有效的基础。
摘要 ：A reinforcement learning (RL) framework is introduced for the efficient synthesis of quantum circuits that generate specified target quantum states from a fixed initial state, addressing a central challenge in both the NISQ era and future fault-tolerant quantum computing. The approach utilizes tabular Q-learning, based on action sequences, within a discretized quantum state space, to effectively manage the exponential growth of the space dimension. The framework introduces a hybrid reward mechanism, combining a static, domain-informed reward that guides the agent toward the target state with customizable dynamic penalties that discourage inefficient circuit structures such as gate congestion and redundant state revisits. By leveraging sparse matrix representations and state-space discretization, the method enables scalable navigation of high-dimensional environments while minimizing computational overhead. Benchmarking on graph-state preparation tasks for up to seven qubits, we demonstrate that the algorithm consistently discovers minimal-depth circuits with optimized gate counts. Moreover, extending the framework to a universal gate set for arbitrary quantum states, it still produces minimal depth circuits, highlighting the algorithm's robustness and adaptability. The results confirm that this RL-driven approach efficiently explores the complex quantum state space and synthesizes near-optimal quantum circuits, providing a resource-efficient foundation for quantum circuit optimization.
分层学习(1篇)
【1】Hierarchical Dimensionless Learning (Hi-π): A physics-data hybrid-driven approach for discovering dimensionless parameter combinations
标题：分层无维度学习（Hi-Pi）：一种物理数据混合驱动的方法，用于发现无维度参数组合
链接：https://arxiv.org/abs/2507.18332
摘要：量纲分析为简化物理复杂性和揭示内在规律提供了一个通用的框架。然而，它的应用程序的高维系统仍然产生冗余的无量纲参数，使其具有挑战性的建立物理意义的描述。在这里，我们介绍分层无因次学习（Hi-{\pi}），这是一种物理-数据混合驱动的方法，它结合了维度分析和符号回归来自动发现关键的无因次参数组合。我们将这种方法应用于流体力学各个研究领域的经典例子。对于Rayleigh-B 'enard对流，该方法准确地提取了两个内在的无量纲参数：Rayleigh数和Prandtl数，验证了其跨多尺度数据的统一表示优势。对于圆管中的粘性流动，该方法自动发现两个最佳的无量纲参数：雷诺数和相对粗糙度，实现精确性和复杂性之间的平衡。对于亚音速流的压缩性修正，该方法有效地提取了经典的压缩性修正公式，同时证明了其通过最佳参数变换发现层次结构表达式的能力。
摘要：Dimensional analysis provides a universal framework for reducing physical complexity and reveal inherent laws. However, its application to high-dimensional systems still generates redundant dimensionless parameters, making it challenging to establish physically meaningful descriptions. Here, we introduce Hierarchical Dimensionless Learning (Hi-{\pi}), a physics-data hybrid-driven method that combines dimensional analysis and symbolic regression to automatically discover key dimensionless parameter combination(s). We applied this method to classic examples in various research fields of fluid mechanics. For the Rayleigh-B\'enard convection, this method accurately extracted two intrinsic dimensionless parameters: the Rayleigh number and the Prandtl number, validating its unified representation advantage across multiscale data. For the viscous flows in a circular pipe, the method automatically discovers two optimal dimensionless parameters: the Reynolds number and relative roughness, achieving a balance between accuracy and complexity. For the compressibility correction in subsonic flow, the method effectively extracts the classic compressibility correction formulation, while demonstrating its capability to discover hierarchical structural expressions through optimal parameter transformations.
医学相关(2篇)
【1】Agentic AI framework for End-to-End Medical Data Inference
标题：端到端医疗数据推理的抽象人工智能框架
链接：https://arxiv.org/abs/2507.18115
备注：10 pages, 5 figures, 2 tables, BIBM conference
摘要：在医疗保健领域构建和部署机器学习解决方案仍然是昂贵且劳动密集型的，因为预处理工作流分散，模型兼容性问题以及严格的数据隐私限制。在这项工作中，我们引入了一个人工智能框架，通过一个模块化的、特定于任务的代理系统，自动化整个临床数据管道，从摄取到推理。这些代理处理结构化和非结构化数据，实现自动特征选择，模型选择和预处理建议，而无需人工干预。我们评估系统的公开数据集，从老年病，姑息治疗，结肠镜成像。例如，对于结构化数据，（焦虑数据）和非结构化数据（结肠镜检查息肉数据），管道从摄取标识符代理的文件类型检测开始，然后是确保隐私合规性的数据分析器代理，我们首先识别数据类型，然后对其进行匿名化。特征提取代理使用基于嵌入的方法识别表格数据的特征，提取所有列名，以及用于图像数据的基于MedGemma的多阶段方法，其推断模态和疾病名称。这些特性指导模型-数据特性匹配器代理从管理的存储库中选择最适合的模型。预处理推荐代理和预处理实现代理然后基于数据类型和模型需求应用定制的预处理。最后，“模型推理代理”在上传的数据上运行选定的模型，并使用SHAP、LIME和DETR注意力图等工具生成可解释的输出。通过自动化机器学习生命周期的这些高摩擦阶段，拟议的框架减少了对重复专家干预的需求，为在临床环境中实施人工智能提供了一种可扩展的，具有成本效益的途径。
摘要：Building and deploying machine learning solutions in healthcare remains expensive and labor-intensive due to fragmented preprocessing workflows, model compatibility issues, and stringent data privacy constraints. In this work, we introduce an Agentic AI framework that automates the entire clinical data pipeline, from ingestion to inference, through a system of modular, task-specific agents. These agents handle both structured and unstructured data, enabling automatic feature selection, model selection, and preprocessing recommendation without manual intervention. We evaluate the system on publicly available datasets from geriatrics, palliative care, and colonoscopy imaging. For example, in the case of structured data (anxiety data) and unstructured data (colonoscopy polyps data), the pipeline begins with file-type detection by the Ingestion Identifier Agent, followed by the Data Anonymizer Agent ensuring privacy compliance, where we first identify the data type and then anonymize it. The Feature Extraction Agent identifies features using an embedding-based approach for tabular data, extracting all column names, and a multi-stage MedGemma-based approach for image data, which infers modality and disease name. These features guide the Model-Data Feature Matcher Agent in selecting the best-fit model from a curated repository. The Preprocessing Recommender Agent and Preprocessing Implementor Agent then apply tailored preprocessing based on data type and model requirements. Finally, the ``Model Inference Agent" runs the selected model on the uploaded data and generates interpretable outputs using tools like SHAP, LIME, and DETR attention maps. By automating these high-friction stages of the ML lifecycle, the proposed framework reduces the need for repeated expert intervention, offering a scalable, cost-efficient pathway for operationalizing AI in clinical environments.
【2】BrisT1D Dataset: Young Adults with Type 1 Diabetes in the UK using Smartwatches
标题：SYS T1 D数据集：英国使用智能手表的1型糖尿病年轻人
链接：https://arxiv.org/abs/2507.17757
备注：13 pages, 14 figures
摘要：背景资料：1型糖尿病（T1D）在管理技术方面取得了快速发展，并为其他慢性疾病的未来管理提供了有用的案例研究。这种管理技术的进一步发展需要探索其现实世界的使用和额外的数据流的潜力。为了促进这一点，我们将T1D Dataset贡献给越来越多的公共T1D管理数据集。该数据集是根据对英国24名年轻人的纵向研究开发的，这些年轻人使用智能手表以及他们通常的T1D管理。调查结果：T1D数据集包括来自T1D管理系统和参与者使用的智能手表的设备数据，以及研究期间进行的每月访谈和焦点小组的记录。器械数据以处理状态提供，用于可用性和更快速的分析，并以原始状态提供，用于深入探索研究中捕获的新见解。结论：该数据集具有一系列潜在应用。定量元素可以支持血糖预测、低血糖预测和闭环算法开发。定性元素可以探索用户体验和意见，以及更广泛的混合方法研究智能手表在T1D管理中的作用。
摘要 ：Background: Type 1 diabetes (T1D) has seen a rapid evolution in management technology and forms a useful case study for the future management of other chronic conditions. Further development of this management technology requires an exploration of its real-world use and the potential of additional data streams. To facilitate this, we contribute the BrisT1D Dataset to the growing number of public T1D management datasets. The dataset was developed from a longitudinal study of 24 young adults in the UK who used a smartwatch alongside their usual T1D management. Findings: The BrisT1D dataset features both device data from the T1D management systems and smartwatches used by participants, as well as transcripts of monthly interviews and focus groups conducted during the study. The device data is provided in a processed state, for usability and more rapid analysis, and in a raw state, for in-depth exploration of novel insights captured in the study. Conclusions: This dataset has a range of potential applications. The quantitative elements can support blood glucose prediction, hypoglycaemia prediction, and closed-loop algorithm development. The qualitative elements enable the exploration of user experiences and opinions, as well as broader mixed-methods research into the role of smartwatches in T1D management.
蒸馏|知识提取(2篇)
【1】C2G-KD: PCA-Constrained Generator for Data-Free Knowledge Distillation
标题：C2 G-KD：PCA约束生成器，用于无数据知识提取
链接：https://arxiv.org/abs/2507.18533
备注：12 pages
摘要：我们介绍了C2 G-KD，一个无数据的知识蒸馏框架，其中训练类条件生成器以生成由冻结教师模型和来自PCA的几何约束指导的合成样本。生成器从不观察真实的训练数据，而是通过语义和结构损失的组合来学习激活教师的输出。通过将生成的样本限制在根据每个类最少两个真实示例估计的特定于类的PCA子空间内，我们保持了拓扑一致性和多样性。MNIST上的实验表明，即使是最小的类结构也足以引导有用的合成训练管道。
摘要：We introduce C2G-KD, a data-free knowledge distillation framework where a class-conditional generator is trained to produce synthetic samples guided by a frozen teacher model and geometric constraints derived from PCA. The generator never observes real training data but instead learns to activate the teacher's output through a combination of semantic and structural losses. By constraining generated samples to lie within class-specific PCA subspaces estimated from as few as two real examples per class, we preserve topological consistency and diversity. Experiments on MNIST show that even minimal class structure is sufficient to bootstrap useful synthetic training pipelines.
【2】Tiny is not small enough: High-quality, low-resource facial animation models through hybrid knowledge distillation
标题：微型还不够小：通过混合知识提炼高质量、低资源的面部动画模型
链接：https://arxiv.org/abs/2507.18352
备注：Accepted to ACM Transactions on Graphics 2025 (SIGGRAPH journal track)
摘要：为语音驱动的3D面部动画训练高质量、鲁棒的机器学习模型需要大量不同的高质量音频动画对数据集。为了克服这种数据集的缺乏，最近的工作引入了大型的预训练语音编码器，这些编码器对输入音频的变化具有鲁棒性，因此，使面部动画模型能够跨扬声器，音频质量和语言进行概括。然而，由此产生的面部动画模型是非常大的，并且只适合在专用机器上进行离线推理。在这项工作中，我们将探讨在游戏开发的背景下，设备上的实时面部动画模型。我们克服了缺乏大型数据集，使用混合知识蒸馏与伪标签。给定一个大的音频数据集，我们采用高性能的教师模型来训练非常小的学生模型。与预先训练的语音编码器相比，我们的学生模型仅由卷积层和全连接层组成，不需要注意上下文或定期更新。在我们的实验中，我们证明，我们可以减少内存占用高达3.4 MB，并要求未来的音频上下文高达81毫秒，同时保持高品质的动画。这为设备上的推理铺平了道路，这是迈向逼真的模型驱动数字角色的重要一步。
摘要：The training of high-quality, robust machine learning models for speech-driven 3D facial animation requires a large, diverse dataset of high-quality audio-animation pairs. To overcome the lack of such a dataset, recent work has introduced large pre-trained speech encoders that are robust to variations in the input audio and, therefore, enable the facial animation model to generalize across speakers, audio quality, and languages. However, the resulting facial animation models are prohibitively large and lend themselves only to offline inference on a dedicated machine. In this work, we explore on-device, real-time facial animation models in the context of game development. We overcome the lack of large datasets by using hybrid knowledge distillation with pseudo-labeling. Given a large audio dataset, we employ a high-performing teacher model to train very small student models. In contrast to the pre-trained speech encoders, our student models only consist of convolutional and fully-connected layers, removing the need for attention context or recurrent updates. In our experiments, we demonstrate that we can reduce the memory footprint to up to 3.4 MB and required future audio context to up to 81 ms while maintaining high-quality animations. This paves the way for on-device inference, an important step towards realistic, model-driven digital characters.
自动驾驶|车辆|车道检测等(1篇)
【1】Machine Unlearning of Traffic State Estimation and Prediction
标题：交通状态估计与预测的机器去学习
链接：https://arxiv.org/abs/2507.17984
摘要：数据驱动的交通状态估计和预测（TSEP）严重依赖于包含敏感信息的数据源。虽然大量的数据推动了重大突破，特别是在基于机器学习的方法中，但它也引发了对隐私，网络安全和数据新鲜度的担忧。这些问题可能会削弱公众对智能交通系统的信任。最近，法规引入了“被遗忘权”，允许用户要求从模型中删除他们的私人数据。由于机器学习模型可以记住旧数据，因此在此类系统中，简单地将其从后端数据库中删除是不够的。为了解决这些挑战，本研究为TSEP引入了一种新的学习范式-机器非学习TSEP-它使经过训练的TSEP模型能够选择性地忘记隐私敏感，中毒或过时的数据。通过赋予模型“忘却”的能力，我们的目标是增强数据驱动的交通TSEP的可信度和可靠性。
摘要：Data-driven traffic state estimation and prediction (TSEP) relies heavily on data sources that contain sensitive information. While the abundance of data has fueled significant breakthroughs, particularly in machine learning-based methods, it also raises concerns regarding privacy, cybersecurity, and data freshness. These issues can erode public trust in intelligent transportation systems. Recently, regulations have introduced the "right to be forgotten", allowing users to request the removal of their private data from models. As machine learning models can remember old data, simply removing it from back-end databases is insufficient in such systems. To address these challenges, this study introduces a novel learning paradigm for TSEP-Machine Unlearning TSEP-which enables a trained TSEP model to selectively forget privacy-sensitive, poisoned, or outdated data. By empowering models to "unlearn," we aim to enhance the trustworthiness and reliability of data-driven traffic TSEP.
联邦学习|隐私保护|加密(2篇)
【1】Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challenges
标题：大规模云机器人操纵的联邦学习：机遇与挑战
链接：https://arxiv.org/abs/2507.17903
备注：Accepted for Presentation at IEEE International Conference on Machine Learning and Cybernetics (ICMLC) 2025
摘要 ：联邦学习（FL）是一种新兴的分布式机器学习范式，其中模型的协作训练涉及设备的动态参与以实现广泛的目标。相比之下，经典的机器学习（ML）通常需要数据位于本地进行训练，而FL利用众多用户设备来训练共享的全局模型，而无需共享私有数据。由于有限的低延迟计算资源，当前的机器人操作任务受到机器人的个体能力和速度的限制。因此，云机器人的概念已经出现，允许机器人应用程序利用计算资源的灵活性和可靠性，有效地减轻了他们在云边缘连续体中的计算需求。毫无疑问，在这种分布式计算环境中，如云机器人操作场景所示，FL提供了多种优势，同时也带来了一些挑战和机遇。在本文中，我们提出了FL的基本概念及其连接到云机器人操作。此外，我们设想了通过FL实现大规模高效可靠的云机器人操作的机遇和挑战，研究人员采用集中或分散设置来设计和验证FL模型。
摘要：Federated Learning (FL) is an emerging distributed machine learning paradigm, where the collaborative training of a model involves dynamic participation of devices to achieve broad objectives. In contrast, classical machine learning (ML) typically requires data to be located on-premises for training, whereas FL leverages numerous user devices to train a shared global model without the need to share private data. Current robotic manipulation tasks are constrained by the individual capabilities and speed of robots due to limited low-latency computing resources. Consequently, the concept of cloud robotics has emerged, allowing robotic applications to harness the flexibility and reliability of computing resources, effectively alleviating their computational demands across the cloud-edge continuum. Undoubtedly, within this distributed computing context, as exemplified in cloud robotic manipulation scenarios, FL offers manifold advantages while also presenting several challenges and opportunities. In this paper, we present fundamental concepts of FL and their connection to cloud robotic manipulation. Additionally, we envision the opportunities and challenges associated with realizing efficient and reliable cloud robotic manipulation at scale through FL, where researchers adopt to design and verify FL models in either centralized or decentralized settings.
【2】Caching Techniques for Reducing the Communication Cost of Federated Learning in IoT Environments
标题：降低物联网环境中联邦学习通信成本的缓存技术
链接：https://arxiv.org/abs/2507.17772
备注：Journal
摘要：联邦学习（FL）允许多个分布式设备在不集中数据的情况下联合训练共享模型，但通信成本仍然是主要瓶颈，特别是在资源受限的环境中。本文介绍了缓存策略- FIFO，LRU和基于优先级-以减少不必要的模型更新传输。通过选择性地转发重要的更新，我们的方法降低了带宽使用，同时保持模型的准确性。在CIFAR-10和医疗数据集上的实验表明，减少了通信，精度损失最小。结果证实，智能缓存提高了可扩展性和内存效率，并在边缘物联网网络中支持可靠的FL，使其适用于智能城市，医疗保健和其他延迟敏感型应用的部署。
摘要：Federated Learning (FL) allows multiple distributed devices to jointly train a shared model without centralizing data, but communication cost remains a major bottleneck, especially in resource-constrained environments. This paper introduces caching strategies - FIFO, LRU, and Priority-Based - to reduce unnecessary model update transmissions. By selectively forwarding significant updates, our approach lowers bandwidth usage while maintaining model accuracy. Experiments on CIFAR-10 and medical datasets show reduced communication with minimal accuracy loss. Results confirm that intelligent caching improves scalability, memory efficiency, and supports reliable FL in edge IoT networks, making it practical for deployment in smart cities, healthcare, and other latency-sensitive applications.
推理|分析|理解|解释(6篇)
【1】Maximizing Prefix-Confidence at Test-Time Efficiently Improves Mathematical Reasoning
标题：在测试时最大化词头置信度有效改进数学推理
链接：https://arxiv.org/abs/2507.18122
摘要：最近的研究表明，语言模型可以通过最大化自己对预测的信心来自我改进，而不依赖于外部验证者或奖励信号。在这项工作中，我们研究了数学推理任务的语言模型的测试时间缩放，其中模型自身的信心用于选择最有希望的尝试。令人惊讶的是，我们发现，我们可以实现显着的性能增益，继续只有最有前途的尝试，选择模型的前缀信心。我们在五个数学推理数据集上系统地评估了前缀置信度缩放：学校级GSM8K和MATH500，以及竞赛级AMC 23，AIME24和AIME25。我们发现，前缀置信度缩放只有32个令牌的前缀实现了更好的准确性计算权衡比多数表决。此外，前缀置信度缩放似乎比BoN更不容易受到长度偏差的影响。最后，我们还使用前缀置信度评估了测试时的训练，发现虽然优于基本模型，但它并没有比前缀置信度缩放更好。
摘要：Recent work has shown that language models can self-improve by maximizing their own confidence in their predictions, without relying on external verifiers or reward signals. In this work, we study the test-time scaling of language models for mathematical reasoning tasks, where the model's own confidence is used to select the most promising attempts. Surprisingly, we find that we can achieve significant performance gains by continuing only the most promising attempt, selected by the model's prefix-confidence. We systematically evaluate prefix-confidence scaling on five mathematical reasoning datasets: the school-level GSM8K and MATH500, and the competition-level AMC23, AIME24, and AIME25. We find that prefix-confidence scaling with prefixes of only 32 tokens achieves a better accuracy-compute trade-off than majority voting. Moreover, prefix-confidence scaling appears less susceptible than BoN to length biases. Finally, we also evaluate test-time training with prefix-confidence and find that, while outperforming the base model, it does not improve over prefix-confidence scaling.
【2】Predictive Scaling Laws for Efficient GRPO Training of Large Reasoning Models
标题：用于大型推理模型高效GRPO训练的预测缩放定律
链接：https://arxiv.org/abs/2507.18014
摘要：使用强化学习方法（如组相对策略优化（GRPO））微调大型语言模型（LLM）用于推理任务在计算上是昂贵的。为了解决这个问题，我们提出了一个预测框架，该框架对训练动态进行建模，并有助于优化资源使用。通过对Llama和Qwen模型（3B 8B）的实验，我们推导出了基于模型大小、初始性能和训练进度的经验缩放律。该定律预测了奖励轨迹，并确定了三个一致的训练阶段：缓慢开始，快速提高和高原。我们发现，训练超过一定数量的epoch几乎没有增益，这表明提前停止可以显着减少计算而不牺牲性能。我们的方法概括了不同的模型类型，为有效的基于GRPO的微调提供了一个实用的指南。
摘要：Fine-tuning large language models (LLMs) for reasoning tasks using reinforcement learning methods like Group Relative Policy Optimization (GRPO) is computationally expensive. To address this, we propose a predictive framework that models training dynamics and helps optimize resource usage. Through experiments on Llama and Qwen models (3B 8B), we derive an empirical scaling law based on model size, initial performance, and training progress. This law predicts reward trajectories and identifies three consistent training phases: slow start, rapid improvement, and plateau. We find that training beyond certain number of an epoch offers little gain, suggesting earlier stopping can significantly reduce compute without sacrificing performance. Our approach generalizes across model types, providing a practical guide for efficient GRPO-based fine-tuning.
【3】Multimodal Fine-grained Reasoning for Post Quality Evaluation
标题：用于岗位质量评估的多模式细粒度推理
链接：https://arxiv.org/abs/2507.17934
备注 ：48 pages
摘要：准确评估帖子质量需要复杂的关系推理来捕捉细微的主题-帖子关系。然而，现有的研究面临着三个主要的局限性：（1）将任务视为单峰分类，未能利用多模态线索和细粒度的质量区分;（2）在深度多模态融合过程中引入噪音，导致误导信号;（3）缺乏捕捉复杂语义关系的能力，如相关性和全面性。为了解决这些问题，我们提出了多模态细粒度主题后关系推理（MFTRR）框架，它模仿人类的认知过程。MFTRR将事后质量评估重新定义为一项排名任务，并将多模态数据纳入其中，以更好地捕捉质量变化。它由两个关键模块组成：（1）局部-全局语义相关推理模块，它在局部和全局级别上对帖子和主题之间的细粒度语义交互进行建模，并通过最大信息融合机制进行增强以抑制噪声;以及（2）多级证据关系推理模块，它探索宏观和微观级别的关系线索以加强基于证据的推理。我们在三个新构建的多模态主题帖子数据集和公共Lazada-Home数据集上评估MFTRR。实验结果表明，MFTRR显着优于最先进的基线，实现了高达9.52%的NDCG@3的改善，比最好的单峰方法在艺术史数据集。
摘要：Accurately assessing post quality requires complex relational reasoning to capture nuanced topic-post relationships. However, existing studies face three major limitations: (1) treating the task as unimodal categorization, which fails to leverage multimodal cues and fine-grained quality distinctions; (2) introducing noise during deep multimodal fusion, leading to misleading signals; and (3) lacking the ability to capture complex semantic relationships like relevance and comprehensiveness. To address these issues, we propose the Multimodal Fine-grained Topic-post Relational Reasoning (MFTRR) framework, which mimics human cognitive processes. MFTRR reframes post-quality assessment as a ranking task and incorporates multimodal data to better capture quality variations. It consists of two key modules: (1) the Local-Global Semantic Correlation Reasoning Module, which models fine-grained semantic interactions between posts and topics at both local and global levels, enhanced by a maximum information fusion mechanism to suppress noise; and (2) the Multi-Level Evidential Relational Reasoning Module, which explores macro- and micro-level relational cues to strengthen evidence-based reasoning. We evaluate MFTRR on three newly constructed multimodal topic-post datasets and the public Lazada-Home dataset. Experimental results demonstrate that MFTRR significantly outperforms state-of-the-art baselines, achieving up to 9.52% NDCG@3 improvement over the best unimodal method on the Art History dataset.
【4】Self-similarity Analysis in Deep Neural Networks
标题：深度神经网络中的自相似性分析
链接：https://arxiv.org/abs/2507.17785
摘要：目前的研究发现，一些深度神经网络在特征表示或参数分布方面表现出很强的层次自相似性。然而，除了对不同训练阶段权重的幂律分布如何影响模型性能的初步研究外，还没有定量分析隐藏空间几何的自相似性如何影响模型权重优化，也没有对内部神经元的动态行为有清楚的了解。为此，本文提出了一种基于隐层神经元输出特征的复杂网络建模方法，研究了不同隐层构造的特征网络的自相似性，并分析了调整特征网络的自相似程度如何增强深度神经网络的分类性能。通过对三种类型的网络（MLP架构、卷积网络和注意力架构）进行验证，这项研究表明，特征网络表现出的自相似程度在不同的模型架构中有所不同。此外，在训练过程中对特征网络的自相似性嵌入约束可以将自相似深度神经网络（MLP架构和注意力架构）的性能提高多达6个百分点。
摘要：Current research has found that some deep neural networks exhibit strong hierarchical self-similarity in feature representation or parameter distribution. However, aside from preliminary studies on how the power-law distribution of weights across different training stages affects model performance,there has been no quantitative analysis on how the self-similarity of hidden space geometry influences model weight optimization, nor is there a clear understanding of the dynamic behavior of internal neurons. Therefore, this paper proposes a complex network modeling method based on the output features of hidden-layer neurons to investigate the self-similarity of feature networks constructed at different hidden layers, and analyzes how adjusting the degree of self-similarity in feature networks can enhance the classification performance of deep neural networks. Validated on three types of networks MLP architectures, convolutional networks, and attention architectures this study reveals that the degree of self-similarity exhibited by feature networks varies across different model architectures. Furthermore, embedding constraints on the self-similarity of feature networks during the training process can improve the performance of self-similar deep neural networks (MLP architectures and attention architectures) by up to 6 percentage points.
【5】Machine Learning Workflow for Analysis of High-Dimensional Order Parameter Space: A Case Study of Polymer Crystallization from Molecular Dynamics Simulations
标题：用于分析多维序参数空间的机器学习工作流程：分子动力学模拟中聚合物结晶的案例研究
链接：https://arxiv.org/abs/2507.17980
备注：30 pages, 8 figures, 1 table
摘要：目前，聚合物中的结晶途径的识别正在使用基于分子模拟的数据在单个序参数（OP）上的预设截止点上进行，以定义成核或结晶区域。除了对临界值的敏感性之外，这些OP中的每一个都引入了自己的系统性偏倚。在这项研究中，提出了一个集成的机器学习工作流程，使用原子分子动力学数据准确地量化聚合物体系中的结晶度。每个原子由一个高维特征向量表示，该特征向量结合了几何、类几何和基于几何的描述符。低维嵌入被用来揭示原子环境中潜在的结构指纹。随后，嵌入的无监督聚类以高保真度识别晶体和非晶体原子。在用多维数据生成高质量的标签后，我们使用监督学习技术来识别可以完全捕获该标签的最小序参数集。进行了各种测试以减少特征集，证明仅使用三个顺序参数就足以重新创建结晶标记。基于这些观察到的OP，结晶度指数（C指数）定义为逻辑回归模型的结晶度概率，在整个过程中保持双峰并实现超过0.98的分类性能（AUC）。值得注意的是，在一个或几个快照上训练的模型能够有效地实时计算结晶度。最后，我们展示了最佳的C指数拟合在结晶的各个阶段是如何演变的，支持了熵在早期成核中占主导地位的假设，而对称性则在以后获得相关性。该工作流程为OP选择提供了数据驱动的策略，并为监测大规模聚合物模拟中的结构转变提供了指标。
摘要：Currently, identification of crystallization pathways in polymers is being carried out using molecular simulation-based data on a preset cut-off point on a single order parameter (OP) to define nucleated or crystallized regions. Aside from sensitivity to cut-off, each of these OPs introduces its own systematic biases. In this study, an integrated machine learning workflow is presented to accurately quantify crystallinity in polymeric systems using atomistic molecular dynamics data. Each atom is represented by a high-dimensional feature vector that combines geometric, thermodynamic-like, and symmetry-based descriptors. Low dimensional embeddings are employed to expose latent structural fingerprints within atomic environments. Subsequently, unsupervised clustering on the embeddings identified crystalline and amorphous atoms with high fidelity. After generating high quality labels with multidimensional data, we use supervised learning techniques to identify a minimal set of order parameters that can fully capture this label. Various tests were conducted to reduce the feature set, demonstrating that using only three order parameters is sufficient to recreate the crystallization labels. Based on these observed OPs, the crystallinity index (C-index) is defined as the logistic regression model's probability of crystallinity, remaining bimodal throughout the process and achieving over 0.98 classification performance (AUC). Notably, a model trained on one or a few snapshots enables efficient on-the-fly computation of crystallinity. Lastly, we demonstrate how the optimal C-index fit evolves during various stages of crystallization, supporting the hypothesis that entropy dominates early nucleation, while symmetry gains relevance later. This workflow provides a data-driven strategy for OP selection and a metric to monitor structural transformations in large-scale polymer simulations.
【6】Sliding Window Informative Canonical Correlation Analysis
标题：滑动窗口信息典型相关分析
链接：https://arxiv.org/abs/2507.17921
备注：22 pages, submitted
摘要 ：典型相关分析（CCA）是一种用于发现两个数据集之间相关特征集的技术。在本文中，我们提出了一种新的扩展CCA的在线，流数据设置：滑动窗口信息典型相关分析（SWICCA）。我们的方法使用流主成分分析（PCA）算法作为后端，并使用这些输出结合一个小的滑动窗口的样本来估计CCA组件的实时。我们激励和描述我们的算法，提供数值模拟来表征其性能，并提供理论性能保证。SWICCA方法是适用的，可扩展到极高的维度，我们提供了一个真实的数据的例子，证明了这一能力。
摘要：Canonical correlation analysis (CCA) is a technique for finding correlated sets of features between two datasets. In this paper, we propose a novel extension of CCA to the online, streaming data setting: Sliding Window Informative Canonical Correlation Analysis (SWICCA). Our method uses a streaming principal component analysis (PCA) algorithm as a backend and uses these outputs combined with a small sliding window of samples to estimate the CCA components in real time. We motivate and describe our algorithm, provide numerical simulations to characterize its performance, and provide a theoretical performance guarantee. The SWICCA method is applicable and scalable to extremely high dimensions, and we provide a real-data example that demonstrates this capability.
检测相关(2篇)
【1】Regression-aware Continual Learning for Android Malware Detection
标题：基于回归感知的持续学习的Android恶意软件检测
链接：https://arxiv.org/abs/2507.18313
备注：Submitted to IEEE Transactions on Information Forensics and Security
摘要：恶意软件发展迅速，迫使基于机器学习（ML）的检测器不断适应。随着防病毒供应商每天处理数十万个新样本，数据集可能会增长到数十亿个样本，这使得完全重新训练变得不切实际。持续学习（CL）已经成为一种可扩展的替代方案，可以在不完全访问数据的情况下实现增量更新，同时减轻灾难性遗忘。在这项工作中，我们分析了一个关键但被忽视的问题，在这种情况下：安全回归。与遗忘不同，遗忘表现为以前看到的数据的一般性能下降，安全回归在样本级别捕获有害的预测变化，例如曾经被正确检测到但在模型更新后逃避检测的恶意软件样本。虽然经常被忽视，但回归在安全关键型应用程序中构成严重风险，因为在系统中悄悄重新引入以前检测到的威胁可能会破坏用户对整个更新过程的信任。为了解决这个问题，我们在基于CL的恶意软件检测器中对安全回归进行了形式化和量化，并提出了一种回归感知惩罚来减轻它。具体来说，我们将正一致训练（PCT）适应CL设置，以模型不可知的方式保留先前的预测行为。在ELSA、Tesseract和AZ-Class数据集上的实验表明，我们的方法有效地减少了不同CL场景下的回归，同时随着时间的推移保持了强大的检测性能。
摘要：Malware evolves rapidly, forcing machine learning (ML)-based detectors to adapt continuously. With antivirus vendors processing hundreds of thousands of new samples daily, datasets can grow to billions of examples, making full retraining impractical. Continual learning (CL) has emerged as a scalable alternative, enabling incremental updates without full data access while mitigating catastrophic forgetting. In this work, we analyze a critical yet overlooked issue in this context: security regression. Unlike forgetting, which manifests as a general performance drop on previously seen data, security regression captures harmful prediction changes at the sample level, such as a malware sample that was once correctly detected but evades detection after a model update. Although often overlooked, regressions pose serious risks in security-critical applications, as the silent reintroduction of previously detected threats in the system may undermine users' trust in the whole updating process. To address this issue, we formalize and quantify security regression in CL-based malware detectors and propose a regression-aware penalty to mitigate it. Specifically, we adapt Positive Congruent Training (PCT) to the CL setting, preserving prior predictive behavior in a model-agnostic manner. Experiments on the ELSA, Tesseract, and AZ-Class datasets show that our method effectively reduces regression across different CL scenarios while maintaining strong detection performance over time.
【2】A Concept-based approach to Voice Disorder Detection
标题：基于概念的语音障碍检测方法
链接：https://arxiv.org/abs/2507.17799
摘要：语音障碍影响了很大一部分人口，使用自动化，非侵入性技术诊断语音障碍的能力将代表医疗保健的重大进步，提高患者的生活质量。最近的研究表明，人工智能模型，特别是深度神经网络（DNN），可以有效地解决这一任务。然而，由于它们的复杂性，这些模型的决策过程通常保持不透明，限制了它们在临床环境中的可信度。本文研究了一种基于可解释AI（XAI）的替代方法，该领域旨在通过提供不同形式的解释来提高DNN的可解释性。具体来说，这项工作侧重于基于概念的模型，如概念瓶颈模型（CBM）和概念嵌入模型（CEM），以及它们如何实现与传统深度学习方法相当的性能，同时提供更透明和可解释的决策框架。
摘要：Voice disorders affect a significant portion of the population, and the ability to diagnose them using automated, non-invasive techniques would represent a substantial advancement in healthcare, improving the quality of life of patients. Recent studies have demonstrated that artificial intelligence models, particularly Deep Neural Networks (DNNs), can effectively address this task. However, due to their complexity, the decision-making process of such models often remain opaque, limiting their trustworthiness in clinical contexts. This paper investigates an alternative approach based on Explainable AI (XAI), a field that aims to improve the interpretability of DNNs by providing different forms of explanations. Specifically, this works focuses on concept-based models such as Concept Bottleneck Model (CBM) and Concept Embedding Model (CEM) and how they can achieve performance comparable to traditional deep learning methods, while offering a more transparent and interpretable decision framework.
分类|识别(6篇)
【1】Gait Recognition Based on Tiny ML and IMU Sensors
标题：基于微型ML和IMU传感器的步态识别
链接：https://arxiv.org/abs/2507.18627
摘要：该项目介绍了使用微型机器学习（Tiny ML）和惯性测量单元（IMU）传感器开发步态识别系统。该系统利用XIAO-nRF 52840 Sense微控制器和LSM 6DS 3 IMU传感器从四种不同的活动中捕获运动数据，包括加速度和角速度：步行、静止、上楼和下楼。采集的数据通过Edge Impulse（一个边缘AI平台）进行处理，该平台能够训练机器学习模型，这些模型可以直接部署到微控制器上进行实时活动分类。数据预处理步骤包括使用滑动窗口和数据归一化等技术从原始传感器数据中提取相关特征，然后训练深度神经网络（DNN）分类器进行活动识别。该模型在测试数据集上达到了80%以上的准确率，证明了其有效分类四种活动的能力。此外，该平台还支持异常检测，进一步增强了系统的鲁棒性。Tiny ML的集成确保了低功耗运行，使其适用于电池供电或能量收集设备。
摘要：This project presents the development of a gait recognition system using Tiny Machine Learning (Tiny ML) and Inertial Measurement Unit (IMU) sensors. The system leverages the XIAO-nRF52840 Sense microcontroller and the LSM6DS3 IMU sensor to capture motion data, including acceleration and angular velocity, from four distinct activities: walking, stationary, going upstairs, and going downstairs. The data collected is processed through Edge Impulse, an edge AI platform, which enables the training of machine learning models that can be deployed directly onto the microcontroller for real-time activity classification.The data preprocessing step involves extracting relevant features from the raw sensor data using techniques such as sliding windows and data normalization, followed by training a Deep Neural Network (DNN) classifier for activity recognition. The model achieves over 80% accuracy on a test dataset, demonstrating its ability to classify the four activities effectively. Additionally, the platform enables anomaly detection, further enhancing the robustness of the system. The integration of Tiny ML ensures low-power operation, making it suitable for battery-powered or energy-harvesting devices.
【2】High-Dimensional Data Classification in Concentric Coordinates
标题：同心坐标中的多维数据分类
链接：https://arxiv.org/abs/2507.18450
备注：8 pages, 21 figures
摘要：具有可解释方法的多维数据的可视化仍然受到不受遮挡影响的高维无损可视化和通过参数化可视化在计算上能够实现的能力的限制。本文提出了一个低到高维的数据支持框架，使用无损同心坐标，是一个更紧凑的推广平行坐标以及以前的圆形坐标。这些是通用线坐标可视化的形式，可以直接支持机器学习算法可视化并促进人类交互。
摘要：The visualization of multi-dimensional data with interpretable methods remains limited by capabilities for both high-dimensional lossless visualizations that do not suffer from occlusion and that are computationally capable by parameterized visualization. This paper proposes a low to high dimensional data supporting framework using lossless Concentric Coordinates that are a more compact generalization of Parallel Coordinates along with former Circular Coordinates. These are forms of the General Line Coordinate visualizations that can directly support machine learning algorithm visualization and facilitate human interaction.
【3】GVCCS: A Dataset for Contrail Identification and Tracking on Visible Whole Sky Camera Sequences
标题：GBCCS：用于可见全天相机序列的轨迹识别和跟踪的数据集
链接：https://arxiv.org/abs/2507.18330
摘要：航空业对气候的影响不仅包括二氧化碳排放，还包括显著的非二氧化碳影响，特别是来自飞机尾迹的影响。这些冰云可以改变地球的辐射平衡，可能与航空二氧化碳的变暖效应相媲美。基于物理学的模型提供了对轨迹形成和气候影响的有用估计，但其准确性在很大程度上取决于大气输入数据的质量，以及用于表示冰粒形成和湿度驱动的持久性等复杂过程的假设。来自卫星和地面照相机等遥感器的观测数据可用于验证和校准这些模型。然而，现有的数据集没有探索轨迹动力学和形成的所有方面：它们通常缺乏时间跟踪，并且没有将轨迹归因于它们的源航班。为了解决这些限制，我们提出了地面可见相机尾迹序列（GVCCS），一个新的开放数据集的尾迹记录与地面的全天空相机在可见光范围内。每个轨迹都被单独标记并随着时间的推移进行跟踪，从而可以对其生命周期进行详细分析。该数据集包含122个视频序列（24，228帧），并包括在相机上方形成的轨迹的飞行标识符。作为参考，我们还提出了一个统一的深度学习框架，用于使用全景分割模型进行轨迹分析，该模型在单个架构中执行语义分割（轨迹像素识别），实例分割（单个轨迹分离）和时间跟踪。通过提供高质量，时间分辨的注释和模型评估的基准，我们的工作支持改进的轨迹监测，并将促进更好地校准物理模型。这为更准确地理解和评估气候影响奠定了基础。
摘要：Aviation's climate impact includes not only CO2 emissions but also significant non-CO2 effects, especially from contrails. These ice clouds can alter Earth's radiative balance, potentially rivaling the warming effect of aviation CO2. Physics-based models provide useful estimates of contrail formation and climate impact, but their accuracy depends heavily on the quality of atmospheric input data and on assumptions used to represent complex processes like ice particle formation and humidity-driven persistence. Observational data from remote sensors, such as satellites and ground cameras, could be used to validate and calibrate these models. However, existing datasets don't explore all aspect of contrail dynamics and formation: they typically lack temporal tracking, and do not attribute contrails to their source flights. To address these limitations, we present the Ground Visible Camera Contrail Sequences (GVCCS), a new open data set of contrails recorded with a ground-based all-sky camera in the visible range. Each contrail is individually labeled and tracked over time, allowing a detailed analysis of its lifecycle. The dataset contains 122 video sequences (24,228 frames) and includes flight identifiers for contrails that form above the camera. As reference, we also propose a unified deep learning framework for contrail analysis using a panoptic segmentation model that performs semantic segmentation (contrail pixel identification), instance segmentation (individual contrail separation), and temporal tracking in a single architecture. By providing high-quality, temporally resolved annotations and a benchmark for model evaluation, our work supports improved contrail monitoring and will facilitate better calibration of physical models. This sets the groundwork for more accurate climate impact understanding and assessments.
【4】Sparse identification of nonlinear dynamics with library optimization mechanism: Recursive long-term prediction perspective
标题：利用库优化机制对非线性动力学进行稀疏识别：渐进式长期预测视角
链接：https://arxiv.org/abs/2507.18220
备注：This work has been submitted to the IEEE for possible publication
摘要：非线性动力学稀疏辨识（SINDy）方法可以根据测量数据发现动力系统的控制方程，其中动力学模型被辨识为给定基函数的稀疏线性组合。SINDy的一个主要挑战是库的设计，它是一组候选基函数，因为对于许多动力系统来说，合适的库并不是微不足道的。为了克服这一困难，本研究提出了SINDy与库优化机制（SINDy-LOM），这是一个稀疏回归技术和库的新的学习策略相结合。在所提出的方法中，基函数参数化。SINDy-LOM方法涉及两层优化架构：内层，其中数据驱动模型被提取为候选基函数的稀疏线性组合，外层，其中基函数从递归长期（RLT）预测精度的角度进行优化;因此，库设计被重新表述为参数化基函数的优化。所得到的SINDy-LOM模型具有良好的可解释性和可用性，因为所提出的方法产生的简约模型。库优化机制大大减轻了用户负担。与传统的SINDy方法相比，RLT视角提高了所得模型的可靠性，该方法只能确保提前一步的预测精度。通过将其应用于柴油机气道系统，这是一个著名的复杂的工业系统，所提出的方法的有效性证明。
摘要：The sparse identification of nonlinear dynamics (SINDy) approach can discover the governing equations of dynamical systems based on measurement data, where the dynamical model is identified as the sparse linear combination of the given basis functions. A major challenge in SINDy is the design of a library, which is a set of candidate basis functions, as the appropriate library is not trivial for many dynamical systems. To overcome this difficulty, this study proposes SINDy with library optimization mechanism (SINDy-LOM), which is a combination of the sparse regression technique and the novel learning strategy of the library. In the proposed approach, the basis functions are parametrized. The SINDy-LOM approach involves a two-layer optimization architecture: the inner-layer, in which the data-driven model is extracted as the sparse linear combination of the candidate basis functions, and the outer-layer, in which the basis functions are optimized from the viewpoint of the recursive long-term (RLT) prediction accuracy; thus, the library design is reformulated as the optimization of the parametrized basis functions. The resulting SINDy-LOM model has good interpretability and usability, as the proposed approach yields the parsimonious model. The library optimization mechanism significantly reduces user burden. The RLT perspective improves the reliability of the resulting model compared with the traditional SINDy approach that can only ensure the one-step-ahead prediction accuracy. The validity of the proposed approach is demonstrated by applying it to a diesel engine airpath system, which is a well-known complex industrial system.
【5】C-AAE: Compressively Anonymizing Autoencoders for Privacy-Preserving Activity Recognition in Healthcare Sensor Streams
标题：C-AAE：压缩式自动编码器，以在医疗保健传感器流中实现隐私保护活动识别
链接：https://arxiv.org/abs/2507.18072
摘要：可穿戴加速度计和陀螺仪对细粒度的行为特征进行编码，可用于重新识别用户，这使得隐私保护对于医疗保健应用至关重要。我们介绍了C-AAE，一种压缩匿名自动编码器，它将匿名自动编码器（AAE）与自适应差分脉冲编码调制（ADPCM）结合在一起。AAE首先将原始传感器窗口投影到一个潜在空间中，该空间保留活动相关特征，同时抑制身份线索。然后，ADPCM对该潜在流进行差分编码，进一步掩蔽残余身份信息并收缩比特率。在MotionSense和PAMAP 2数据集上的实验表明，相对于单独的AAE，C-AAE将用户重新识别F1分数降低了10-15个百分点，同时将活动识别F1保持在未受保护的基线的5个百分点以内。ADPCM还减少了大约75%的数据量，减轻了传输和存储开销。这些结果表明，C-AAE提供了一个实用的途径，以平衡隐私和实用性的连续，基于传感器的活动识别医疗保健。
摘要：Wearable accelerometers and gyroscopes encode fine-grained behavioural signatures that can be exploited to re-identify users, making privacy protection essential for healthcare applications. We introduce C-AAE, a compressive anonymizing autoencoder that marries an Anonymizing AutoEncoder (AAE) with Adaptive Differential Pulse-Code Modulation (ADPCM). The AAE first projects raw sensor windows into a latent space that retains activity-relevant features while suppressing identity cues. ADPCM then differentially encodes this latent stream, further masking residual identity information and shrinking the bitrate. Experiments on the MotionSense and PAMAP2 datasets show that C-AAE cuts user re-identification F1 scores by 10-15 percentage points relative to AAE alone, while keeping activity-recognition F1 within 5 percentage points of the unprotected baseline. ADPCM also reduces data volume by roughly 75 %, easing transmission and storage overheads. These results demonstrate that C-AAE offers a practical route to balancing privacy and utility in continuous, sensor-based activity recognition for healthcare.
【6】CoCAI: Copula-based Conformal Anomaly Identification for Multivariate Time-Series
标题：CoCAE：基于Copula的多元时间序列共形异常识别
链接：https://arxiv.org/abs/2507.17796
备注：Accepted for Presentation at Runtime Verification 25
摘要：我们提出了一个新的框架，利用生成式人工智能和基于copula的建模来解决多变量时间序列分析中的两个关键挑战：提供准确的预测和实现强大的异常检测。我们的方法，基于Copula的多变量时间序列共形异常识别（CoCAI），利用基于扩散的模型来捕获数据中的复杂依赖关系，从而实现高质量的预测。使用保形预测技术进一步校准模型的输出，产生统计上有效的预测区域，即，以期望的置信水平覆盖真实的目标值。从这些校准的预测开始，通过将降维技术与基于copula的建模相结合来执行稳健的离群值检测，从而提供统计接地异常得分。CoCAI受益于离线校准阶段，允许在部署过程中最小的开销，并提供植根于既定理论基础的可操作结果。对来自配水和污水系统的实际操作数据进行的实证测试证实了CoCAI在准确预测数据的目标序列和识别其中的异常段方面的有效性。
摘要：We propose a novel framework that harnesses the power of generative artificial intelligence and copula-based modeling to address two critical challenges in multivariate time-series analysis: delivering accurate predictions and enabling robust anomaly detection. Our method, Copula-based Conformal Anomaly Identification for Multivariate Time-Series (CoCAI), leverages a diffusion-based model to capture complex dependencies within the data, enabling high quality forecasting. The model's outputs are further calibrated using a conformal prediction technique, yielding predictive regions which are statistically valid, i.e., cover the true target values with a desired confidence level. Starting from these calibrated forecasts, robust outlier detection is performed by combining dimensionality reduction techniques with copula-based modeling, providing a statistically grounded anomaly score. CoCAI benefits from an offline calibration phase that allows for minimal overhead during deployment and delivers actionable results rooted in established theoretical foundations. Empirical tests conducted on real operational data derived from water distribution and sewerage systems confirm CoCAI's effectiveness in accurately forecasting target sequences of data and in identifying anomalous segments within them.
3D|3D重建等相关(1篇)
【1】ReSem3D: Refinable 3D Spatial Constraints via Fine-Grained Semantic Grounding for Generalizable Robotic Manipulation
标题：ReSemm 3D：通过细粒度语义基础可细化3D空间约束，以实现可推广的机器人操纵
链接：https://arxiv.org/abs/2507.18262
备注：12 pages,9 figures
摘要：语义驱动的3D空间约束使高级语义表示与低级动作空间对齐，促进了机器人操作中任务理解和执行的统一。多模态大语言模型（MLLM）和视觉基础模型（VFM）的协同推理使跨模态3D空间约束构造成为可能。然而，现有的方法有三个关键的限制：（1）粗糙的语义粒度的约束建模，（2）缺乏实时闭环规划，（3）妥协的鲁棒性在语义上不同的环境。为了解决这些挑战，我们提出了ReSem 3D，一个统一的操纵框架语义多样化的环境，利用VFM和MLLM之间的协同作用，以实现细粒度的视觉接地和动态构造层次的3D空间约束实时操纵。具体来说，该框架是由MLLM中的分层递归推理驱动的，MLLM与VFM交互，以自动从自然语言指令和RGB-D观察两个阶段构建3D空间约束：部分级提取和区域级细化。随后，将这些约束编码为关节空间中的实时优化目标，从而实现对动态干扰的反应行为。在语义丰富的家庭和稀疏的化学实验室环境中进行广泛的模拟和现实世界的实验。结果表明，ReSem 3D在零触发（zero-shot）条件下执行多种操作任务，具有较强的适应性和通用性。代码和视频在https://resem3d.github.io。
摘要：Semantics-driven 3D spatial constraints align highlevel semantic representations with low-level action spaces, facilitating the unification of task understanding and execution in robotic manipulation. The synergistic reasoning of Multimodal Large Language Models (MLLMs) and Vision Foundation Models (VFMs) enables cross-modal 3D spatial constraint construction. Nevertheless, existing methods have three key limitations: (1) coarse semantic granularity in constraint modeling, (2) lack of real-time closed-loop planning, (3) compromised robustness in semantically diverse environments. To address these challenges, we propose ReSem3D, a unified manipulation framework for semantically diverse environments, leveraging the synergy between VFMs and MLLMs to achieve fine-grained visual grounding and dynamically constructs hierarchical 3D spatial constraints for real-time manipulation. Specifically, the framework is driven by hierarchical recursive reasoning in MLLMs, which interact with VFMs to automatically construct 3D spatial constraints from natural language instructions and RGB-D observations in two stages: part-level extraction and region-level refinement. Subsequently, these constraints are encoded as real-time optimization objectives in joint space, enabling reactive behavior to dynamic disturbances. Extensive simulation and real-world experiments are conducted in semantically rich household and sparse chemical lab environments. The results demonstrate that ReSem3D performs diverse manipulation tasks under zero-shot conditions, exhibiting strong adaptability and generalization. Code and videos at https://resem3d.github.io.
编码器(1篇)
【1】VIBE: Video-Input Brain Encoder for fMRI Response Modeling
标题：VMBE：用于fMRI反应建模的视频输入大脑编码器
链接：https://arxiv.org/abs/2507.17958
摘要：我们提出了VIBE，一个两阶段的Transformer，融合多模态视频，音频和文本功能来预测功能磁共振成像活动。来自开源模型（Qwen2.5、BEAT、Whisper、SlowFast、V-JEPA）的表示由模态融合Transformer合并，并由具有旋转嵌入的预测Transformer进行时间解码。在来自CNeuroMod数据集的65小时电影数据上进行训练，并在20个种子中进行集成，VIBE在发行版Friends S 07上获得了32.25的平均分组皮尔逊相关性，在六部发行版外电影上获得了21.25的平均分组皮尔逊相关性。同一架构的早期迭代分别获得0.3198和0.2096，赢得了第一阶段，并在Algonauts 2025挑战赛中获得第二名。
摘要：We present VIBE, a two-stage Transformer that fuses multi-modal video, audio, and text features to predict fMRI activity. Representations from open-source models (Qwen2.5, BEATs, Whisper, SlowFast, V-JEPA) are merged by a modality-fusion transformer and temporally decoded by a prediction transformer with rotary embeddings. Trained on 65 hours of movie data from the CNeuroMod dataset and ensembled across 20 seeds, VIBE attains mean parcel-wise Pearson correlations of 32.25 on in-distribution Friends S07 and 21.25 on six out-of-distribution films. An earlier iteration of the same architecture obtained 0.3198 and 0.2096, respectively, winning Phase-1 and placing second overall in the Algonauts 2025 Challenge.
优化|敛散性(5篇)
【1】TRPrompt: Bootstrapping Query-Aware Prompt Optimization from Textual Rewards
标题：TRPromise：从文本奖励中引导查询感知提示优化
链接：https://arxiv.org/abs/2507.18618
摘要：即时优化提高了大型语言模型（LLM）的推理能力，而无需对目标模型进行参数更新。在基于启发式的“一步一步思考”方法之后，该领域已经朝着两个主要方向发展：一组方法使用文本反馈以无训练的方式从通用LLM中引出改进的提示，同时的研究依赖于数字奖励来训练特殊提示模型，为目标模型提供最佳提示。在本文中，我们介绍了文本奖励提示框架（TRPrompt），它通过直接将文本反馈纳入提示模型的训练来统一这些方法。我们的框架不需要事先收集数据集，并且正在通过对生成的提示的反馈进行迭代改进。当与LLM内在化什么是“好”提示的概念的能力相结合时，文本奖励提供的高分辨率信号允许我们训练提示模型，为来自具有挑战性的数学数据集GSMHard和MATH的问题产生最先进的特定于查询的提示。
摘要：Prompt optimization improves the reasoning abilities of large language models (LLMs) without requiring parameter updates to the target model. Following heuristic-based "Think step by step" approaches, the field has evolved in two main directions: while one group of methods uses textual feedback to elicit improved prompts from general-purpose LLMs in a training-free way, a concurrent line of research relies on numerical rewards to train a special prompt model, tailored for providing optimal prompts to the target model. In this paper, we introduce the Textual Reward Prompt framework (TRPrompt), which unifies these approaches by directly incorporating textual feedback into training of the prompt model. Our framework does not require prior dataset collection and is being iteratively improved with the feedback on the generated prompts. When coupled with the capacity of an LLM to internalize the notion of what a "good" prompt is, the high-resolution signal provided by the textual rewards allows us to train a prompt model yielding state-of-the-art query-specific prompts for the problems from the challenging math datasets GSMHard and MATH.
【2】Group Sequence Policy Optimization
标题：组序列策略优化
链接：https://arxiv.org/abs/2507.18071
摘要：本文介绍了组序列策略优化（GSPO），这是我们用于训练大型语言模型的稳定，高效和高性能的强化学习算法。与以往采用令牌级重要性比的算法不同，GSPO基于序列似然定义重要性比，并执行序列级裁剪、奖励和优化。我们证明，GSPO实现了优越的训练效率和性能相比，GRPO算法，特别是稳定混合专家（MoE）RL训练，并有可能简化RL基础设施的设计。GSPO的这些优点促成了最新Qwen3型号的显着改进。
摘要：This paper introduces Group Sequence Policy Optimization (GSPO), our stable, efficient, and performant reinforcement learning algorithm for training large language models. Unlike previous algorithms that adopt token-level importance ratios, GSPO defines the importance ratio based on sequence likelihood and performs sequence-level clipping, rewarding, and optimization. We demonstrate that GSPO achieves superior training efficiency and performance compared to the GRPO algorithm, notably stabilizes Mixture-of-Experts (MoE) RL training, and has the potential for simplifying the design of RL infrastructure. These merits of GSPO have contributed to the remarkable improvements in the latest Qwen3 models.
【3】SIFOTL: A Principled, Statistically-Informed Fidelity-Optimization Method for Tabular Learning
标题：SIFOTL：一种原则性的、基于统计信息的表格学习保真度优化方法
链接：https://arxiv.org/abs/2507.17979
摘要：识别驱动表格数据集中数据变化的因素对于分析和决策支持系统来说是一个重大挑战，尤其是那些专注于医疗保健的系统。隐私规则限制了数据访问，复杂流程的噪音阻碍了分析。为了解决这一挑战，我们提出了SIFOTL（统计信息保真度优化方法表学习），（i）提取符合隐私的数据汇总统计，（ii）采用双XGBoost模型在LLM的帮助下将干预信号与噪声分离，（iii）通过Pareto加权决策树合并XGBoost输出，以确定负责移位的可解释段。与现有的分析可能会忽略噪音或需要基于LLM的分析的完整数据访问不同，SIFOTL仅使用隐私安全的汇总统计来解决这两个挑战。对于模拟新的医疗保险药物补贴的MEPS面板数据集，SIFOTL的F1得分为0.85，大大优于BigQuery贡献分析（F1=0.46）和统计测试（F1=0.20），以确定接受补贴的细分市场。此外，在基于Synthea ABM生成的18个不同的EHR数据集中，SIFOTL在没有噪声的情况下保持F1分数为0.86-0.96，即使注入观测噪声也>= 0.75，而在相同的测试下，基线平均F1分数范围为0.19-0.67。因此，SIFOTL提供了一个可解释的、注重隐私的工作流程，该工作流程对观测噪声具有经验鲁棒性。
摘要：Identifying the factors driving data shifts in tabular datasets is a significant challenge for analysis and decision support systems, especially those focusing on healthcare. Privacy rules restrict data access, and noise from complex processes hinders analysis. To address this challenge, we propose SIFOTL (Statistically-Informed Fidelity-Optimization Method for Tabular Learning) that (i) extracts privacy-compliant data summary statistics, (ii) employs twin XGBoost models to disentangle intervention signals from noise with assistance from LLMs, and (iii) merges XGBoost outputs via a Pareto-weighted decision tree to identify interpretable segments responsible for the shift. Unlike existing analyses which may ignore noise or require full data access for LLM-based analysis, SIFOTL addresses both challenges using only privacy-safe summary statistics. Demonstrating its real-world efficacy, for a MEPS panel dataset mimicking a new Medicare drug subsidy, SIFOTL achieves an F1 score of 0.85, substantially outperforming BigQuery Contribution Analysis (F1=0.46) and statistical tests (F1=0.20) in identifying the segment receiving the subsidy. Furthermore, across 18 diverse EHR datasets generated based on Synthea ABM, SIFOTL sustains F1 scores of 0.86-0.96 without noise and >= 0.75 even with injected observational noise, whereas baseline average F1 scores range from 0.19-0.67 under the same tests. SIFOTL, therefore, provides an interpretable, privacy-conscious workflow that is empirically robust to observational noise.
【4】Hybrid quantum-classical algorithm for near-optimal planning in POMDPs
标题：POMDPs中接近最优规划的量子经典混合算法
链接：https://arxiv.org/abs/2507.18606
摘要：强化学习（RL）为部分可观察环境中的决策提供了一个原则性框架，可以将其建模为马尔可夫决策过程，并通过动态决策贝叶斯网络进行压缩表示。最近的进展表明，稀疏贝叶斯网络上的推理可以使用量子拒绝采样结合振幅放大来加速，从而导致估计接受概率的计算加速。在此基础上，我们引入了量子贝叶斯强化学习（QBRL），这是一种混合量子经典前瞻算法，用于部分可观察环境中基于模型的RL。我们提出了一个严格的，无预言机的时间复杂度分析下的容错假设的量子设备。与假设黑盒预言的标准处理不同，我们显式指定推理过程，使我们的界限能够更准确地反映真实的计算成本。我们表明，对于动态形成稀疏贝叶斯网络的环境，可以通过量子增强的信念更新以亚二次快的速度实现基于地平线的近最佳规划。 此外，我们提出了数值实验基准QBRL对它的经典的简单而说明性的决策任务。我们的研究结果提供了量子计算优势如何转化为决策性能的详细分析，强调了优势的大小在不同的部署设置中可能会有很大差异。
摘要 ：Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactly represented through dynamic decision Bayesian networks. Recent advances demonstrate that inference on sparse Bayesian networks can be accelerated using quantum rejection sampling combined with amplitude amplification, leading to a computational speedup in estimating acceptance probabilities.\\ Building on this result, we introduce Quantum Bayesian Reinforcement Learning (QBRL), a hybrid quantum-classical look-ahead algorithm for model-based RL in partially observable environments. We present a rigorous, oracle-free time complexity analysis under fault-tolerant assumptions for the quantum device. Unlike standard treatments that assume a black-box oracle, we explicitly specify the inference process, allowing our bounds to more accurately reflect the true computational cost. We show that, for environments whose dynamics form a sparse Bayesian network, horizon-based near-optimal planning can be achieved sub-quadratically faster through quantum-enhanced belief updates. Furthermore, we present numerical experiments benchmarking QBRL against its classical counterpart on simple yet illustrative decision-making tasks. Our results offer a detailed analysis of how the quantum computational advantage translates into decision-making performance, highlighting that the magnitude of the advantage can vary significantly across different deployment settings.
【5】Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control I: Penalty Approach
标题：群稀疏反馈线性二次最优控制的非凸优化框架I：惩罚方法
链接：https://arxiv.org/abs/2507.18114
摘要：针对无限时域线性二次型（LQ）问题，提出了一个群稀疏反馈控制器设计的统一非凸优化框架。我们解决经典LQ问题的两个突出的扩展：具有固定通信拓扑的分布式LQ问题（DFT-LQ）和稀疏反馈LQ问题（SF-LQ），这两个问题的动机是在大规模系统中需要可扩展和结构感知控制。不同于现有的方法，依赖于凸松弛或仅限于块对角结构，我们直接制定控制器合成作为一个有限维非凸优化问题与组$\ell_0$-范数正则化，捕捉一般稀疏模式。我们建立了DFT-LQ和SF-LQ问题之间的联系，表明两者都可以在我们的统一框架内解决。此外，我们提出了一种基于惩罚的近似交替线性化最小化（PALM）算法，并提供了一个严格的收敛性分析，在温和的假设下，克服了缺乏的目标函数。所提出的方法承认有效的解决方案的所有子问题，并保证全局收敛到临界点。我们的研究结果填补了文献中的一个关键空白，使直接设计组稀疏反馈增益的理论保证，而不诉诸凸代理或限制性的结构假设。
摘要：This paper develops a unified nonconvex optimization framework for the design of group-sparse feedback controllers in infinite-horizon linear-quadratic (LQ) problems. We address two prominent extensions of the classical LQ problem: the distributed LQ problem with fixed communication topology (DFT-LQ) and the sparse feedback LQ problem (SF-LQ), both of which are motivated by the need for scalable and structure-aware control in large-scale systems. Unlike existing approaches that rely on convex relaxations or are limited to block-diagonal structures, we directly formulate the controller synthesis as a finite-dimensional nonconvex optimization problem with group $\ell_0$-norm regularization, capturing general sparsity patterns. We establish a connection between DFT-LQ and SF-LQ problems, showing that both can be addressed within our unified framework. Furthermore, we propose a penalty-based proximal alternating linearized minimization (PALM) algorithm and provide a rigorous convergence analysis under mild assumptions, overcoming the lack of coercivity in the objective function. The proposed method admits efficient solvers for all subproblems and guarantees global convergence to critical points. Our results fill a key gap in the literature by enabling the direct design of group-sparse feedback gains with theoretical guarantees, without resorting to convex surrogates or restrictive structural assumptions.
预测|估计(8篇)
【1】Digital Twin Technologies in Predictive Maintenance: Enabling Transferability via Sim-to-Real and Real-to-Sim Transfer
标题：预测性维护中的数字双胞胎技术：通过模拟到真实和真实到模拟传输实现可传输性
链接：https://arxiv.org/abs/2507.18449
备注：Accepted and presented at 2024 ASCE International Conference on Computing in Civil Engineering (i3CE 2024)
摘要：物联网（IoT）和人工智能的发展促进了数字孪生（DT）从概念性想法到更可实现的现实的演变。然而，由于缺乏标准化的框架，从学术界到工业界的过渡是复杂的。本文建立在作者以前建立的功能和信息需求支持标准化DT开发，重点放在一个关键方面：可移植性。虽然现有的DT研究主要集中在资产转移上，但“模拟到真实转移”和“真实到模拟转移”（在模拟和真实世界运营之间转移知识）的重要性对于DT的全面生命周期管理至关重要。这一过程中的一个关键挑战是校准“现实差距”，即模拟预测与实际结果之间的差异。我们的研究调查了将单个现实差距分析（RGA）模块集成到现有DT框架中以有效管理模拟到真实和真实到模拟传输的影响。数据管道将RGA模块与DT框架的现有组件（包括历史存储库和仿真模型）连接起来，从而促进了这种集成。在卡内基梅隆大学的人行天桥上的案例研究展示了我们的方法与现有框架的不同层次的集成的性能。通过全面实施RGA模块和完整的数据管道，我们的方法能够在不影响效率的情况下在模拟和真实世界操作之间进行双向知识转移。
摘要：The advancement of the Internet of Things (IoT) and Artificial Intelligence has catalyzed the evolution of Digital Twins (DTs) from conceptual ideas to more implementable realities. Yet, transitioning from academia to industry is complex due to the absence of standardized frameworks. This paper builds upon the authors' previously established functional and informational requirements supporting standardized DT development, focusing on a crucial aspect: transferability. While existing DT research primarily centers on asset transfer, the significance of "sim-to-real transfer" and "real-to-sim transfer"--transferring knowledge between simulations and real-world operations--is vital for comprehensive lifecycle management in DTs. A key challenge in this process is calibrating the "reality gap," the discrepancy between simulated predictions and actual outcomes. Our research investigates the impact of integrating a single Reality Gap Analysis (RGA) module into an existing DT framework to effectively manage both sim-to-real and real-to-sim transfers. This integration is facilitated by data pipelines that connect the RGA module with the existing components of the DT framework, including the historical repository and the simulation model. A case study on a pedestrian bridge at Carnegie Mellon University showcases the performance of different levels of integration of our approach with an existing framework. With full implementation of an RGA module and a complete data pipeline, our approach is capable of bidirectional knowledge transfer between simulations and real-world operations without compromising efficiency.
【2】NLML-HPE: Head Pose Estimation with Limited Data via Manifold Learning
标题：NLML-HPE：通过管汇学习利用有限数据进行头部姿势估计
链接：https://arxiv.org/abs/2507.18429
摘要：头部姿态估计（HPE）在各种计算机视觉应用中起着关键作用，例如人机交互和面部识别。在本文中，我们提出了一种新的深度学习方法，用于通过非线性流形学习（称为NLML-HPE）使用有限的训练数据进行头部姿势估计。该方法基于张量分解（即，Tucker分解）和前馈神经网络。与传统的基于分类的方法不同，我们的方法将头部姿态估计公式化为回归问题，将输入地标映射到姿态角的连续表示中。为此，我们的方法使用张量分解来分割每个欧拉角（偏航，俯仰，滚动）以分离子空间，并将底层流形的每个维度建模为余弦曲线。我们应对两个关键挑战：1。几乎所有HPE数据集都存在不正确和不准确的姿势注释。因此，我们通过旋转固定姿势集的3D头部模型并渲染相应的2D图像，为我们的训练集生成了精确且一致的2D头部姿势数据集。2.我们在有限的训练数据下实现了实时性能，因为我们的方法准确地从面部标志中捕获了对象旋转的性质。一旦学习了围绕每个轴旋转的基础流形，模型就可以非常快地预测看不见的数据。我们的训练和测试代码可以在线获得，以及我们的训练模型：https：//github.com/MahdiGhafoorian/NLML_HPE。
摘要 ：Head pose estimation (HPE) plays a critical role in various computer vision applications such as human-computer interaction and facial recognition. In this paper, we propose a novel deep learning approach for head pose estimation with limited training data via non-linear manifold learning called NLML-HPE. This method is based on the combination of tensor decomposition (i.e., Tucker decomposition) and feed forward neural networks. Unlike traditional classification-based approaches, our method formulates head pose estimation as a regression problem, mapping input landmarks into a continuous representation of pose angles. To this end, our method uses tensor decomposition to split each Euler angle (yaw, pitch, roll) to separate subspaces and models each dimension of the underlying manifold as a cosine curve. We address two key challenges: 1. Almost all HPE datasets suffer from incorrect and inaccurate pose annotations. Hence, we generated a precise and consistent 2D head pose dataset for our training set by rotating 3D head models for a fixed set of poses and rendering the corresponding 2D images. 2. We achieved real-time performance with limited training data as our method accurately captures the nature of rotation of an object from facial landmarks. Once the underlying manifold for rotation around each axis is learned, the model is very fast in predicting unseen data. Our training and testing code is available online along with our trained models: https: //github.com/MahdiGhafoorian/NLML_HPE.
【3】Multi-Model Ensemble and Reservoir Computing for River Discharge Prediction in Ungauged Basins
标题：无资料流域河流流量预测的多模型集合和水库计算
链接：https://arxiv.org/abs/2507.18423
摘要：尽管迫切需要准确的洪水预报和水资源管理，但许多地区缺乏足够的河流流量观测，限制了整体径流分析的技能。尽管存在许多基于物理和机器学习的模型，但在数据稀缺的条件下实现高准确性，可解释性和计算效率仍然是一个重大挑战。我们解决这个挑战与一种新的方法，水文预测与多模型集成和水库计算（HYPER），利用多模型集成和水库计算（RC）。我们的方法首先适用于贝叶斯模型平均（BMA）的43个“未校准”流域为基础的概念性水文模型。然后，通过线性回归训练RC模型，以纠正BMA输出中的错误，这是一个非迭代过程，可确保高计算效率。对于无资料流域，我们推断所需的BMA和RC的权重，将它们连接到集水属性从有资料的流域，创建一个可推广的框架。我们使用来自日本87个流域的数据对HYPER进行了评估。在数据丰富的场景中，HYPER（中位数Kling-Gupta效率，KGE为0.56）执行了与基准LSTM（KGE 0.55）相当的性能，但只需要其计算时间的5%。在数据稀缺的情况下（23%的流域被测量），HYPER保持了稳健的性能（KGE 0.55）和较低的不确定性，而LSTM的性能显著下降（KGE -0.04）。这些结果表明，个别概念水文模型不一定需要校准时，一个有效的大合奏组装和基于机器学习的偏差校正相结合。HYPER为流量预测提供了一个强大，高效和可推广的解决方案，特别是在无资料流域，使其适用于广泛的地区。
摘要：Despite the critical need for accurate flood prediction and water management, many regions lack sufficient river discharge observations, limiting the skill of rainfall-runoff analyses. Although numerous physically based and machine learning models exist, achieving high accuracy, interpretability, and computational efficiency under data-scarce conditions remains a major challenge. We address this challenge with a novel method, HYdrological Prediction with multi-model Ensemble and Reservoir computing (HYPER) that leverages multi-model ensemble and reservoir computing (RC). Our approach first applies Bayesian model averaging (BMA) to 43 "uncalibrated" catchment-based conceptual hydrological models. An RC model is then trained via linear regression to correct errors in the BMA output, a non-iterative process that ensures high computational efficiency. For ungauged basins, we infer the required BMA and RC weights by linking them to catchment attributes from gauged basins, creating a generalizable framework. We evaluated HYPER using data from 87 river basins in Japan. In a data-rich scenario, HYPER (median Kling-Gupta Efficiency, KGE, of 0.56) performed comparably to a benchmark LSTM (KGE 0.55) but required only 5% of its computational time. In a data-scarce scenario (23% of basins gauged), HYPER maintained robust performance (KGE 0.55) and lower uncertainty, whereas the LSTM's performance degraded significantly (KGE -0.04). These results reveal that individual conceptual hydrological models do not necessarily need to be calibrated when an effectively large ensemble is assembled and combined with machine-learning-based bias correction. HYPER provides a robust, efficient, and generalizable solution for discharge prediction, particularly in ungauged basins, making it applicable to a wide range of regions.
【4】Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring
标题：利用数据增强和Siamese学习进行预测流程监控
链接：https://arxiv.org/abs/2507.18293
摘要：预测性流程监控（PPM）支持基于事件日志预测未来事件或正在进行的业务流程实例的结果。然而，深度学习PPM方法通常受到现实世界事件日志的低可变性和小尺寸的限制。为了解决这个问题，我们引入了SiamSA-PPM，这是一种新颖的自监督学习框架，它将Siamese学习与统计增强相结合，用于预测过程监控。它采用了三种新的统计接地转换方法，利用控制流语义和频繁的行为模式，以产生现实的，语义上有效的新的跟踪变量。这些增强的视图在暹罗学习设置中使用，以学习过程前缀的可概括表示，而不需要标记监督。在真实事件日志上进行的大量实验表明，与SOTA相比，SiamSA-PPM在下一个活动和最终结果预测任务中都实现了具有竞争力或优越的性能。我们的研究结果进一步表明，统计增强显著优于随机变换，并改善了数据的可变性，突出了SiamSA-PPM作为过程预测中训练数据丰富的一个有前途的方向。
摘要：Predictive Process Monitoring (PPM) enables forecasting future events or outcomes of ongoing business process instances based on event logs. However, deep learning PPM approaches are often limited by the low variability and small size of real-world event logs. To address this, we introduce SiamSA-PPM, a novel self-supervised learning framework that combines Siamese learning with Statistical Augmentation for Predictive Process Monitoring. It employs three novel statistically grounded transformation methods that leverage control-flow semantics and frequent behavioral patterns to generate realistic, semantically valid new trace variants. These augmented views are used within a Siamese learning setup to learn generalizable representations of process prefixes without the need for labeled supervision. Extensive experiments on real-life event logs demonstrate that SiamSA-PPM achieves competitive or superior performance compared to the SOTA in both next activity and final outcome prediction tasks. Our results further show that statistical augmentation significantly outperforms random transformations and improves variability in the data, highlighting SiamSA-PPM as a promising direction for training data enrichment in process prediction.
【5】Goal-based Trajectory Prediction for improved Cross-Dataset Generalization
标题：基于目标的弹道预测，用于改进的跨数据集综合
链接：https://arxiv.org/abs/2507.18196
备注：Accepted on IEEE ITSC 2025
摘要：为了实现完全自动驾驶，对周围环境的良好了解是必要的。特别是预测其他交通参与者的未来状态带来了不小的挑战。当前的SotA模型在真实数据集（例如Argoverse 2，NuScenes）上训练时已经显示出有希望的结果。当这些模型被部署到新的/看不见的区域时，就会出现问题。通常，性能会显著下降，表明模型缺乏泛化能力。在这项工作中，我们引入了一种新的图神经网络（GNN），该网络利用由交通参与者和矢量化道路网络组成的异构图。后者用于以多阶段方法对目标（即预测轨迹的端点）进行分类，从而更好地概括看不见的场景。我们通过跨数据集评估来展示目标选择过程的有效性，即在Argoverse 2上进行训练并在NuScenes上进行评估。
摘要：To achieve full autonomous driving, a good understanding of the surrounding environment is necessary. Especially predicting the future states of other traffic participants imposes a non-trivial challenge. Current SotA-models already show promising results when trained on real datasets (e.g. Argoverse2, NuScenes). Problems arise when these models are deployed to new/unseen areas. Typically, performance drops significantly, indicating that the models lack generalization. In this work, we introduce a new Graph Neural Network (GNN) that utilizes a heterogeneous graph consisting of traffic participants and vectorized road network. Latter, is used to classify goals, i.e. endpoints of the predicted trajectories, in a multi-staged approach, leading to a better generalization to unseen scenarios. We show the effectiveness of the goal selection process via cross-dataset evaluation, i.e. training on Argoverse2 and evaluating on NuScenes.
【6】Multiscale Neural PDE Surrogates for Prediction and Downscaling: Application to Ocean Currents
标题：预测和缩减尺度的多尺度神经偏东方程代理：应用于洋流
链接：https://arxiv.org/abs/2507.18067
备注：Workshop @ ICML2025
摘要：由偏微分方程控制的物理系统的精确建模是科学计算的核心挑战。在海洋学中，高分辨率海流数据对于海岸管理、环境监测和海上安全至关重要。然而，现有的卫星产品，例如空间分辨率约为0.08度的哥白尼海水流速数据和全球海洋模型，往往缺乏详细的地方分析所需的空间粒度。在这项工作中，我们（a）引入了一个基于神经算子的监督深度学习框架，用于求解偏微分方程并提供任意分辨率的解决方案，（b）提出了一个应用于哥白尼洋流数据的降尺度模型。此外，我们的方法可以模拟代理偏微分方程和预测解决方案在任意分辨率，无论输入分辨率。我们评估了我们的模型在现实世界的哥白尼海流数据和合成Navier-Stokes模拟数据集。
摘要：Accurate modeling of physical systems governed by partial differential equations is a central challenge in scientific computing. In oceanography, high-resolution current data are critical for coastal management, environmental monitoring, and maritime safety. However, available satellite products, such as Copernicus data for sea water velocity at ~0.08 degrees spatial resolution and global ocean models, often lack the spatial granularity required for detailed local analyses. In this work, we (a) introduce a supervised deep learning framework based on neural operators for solving PDEs and providing arbitrary resolution solutions, and (b) propose downscaling models with an application to Copernicus ocean current data. Additionally, our method can model surrogate PDEs and predict solutions at arbitrary resolution, regardless of the input resolution. We evaluated our model on real-world Copernicus ocean current data and synthetic Navier-Stokes simulation datasets.
【7】Causal Mechanism Estimation in Multi-Sensor Systems Across Multiple Domains
标题：跨多域多传感器系统中的因果机制估计
链接：https://arxiv.org/abs/2507.17792
摘要：为了通过因果关系的视角更深入地了解复杂的传感器系统，我们提出了共同和个体因果机制估计（CICME），这是一种新颖的三步方法，可以从多个领域收集的异构数据中推断因果机制。通过利用因果迁移学习（CTL）的原理，CICME能够在提供足够的样本时可靠地检测域不变的因果机制。所确定的共同因果机制进一步用于指导对每个领域中剩余因果机制的单独估计。CICME的性能进行评估的线性高斯模型的情况下，灵感来自制造过程。在现有的基于连续优化的因果发现方法的基础上，我们证明了CICME利用了在合并数据上应用因果发现的好处，并重复地对来自各个领域的数据进行因果发现，在某些情况下，它甚至优于两种基线方法。
摘要：To gain deeper insights into a complex sensor system through the lens of causality, we present common and individual causal mechanism estimation (CICME), a novel three-step approach to inferring causal mechanisms from heterogeneous data collected across multiple domains. By leveraging the principle of Causal Transfer Learning (CTL), CICME is able to reliably detect domain-invariant causal mechanisms when provided with sufficient samples. The identified common causal mechanisms are further used to guide the estimation of the remaining causal mechanisms in each domain individually. The performance of CICME is evaluated on linear Gaussian models under scenarios inspired from a manufacturing process. Building upon existing continuous optimization-based causal discovery methods, we show that CICME leverages the benefits of applying causal discovery on the pooled data and repeatedly on data from individual domains, and it even outperforms both baseline methods under certain scenarios.
【8】Multimodal Recurrent Ensembles for Predicting Brain Responses to Naturalistic Movies (Algonauts 2025)
标题：预测大脑对自然主义电影反应的多模式复发合奏（Algonauts 2025）
链接：https://arxiv.org/abs/2507.17897
备注：8 pages, 2 figures, 1 table. Invited report, CCN 2025 Algonauts Project session (3rd-place team). Code: this https URL
摘要：准确预测分布的皮质对自然刺激的反应需要模型，随着时间的推移整合视觉，听觉和语义信息。我们提出了一个分层的多模态循环集成，将预先训练的视频，音频和语言嵌入映射到fMRI时间序列，同时四名受试者观看了Algonauts 2025挑战提供的近80小时的电影。特定于模态的双向RNN对时间动态进行编码;它们的隐藏状态被融合并传递到第二个递归层，而轻量级的特定于受试者的头部输出1000个皮层包裹的响应。训练依赖于复合MSE相关损失和课程，逐渐将重点从早期感觉转移到后期关联区域。平均100个模型变量进一步增强了鲁棒性。由此产生的系统在竞争排行榜上排名第三，实现了整体皮尔逊r = 0.2094和所有参与者中最高的单包裹峰值分数（平均r = 0.63），对于最具挑战性的主题（主题5）有特别强的收益。该方法为未来的多模态大脑编码基准建立了一个简单的，可扩展的基线。
摘要：Accurately predicting distributed cortical responses to naturalistic stimuli requires models that integrate visual, auditory and semantic information over time. We present a hierarchical multimodal recurrent ensemble that maps pretrained video, audio, and language embeddings to fMRI time series recorded while four subjects watched almost 80 hours of movies provided by the Algonauts 2025 challenge. Modality-specific bidirectional RNNs encode temporal dynamics; their hidden states are fused and passed to a second recurrent layer, and lightweight subject-specific heads output responses for 1000 cortical parcels. Training relies on a composite MSE-correlation loss and a curriculum that gradually shifts emphasis from early sensory to late association regions. Averaging 100 model variants further boosts robustness. The resulting system ranked third on the competition leaderboard, achieving an overall Pearson r = 0.2094 and the highest single-parcel peak score (mean r = 0.63) among all participants, with particularly strong gains for the most challenging subject (Subject 5). The approach establishes a simple, extensible baseline for future multimodal brain-encoding benchmarks.
其他神经网络|深度学习|模型|建模(18篇)
【1】Neural Tangent Kernels and Fisher Information Matrices for Simple ReLU Networks with Random Hidden Weights
标题：具有随机隐藏权重的简单ReLU网络的神经切核和Fisher信息矩阵
链接：https://arxiv.org/abs/2507.18555
摘要：讨论了具有随机隐藏权重的2层ReLU网络的Fisher信息矩阵和神经正切核（NTK）。我们讨论了这两个概念之间的关系作为一个线性变换，并表明，谱分解NTK的具体形式的特征函数与主要特征值。我们还得到了由两层神经网络所表示的函数的一个逼近公式。
摘要：Fisher information matrices and neural tangent kernels (NTK) for 2-layer ReLU networks with random hidden weight are argued. We discuss the relation between both notions as a linear transformation and show that spectral decomposition of NTK with concrete forms of eigenfunctions with major eigenvalues. We also obtain an approximation formula of the functions presented by the 2-layer neural networks.
【2】The Price equation reveals a universal force-metric-bias law of algorithmic learning and natural selection
标题：普莱斯方程揭示了算法学习和自然选择的普遍力量-指标-偏差定律
链接：https://arxiv.org/abs/2507.18549
摘要：不同的学习算法，优化方法和自然选择共享一个共同的数学结构，尽管它们有明显的差异。在这里，我展示了一个简单的普赖斯方程的变化的符号划分揭示了一个普遍的力度量偏差（FMB）定律：$\Delta\mathbf{\theta} = \mathbf{M}\，\mathbf{f} + \mathbf{b} + \mathbf{\xi}$。力$\mathbf{f}$通过参数和性能之间的协方差驱动参数$\Delta\mathbf{\theta}$的改进。度量$\mathbf{M}$通过逆曲率重新缩放运动。偏置$\mathbf{b}$增加了参考系中的动量或变化。噪声$\mathbf{\xi}$使探索成为可能。这个框架将自然选择、贝叶斯更新、牛顿方法、随机梯度下降、随机朗之万动力学、亚当优化和大多数其他算法统一为同一底层过程的特殊情况。普赖斯方程还揭示了为什么费舍尔信息，Kullback-Leibler分歧和达朗贝尔原理在学习动力学中自然出现。通过揭示这种共同的结构，FMB定律为理解、比较和设计跨学科的学习算法提供了原则性的基础。
摘要：Diverse learning algorithms, optimization methods, and natural selection share a common mathematical structure, despite their apparent differences. Here I show that a simple notational partitioning of change by the Price equation reveals a universal force-metric-bias (FMB) law: $\Delta\mathbf{\theta} = \mathbf{M}\,\mathbf{f} + \mathbf{b} + \mathbf{\xi}$. The force $\mathbf{f}$ drives improvement in parameters, $\Delta\mathbf{\theta}$, through the covariance between the parameters and performance. The metric $\mathbf{M}$ rescales movement by inverse curvature. The bias $\mathbf{b}$ adds momentum or changes in the frame of reference. The noise $\mathbf{\xi}$ enables exploration. This framework unifies natural selection, Bayesian updating, Newton's method, stochastic gradient descent, stochastic Langevin dynamics, Adam optimization, and most other algorithms as special cases of the same underlying process. The Price equation also reveals why Fisher information, Kullback-Leibler divergence, and d'Alembert's principle arise naturally in learning dynamics. By exposing this common structure, the FMB law provides a principled foundation for understanding, comparing, and designing learning algorithms across disciplines.
【3】Elucidating the Design Space of Arbitrary-Noise-Based Diffusion Models
标题：解析基于辅助噪音的扩散模型的设计空间
链接：https://arxiv.org/abs/2507.18534
备注：21 pages, 4 figures
摘要：EDM阐明了扩散模型的统一设计空间，但其固定的噪声模式仅限于纯高斯噪声，限制了图像恢复的进展。我们的研究表明，强制注入高斯噪声破坏退化图像，过度扩展图像变换距离，并增加恢复的复杂性。为了解决这个问题，我们提出的EDA阐明了基于随机噪声的扩散模型的设计空间。从理论上讲，EDA扩展了噪声模式的自由度，同时保留了EDM的原始模块灵活性，并严格证明了在恢复过程中增加噪声复杂度不会产生额外的计算开销。EDA在三个典型任务上进行了验证：MRI偏置场校正（全局平滑噪声）、CT金属伪影减少（全局尖锐噪声）和自然图像阴影去除（局部边界感知噪声）。仅5个采样步骤，EDA优于大多数特定任务的方法，并在偏置场校正和阴影消除方面实现了最先进的性能。
摘要：EDM elucidates the unified design space of diffusion models, yet its fixed noise patterns restricted to pure Gaussian noise, limit advancements in image restoration. Our study indicates that forcibly injecting Gaussian noise corrupts the degraded images, overextends the image transformation distance, and increases restoration complexity. To address this problem, our proposed EDA Elucidates the Design space of Arbitrary-noise-based diffusion models. Theoretically, EDA expands the freedom of noise pattern while preserving the original module flexibility of EDM, with rigorous proof that increased noise complexity incurs no additional computational overhead during restoration. EDA is validated on three typical tasks: MRI bias field correction (global smooth noise), CT metal artifact reduction (global sharp noise), and natural image shadow removal (local boundary-aware noise). With only 5 sampling steps, EDA outperforms most task-specific methods and achieves state-of-the-art performance in bias field correction and shadow removal.
【4】A Comprehensive Review of Diffusion Models in Smart Agriculture: Progress, Applications, and Challenges
标题：智慧农业扩散模型的全面回顾：进展、应用和挑战
链接：https://arxiv.org/abs/2507.18376
摘要：随着全球人口增长和耕地资源日益稀缺，智慧农业和精准农业成为未来农业发展的重要方向，人工智能（AI）技术，特别是深度学习模型，在作物监测和病虫害检测等领域得到了广泛应用。作为一种新兴的生成模型，扩散模型在农业图像处理、数据增强和遥感等任务中表现出了巨大的前景。与传统的生成对抗网络（GANs）相比，扩散模型提供了更好的训练稳定性和生成质量，有效地解决了农业数据有限和图像样本不平衡等挑战。本文综述了扩散模型在农业中应用的最新进展，重点介绍了扩散模型在农作物病虫害检测、遥感图像增强、作物生长预测和农业资源管理等方面的应用潜力。实验结果表明，扩散模型显着提高模型的准确性和鲁棒性的数据增强，图像生成和去噪，特别是在复杂的环境。尽管在计算效率和推广能力方面存在挑战，但随着技术的进步，预计扩散模型将在智能和精准农业中发挥越来越重要的作用，为全球农业的可持续发展提供实质性支持。
摘要：With the global population growing and arable land resources becoming increasingly scarce,smart agriculture and precision agriculture have emerged as key directions for the future ofagricultural development.Artificial intelligence (AI) technologies, particularly deep learning models, have found widespread applications in areas such as crop monitoring and pest detection. As an emerging generative model, diffusion models have shown significant promise in tasks like agricultural image processing, data augmentation, and remote sensing. Compared to traditional generative adversarial networks (GANs), diffusion models offer superior training stability and generation quality, effectively addressing challenges such as limited agricultural data and imbalanced image samples. This paper reviews the latest advancements in the application of diffusion models in agriculture, focusing on their potential in crop pest and disease detection, remote sensing image enhancement, crop growth prediction, and agricultural resource management. Experimental results demonstrate that diffusion models significantly improve model accuracy and robustness in data augmentation, image generation, and denoising, especially in complex environments. Despite challenges related to computational efficiency and generalization capabilities, diffusion models are expected to play an increasingly important role in smart and precision agriculture as technology advances, providing substantial support for the sustainable development of global agriculture.
【5】ChronoSelect: Robust Learning with Noisy Labels via Dynamics Temporal Memory
标题：Chronoselect：通过动态时间记忆利用噪音标签进行稳健学习
链接：https://arxiv.org/abs/2507.18183
摘要：在真实世界的数据集上训练深度神经网络通常会受到噪声标签的阻碍，这些标签可以被过度参数化的模型记住，导致泛化性能显著下降。虽然现有的带噪声标签（LNL）学习方法已经取得了相当大的进展，但它们从根本上受到静态快照评估的影响，并且无法利用学习进化的丰富时间动态。在本文中，我们提出了ChronoSelect（计时表示其时间性质），一种新颖的框架，具有创新的四阶段内存架构，压缩预测历史到紧凑的时间分布。我们独特的滑动更新机制与受控衰减保持每个样本只有四个动态内存单元，逐步强调最近的模式，同时保留必要的历史知识。这使得通过时间轨迹分析和双分支一致性将样本精确地三向划分为干净、边界和噪声子集。理论保证证明了该机制在噪声条件下的收敛性和稳定性。大量的实验证明了ChronoSelect在合成和真实世界基准测试中的最先进性能。
摘要 ：Training deep neural networks on real-world datasets is often hampered by the presence of noisy labels, which can be memorized by over-parameterized models, leading to significant degradation in generalization performance. While existing methods for learning with noisy labels (LNL) have made considerable progress, they fundamentally suffer from static snapshot evaluations and fail to leverage the rich temporal dynamics of learning evolution. In this paper, we propose ChronoSelect (chrono denoting its temporal nature), a novel framework featuring an innovative four-stage memory architecture that compresses prediction history into compact temporal distributions. Our unique sliding update mechanism with controlled decay maintains only four dynamic memory units per sample, progressively emphasizing recent patterns while retaining essential historical knowledge. This enables precise three-way sample partitioning into clean, boundary, and noisy subsets through temporal trajectory analysis and dual-branch consistency. Theoretical guarantees prove the mechanism's convergence and stability under noisy conditions. Extensive experiments demonstrate ChronoSelect's state-of-the-art performance across synthetic and real-world benchmarks.
【6】Clo-HDnn: A 4.66 TFLOPS/W and 3.78 TOPS/W Continual On-Device Learning Accelerator with Energy-efficient Hyperdimensional Computing via Progressive Search
标题：Clo-HDnn：4.66 TFLOPS/W和3.78 TOPS/W连续设备上学习加速器，通过渐进式搜索实现节能超维计算
链接：https://arxiv.org/abs/2507.17953
备注：Published in 2025 Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits), Kyoto, Japan, 2025
摘要：Clo-HDnn是一款设备上学习（ODL）加速器，专为新兴的持续学习（CL）任务而设计。Clo-HDnn集成了多维计算（HDC）、低成本的Kronecker HD编码器和权重聚类特征提取（WCFE），以优化准确性和效率。Clo-HDnn采用无梯度CL，以类超向量的形式有效地更新和存储学习到的知识。它的双模式操作可以为简单的数据集绕过昂贵的特征提取，而渐进式搜索通过仅编码和比较部分查询超向量将复杂性降低高达61%。达到4.66 TFLOPS/W（FE）和3.78 TOPS/W（分类器），Clo-HDnn与SOTA ODL加速器相比，提供了7.77倍和4.85倍的能效。
摘要：Clo-HDnn is an on-device learning (ODL) accelerator designed for emerging continual learning (CL) tasks. Clo-HDnn integrates hyperdimensional computing (HDC) along with low-cost Kronecker HD Encoder and weight clustering feature extraction (WCFE) to optimize accuracy and efficiency. Clo-HDnn adopts gradient-free CL to efficiently update and store the learned knowledge in the form of class hypervectors. Its dual-mode operation enables bypassing costly feature extraction for simpler datasets, while progressive search reduces complexity by up to 61% by encoding and comparing only partial query hypervectors. Achieving 4.66 TFLOPS/W (FE) and 3.78 TOPS/W (classifier), Clo-HDnn delivers 7.77x and 4.85x higher energy efficiency compared to SOTA ODL accelerators.
【7】From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models
标题：从种子到收获：利用人工智能增强人类创造力，实现红色团队文本到图像模型
链接：https://arxiv.org/abs/2507.17922
摘要：文本到图像（T2I）模型在许多应用中变得普遍，这使得它们对对抗性攻击的鲁棒性评估成为一个关键的优先事项。在不同的领域中持续访问新的和具有挑战性的对抗性提示，对于压力测试这些模型对来自多个载体的新型攻击的弹性至关重要。用于生成这样的提示的当前技术或者完全由人创作或者合成地生成。一方面，人工制作的对抗性提示的数据集通常太小，并且在文化和上下文表示方面不平衡。另一方面，合成生成的提示的数据集实现了规模，但通常缺乏人类制作的提示中的现实细微差别和创造性对抗策略。为了结合人类和机器方法的优势，我们提出了Seed2Harvest，这是一种混合红队方法，用于指导文化多样性，人工制作的对抗性提示种子的扩展。由此产生的提示保留了人类提示的特征和攻击模式，同时保持了可比的平均攻击成功率（0.31 NudeNet，0.36 SD NSFW，0.12 Q16）。我们的扩展数据集实现了更高的多样性，具有535个独特的地理位置和7.48的香农熵，而原始数据集中有58个位置和5.28的熵。我们的工作证明了人机协作在利用人类创造力和机器计算能力以实现全面的、可扩展的红团队进行连续T2I模型安全评估方面的重要性。
摘要：Text-to-image (T2I) models have become prevalent across numerous applications, making their robust evaluation against adversarial attacks a critical priority. Continuous access to new and challenging adversarial prompts across diverse domains is essential for stress-testing these models for resilience against novel attacks from multiple vectors. Current techniques for generating such prompts are either entirely authored by humans or synthetically generated. On the one hand, datasets of human-crafted adversarial prompts are often too small in size and imbalanced in their cultural and contextual representation. On the other hand, datasets of synthetically-generated prompts achieve scale, but typically lack the realistic nuances and creative adversarial strategies found in human-crafted prompts. To combine the strengths of both human and machine approaches, we propose Seed2Harvest, a hybrid red-teaming method for guided expansion of culturally diverse, human-crafted adversarial prompt seeds. The resulting prompts preserve the characteristics and attack patterns of human prompts while maintaining comparable average attack success rates (0.31 NudeNet, 0.36 SD NSFW, 0.12 Q16). Our expanded dataset achieves substantially higher diversity with 535 unique geographic locations and a Shannon entropy of 7.48, compared to 58 locations and 5.28 entropy in the original dataset. Our work demonstrates the importance of human-machine collaboration in leveraging human creativity and machine computational capacity to achieve comprehensive, scalable red-teaming for continuous T2I model safety evaluation.
【8】SETOL: A Semi-Empirical Theory of (Deep) Learning
标题：SETOL：（深度）学习的半经验理论
链接：https://arxiv.org/abs/2507.17912
备注：139 pages, 28 figures. Code for experiments available at this https URL
摘要：我们提出了一个半经验学习理论（SETOL），解释了最先进的（SOTA）神经网络（NN）的卓越性能。我们提供了一个正式的解释重尾自正则化（HTSR）的唯象理论中的基本量的起源：重尾幂律层质量度量，阿尔法和阿尔法帽。在之前的工作中，这些指标已经被证明可以预测预先训练的SOTA NN模型的测试准确性趋势，重要的是，无需访问测试或训练数据。我们的SETOL使用统计力学技术以及随机矩阵理论和量子化学的先进方法。推导提出了理想学习的新数学前提，包括一个新的度量ERG，它相当于应用威尔逊精确重整化群的一个步骤。我们测试的假设和预测的SETOL上一个简单的3层多层感知器（MLP），表现出良好的协议与关键的理论假设。对于SOTA NN模型，我们展示了如何通过简单地计算层权重矩阵的经验谱密度（ESD）并将此ESD插入我们的SETOL公式来估计训练NN的各个层质量。值得注意的是，我们检查了HTSR alpha和SETOL ERG层质量指标的性能，发现它们在我们的MLP和SOTA NN上都非常一致。
摘要：We present a SemiEmpirical Theory of Learning (SETOL) that explains the remarkable performance of State-Of-The-Art (SOTA) Neural Networks (NNs). We provide a formal explanation of the origin of the fundamental quantities in the phenomenological theory of Heavy-Tailed Self-Regularization (HTSR): the heavy-tailed power-law layer quality metrics, alpha and alpha-hat. In prior work, these metrics have been shown to predict trends in the test accuracies of pretrained SOTA NN models, importantly, without needing access to either testing or training data. Our SETOL uses techniques from statistical mechanics as well as advanced methods from random matrix theory and quantum chemistry. The derivation suggests new mathematical preconditions for ideal learning, including a new metric, ERG, which is equivalent to applying a single step of the Wilson Exact Renormalization Group. We test the assumptions and predictions of SETOL on a simple 3-layer multilayer perceptron (MLP), demonstrating excellent agreement with the key theoretical assumptions. For SOTA NN models, we show how to estimate the individual layer qualities of a trained NN by simply computing the empirical spectral density (ESD) of the layer weight matrices and plugging this ESD into our SETOL formulas. Notably, we examine the performance of the HTSR alpha and the SETOL ERG layer quality metrics, and find that they align remarkably well, both on our MLP and on SOTA NNs.
【9】Deep learning-aided inverse design of porous metamaterials
标题：多孔超材料的深度学习辅助逆设计
链接：https://arxiv.org/abs/2507.17907
备注：31 pages, 29 figures
摘要：该研究的最终目的是使用基于深度学习的生成框架探索多孔超材料的逆向设计。具体来说，我们开发了一个属性变分自动编码器（pVAE），一个变分自动编码器（VAE）与回归增强，生成结构化的超材料与定制的水力特性，如孔隙率和渗透率。虽然这项工作使用晶格玻尔兹曼方法（LBM）生成有限的多孔微观结构的固有渗透率张量数据，卷积神经网络（CNN）的训练使用自下而上的方法来预测有效的水力特性。与直接LBM模拟相比，这显著降低了计算成本。pVAE框架在两个数据集上进行训练：人工多孔微结构的合成数据集和来自真实开孔泡沫的体积元素的CT扫描图像。VAE的编码器-解码器架构捕获关键的微观结构特征，将其映射到紧凑且可解释的潜在空间中，以进行有效的结构-性质探索。该研究对潜在空间进行了详细的分析和解释，展示了其在结构-属性映射、插值和逆向设计中的作用。这种方法有助于产生具有所需特性的新超材料。本研究中使用的数据集和代码将开放获取，以支持进一步的研究。
摘要：The ultimate aim of the study is to explore the inverse design of porous metamaterials using a deep learning-based generative framework. Specifically, we develop a property-variational autoencoder (pVAE), a variational autoencoder (VAE) augmented with a regressor, to generate structured metamaterials with tailored hydraulic properties, such as porosity and permeability. While this work uses the lattice Boltzmann method (LBM) to generate intrinsic permeability tensor data for limited porous microstructures, a convolutional neural network (CNN) is trained using a bottom-up approach to predict effective hydraulic properties. This significantly reduces the computational cost compared to direct LBM simulations. The pVAE framework is trained on two datasets: a synthetic dataset of artificial porous microstructures and CT-scan images of volume elements from real open-cell foams. The encoder-decoder architecture of the VAE captures key microstructural features, mapping them into a compact and interpretable latent space for efficient structure-property exploration. The study provides a detailed analysis and interpretation of the latent space, demonstrating its role in structure-property mapping, interpolation, and inverse design. This approach facilitates the generation of new metamaterials with desired properties. The datasets and codes used in this study will be made open-access to support further research.
【10】Lower Bounds for Public-Private Learning under Distribution Shift
标题：分布转移下公私学习的下界
链接：https://arxiv.org/abs/2507.17895
备注：Preprint
摘要：在实践中，最有效的差异化私有机器学习算法依赖于一个额外的据称是公共数据的来源。当两个来源结合起来超过其部分的总和时，这种范式最有趣。然而，也有一些设置，如均值估计，我们有很强的下限，这表明当两个数据源具有相同的分布时，组合两个数据源没有互补的价值。在这项工作中，我们将公-私学习的已知下限扩展到两个数据源表现出显着分布偏移的设置。我们的结果适用于高斯均值估计的两个分布有不同的手段，和高斯线性回归的两个分布表现出参数移动。我们发现，当移位是小的（相对于期望的精度），无论是公共或私人数据必须足够丰富的私人参数估计。相反，当变化很大时，公共数据没有任何好处。
摘要：The most effective differentially private machine learning algorithms in practice rely on an additional source of purportedly public data. This paradigm is most interesting when the two sources combine to be more than the sum of their parts. However, there are settings such as mean estimation where we have strong lower bounds, showing that when the two data sources have the same distribution, there is no complementary value to combining the two data sources. In this work we extend the known lower bounds for public-private learning to setting where the two data sources exhibit significant distribution shift. Our results apply to both Gaussian mean estimation where the two distributions have different means, and to Gaussian linear regression where the two distributions exhibit parameter shift. We find that when the shift is small (relative to the desired accuracy), either public or private data must be sufficiently abundant to estimate the private parameter. Conversely, when the shift is large, public data provides no benefit.
【11】Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data
标题：HSYS 1.0：表格科学数据上可重复和可解释机器学习的开源框架
链接：https://arxiv.org/abs/2507.17791
备注：17 pages
摘要：Hacker是一个开源的、可扩展的、基于Python的软件框架，用于促进表格数据的可复制和可解释的机器学习工作流。它满足了对透明实验数据分析来源日益增长的需求，确保整个分析过程-包括有关数据转换和方法选择的决策-被记录，可访问，可复制和相关利益相关者可理解。该平台包括标准化数据预处理、可视化、机器学习模型训练、评估、解释、结果检查和未知数据的模型预测等模块。为了进一步使没有接受过数据科学正规培训的研究人员能够获得有意义和可操作的见解，Hessels提供了一个用户友好的界面，可以设计计算实验，检查结果，包括使用语言学术语的机器学习决策的新解释方法。根据MIT许可证发布，Hundreds可以通过GitHub和PyPI访问，支持社区驱动的开发并促进遵守FAIR原则。
摘要：Helix is an open-source, extensible, Python-based software framework to facilitate reproducible and interpretable machine learning workflows for tabular data. It addresses the growing need for transparent experimental data analytics provenance, ensuring that the entire analytical process -- including decisions around data transformation and methodological choices -- is documented, accessible, reproducible, and comprehensible to relevant stakeholders. The platform comprises modules for standardised data preprocessing, visualisation, machine learning model training, evaluation, interpretation, results inspection, and model prediction for unseen data. To further empower researchers without formal training in data science to derive meaningful and actionable insights, Helix features a user-friendly interface that enables the design of computational experiments, inspection of outcomes, including a novel interpretation approach to machine learning decisions using linguistic terms all within an integrated environment. Released under the MIT licence, Helix is accessible via GitHub and PyPI, supporting community-driven development and promoting adherence to the FAIR principles.
【12】Hyperbolic Deep Learning for Foundation Models: A Survey
标题：基础模型的双曲深度学习：调查
链接：https://arxiv.org/abs/2507.17787
备注：11 Pages, SIGKDD 2025
摘要：在大规模数据集上预训练的基础模型，包括大型语言模型（LLM），视觉语言模型（VLM）和大型多模态模型，在各种下游任务中取得了显着的成功。然而，最近的研究表明，这些模型的基本局限性：（1）有限的代表能力，（2）较低的适应性，（3）减少可扩展性。这些缺点提出了一个关键问题：欧几里德几何真的是所有基础模型的最佳归纳偏差吗？或者，结合替代几何空间可以使模型更好地与真实世界数据的内在结构保持一致，并改善推理过程？双曲空间是一类非欧几里德流形，其特征在于体积随距离呈指数增长，它提供了一个数学上的解决方案。这些空间使得能够实现分层结构的低失真嵌入（例如，树，分类法）和幂律分布，与欧几里德对应物相比，其维数大大减少。最近的进展利用这些属性来增强基础模型，包括提高LLM的复杂推理能力，VLM的zero-shot泛化和跨模态语义对齐，同时保持参数效率。本文综述了双曲神经网络及其在地基模型中的最新发展。我们进一步概述了推进该领域的关键挑战和研究方向。
摘要 ：Foundation models pre-trained on massive datasets, including large language models (LLMs), vision-language models (VLMs), and large multimodal models, have demonstrated remarkable success in diverse downstream tasks. However, recent studies have shown fundamental limitations of these models: (1) limited representational capacity, (2) lower adaptability, and (3) diminishing scalability. These shortcomings raise a critical question: is Euclidean geometry truly the optimal inductive bias for all foundation models, or could incorporating alternative geometric spaces enable models to better align with the intrinsic structure of real-world data and improve reasoning processes? Hyperbolic spaces, a class of non-Euclidean manifolds characterized by exponential volume growth with respect to distance, offer a mathematically grounded solution. These spaces enable low-distortion embeddings of hierarchical structures (e.g., trees, taxonomies) and power-law distributions with substantially fewer dimensions compared to Euclidean counterparts. Recent advances have leveraged these properties to enhance foundation models, including improving LLMs' complex reasoning ability, VLMs' zero-shot generalization, and cross-modal semantic alignment, while maintaining parameter efficiency. This paper provides a comprehensive review of hyperbolic neural networks and their recent development for foundation models. We further outline key challenges and research directions to advance the field.
【13】Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges
标题：利用基础模型推进金融工程：进展、应用和挑战
链接：https://arxiv.org/abs/2507.18577
备注：Under Review
摘要：基础模型（FM）的出现-具有强大泛化能力的大规模预训练模型-为金融工程开辟了新的领域。虽然GPT-4和Gemini等通用FM在从财务报告摘要到感知预测的任务中表现出了良好的性能，但许多金融应用程序仍然受到多模态推理，法规遵从性和数据隐私等独特领域要求的限制。这些挑战刺激了金融基础模型（FFM）的出现，这是一种专门为金融设计的新模型。本调查对FFM进行了全面的概述，并对三种关键模式进行了分类：金融语言基础模型（FinLFM），金融时间序列基础模型（FinTSFM）和金融视觉语言基础模型（FinVLFM）。我们回顾了他们的架构，培训方法，数据集和现实世界的应用程序。此外，我们还确定了数据可用性，算法可扩展性和基础设施限制方面的关键挑战，并提供了对未来研究机会的见解。我们希望这项调查既能为理解FFM提供全面的参考，也能为未来的创新提供实用的路线图。我们的网站https://github.com/FinFM/Awesome-FinFMs上将保留实况调查团有关出版物和资源的最新汇编。
摘要：The advent of foundation models (FMs) - large-scale pre-trained models with strong generalization capabilities - has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of Financial Foundation Models (FFMs) - a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: Financial Language Foundation Models (FinLFMs), Financial Time-Series Foundation Models (FinTSFMs), and Financial Visual-Language Foundation Models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints, and offer insights into future research opportunities. We hope this survey serves as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation. An updated collection of FFM-related publications and resources will be maintained on our website https://github.com/FinFM/Awesome-FinFMs.
【14】On Reconstructing Training Data From Bayesian Posteriors and Trained Models
标题：关于从Bayesian后验和训练模型重建训练数据
链接：https://arxiv.org/abs/2507.18372
摘要：公开发布模型的规范及其训练参数意味着对手可以尝试通过训练数据重建攻击来重建有关训练数据的信息，这是现代机器学习方法的一个主要漏洞。本文主要有三个贡献：建立一个数学框架来表达这个问题，通过最大平均差异等价性来描述训练数据的特征，并概述了贝叶斯和非贝叶斯模型中重建数据的得分匹配框架，前者是文献中的第一个。
摘要：Publicly releasing the specification of a model with its trained parameters means an adversary can attempt to reconstruct information about the training data via training data reconstruction attacks, a major vulnerability of modern machine learning methods. This paper makes three primary contributions: establishing a mathematical framework to express the problem, characterising the features of the training data that are vulnerable via a maximum mean discrepancy equivalance and outlining a score matching framework for reconstructing data in both Bayesian and non-Bayesian models, the former is a first in the literature.
【15】Quantum Machine Learning Playground
标题：量子机器学习游乐场
链接：https://arxiv.org/abs/2507.17931
备注：Accepted to IEEE Computer Graphics and Applications. Final version: https://doi.org/10.1109/MCG.2024.3456288
摘要：本文介绍了一种创新的交互式可视化工具，旨在揭开量子机器学习（QML）算法的神秘面纱。我们的工作受到经典机器学习可视化工具（如TensorFlow Playground）成功的启发，旨在弥合QML领域可视化资源的差距。本文全面概述了量子计算和经典机器学习的相关可视化隐喻，算法可视化概念的开发，以及作为交互式Web应用程序的具体实现的设计。通过将所谓的数据重新上传通用量子分类器作为代表性的QML模型的常见可视化隐喻相结合，本文旨在降低量子计算的进入门槛，并鼓励该领域的进一步创新。附带的交互式应用程序是量子机器学习操场的第一个版本，用于学习和探索QML模型。
摘要：This article introduces an innovative interactive visualization tool designed to demystify quantum machine learning (QML) algorithms. Our work is inspired by the success of classical machine learning visualization tools, such as TensorFlow Playground, and aims to bridge the gap in visualization resources specifically for the field of QML. The article includes a comprehensive overview of relevant visualization metaphors from both quantum computing and classical machine learning, the development of an algorithm visualization concept, and the design of a concrete implementation as an interactive web application. By combining common visualization metaphors for the so-called data re-uploading universal quantum classifier as a representative QML model, this article aims to lower the entry barrier to quantum computing and encourage further innovation in the field. The accompanying interactive application is a proposal for the first version of a quantum machine learning playground for learning and exploring QML models.
【16】Integrating Feature Selection and Machine Learning for Nitrogen Assessment in Grapevine Leaves using In-Field Hyperspectral Imaging
标题：使用现场高光谱成像集成特征选择和机器学习进行葡萄叶氮评估
链接：https://arxiv.org/abs/2507.17869
摘要 ：氮（N）是葡萄园中最重要的营养素之一，影响植物生长和后续产品，如葡萄酒和果汁。由于土壤氮素具有高度的空间和时间变异性，因此需要准确估计葡萄树叶片的氮素浓度并在单株水平上管理施肥，以最佳地满足植物需求。在这项研究中，我们使用的高光谱图像的波长范围从400美元到1000纳米的四个不同的葡萄品种收集不同的葡萄园，在两个生长季节的两个生长阶段，开发模型，用于预测叶片水平和冠层水平的氮浓度。图像处理后，采用两种特征选择方法，以确定最佳的一组光谱波段，响应叶片氮浓度。选择的光谱带用于训练和测试两种不同的机器学习（ML）模型，梯度提升和XGBoost，用于预测氮浓度。对叶级和冠层级数据集所选波段的比较表明，通过特征选择方法识别的大多数光谱区域跨越两种方法和数据集类型（叶级和冠层级数据集），特别是在关键区域500- 525 nm、650- 690 nm、750- 800 nm和900- 950 nm。这些发现表明这些光谱区域用于预测氮含量的鲁棒性。N预测的结果表明，ML模型实现了0.49的冠层水平数据的R平方和0.57的叶水平数据的R平方，尽管每个分析水平使用不同的选定的光谱带。这项研究表明，使用现场高光谱成像和光谱数据的综合功能选择和ML技术来监测葡萄园的氮素状况的潜力。
摘要：Nitrogen (N) is one of the most crucial nutrients in vineyards, affecting plant growth and subsequent products such as wine and juice. Because soil N has high spatial and temporal variability, it is desirable to accurately estimate the N concentration of grapevine leaves and manage fertilization at the individual plant level to optimally meet plant needs. In this study, we used in-field hyperspectral images with wavelengths ranging from $400 to 1000nm of four different grapevine cultivars collected from distinct vineyards and over two growth stages during two growing seasons to develop models for predicting N concentration at the leaf-level and canopy-level. After image processing, two feature selection methods were employed to identify the optimal set of spectral bands that were responsive to leaf N concentrations. The selected spectral bands were used to train and test two different Machine Learning (ML) models, Gradient Boosting and XGBoost, for predicting nitrogen concentrations. The comparison of selected bands for both leaf-level and canopy-level datasets showed that most of the spectral regions identified by the feature selection methods were across both methods and the dataset types (leaf- and canopy-level datasets), particularly in the key regions, 500-525nm, 650-690nm, 750-800nm, and 900-950nm. These findings indicated the robustness of these spectral regions for predicting nitrogen content. The results for N prediction demonstrated that the ML model achieved an R square of 0.49 for canopy-level data and an R square of 0.57 for leaf-level data, despite using different sets of selected spectral bands for each analysis level. The study demonstrated the potential of using in-field hyperspectral imaging and the use of spectral data in integrated feature selection and ML techniques to monitor N status in vineyards.
【17】Towards Robust Foundation Models for Digital Pathology
标题：迈向稳健的数字病理学基础模型
链接：https://arxiv.org/abs/2507.17845
摘要：生物医学基础模型（FM）正在迅速改变AI医疗保健研究并进入临床验证。然而，他们对学习非生物技术特征的敏感性-包括手术/内窥镜技术，实验室程序和扫描仪硬件的变化-为临床部署带来了风险。我们提出了第一个系统的调查病理FM鲁棒性非生物特征。我们的工作（i）介绍了量化FM鲁棒性的措施，（ii）展示了有限的鲁棒性的后果，（iii）提出了一个FM鲁棒性的框架，以减轻这些问题。具体来说，我们开发了PathoROB，这是一个鲁棒性基准，具有三个新的指标，包括鲁棒性指数，以及四个数据集，涵盖了来自34个医疗中心的28个生物类。我们的实验揭示了所有20个评估的FM的鲁棒性缺陷，以及它们之间的鲁棒性差异。我们发现，非鲁棒FM表示可能导致主要的诊断下游错误和临床失误，从而阻止安全的临床采用。使用更强大的FM和事后鲁棒性大大降低了（但尚未消除）此类错误的风险。这项工作确立了稳健性评价对于在临床采用前确认病理FM至关重要，并证明了未来FM开发必须将稳健性作为核心设计原则。PathoROB提供了一个蓝图，用于评估生物医学领域的稳健性，指导FM改进工作，使其更加稳健，具有代表性和临床可部署的AI系统，将生物信息优先于技术工件。
摘要：Biomedical Foundation Models (FMs) are rapidly transforming AI-enabled healthcare research and entering clinical validation. However, their susceptibility to learning non-biological technical features -- including variations in surgical/endoscopic techniques, laboratory procedures, and scanner hardware -- poses risks for clinical deployment. We present the first systematic investigation of pathology FM robustness to non-biological features. Our work (i) introduces measures to quantify FM robustness, (ii) demonstrates the consequences of limited robustness, and (iii) proposes a framework for FM robustification to mitigate these issues. Specifically, we developed PathoROB, a robustness benchmark with three novel metrics, including the robustness index, and four datasets covering 28 biological classes from 34 medical centers. Our experiments reveal robustness deficits across all 20 evaluated FMs, and substantial robustness differences between them. We found that non-robust FM representations can cause major diagnostic downstream errors and clinical blunders that prevent safe clinical adoption. Using more robust FMs and post-hoc robustification considerably reduced (but did not yet eliminate) the risk of such errors. This work establishes that robustness evaluation is essential for validating pathology FMs before clinical adoption and demonstrates that future FM development must integrate robustness as a core design principle. PathoROB provides a blueprint for assessing robustness across biomedical domains, guiding FM improvement efforts towards more robust, representative, and clinically deployable AI systems that prioritize biological information over technical artifacts.
【18】Comparison of Optimised Geometric Deep Learning Architectures, over Varying Toxicological Assay Data Environments
标题：不同毒理学分析数据环境下优化的几何深度学习架构的比较
链接：https://arxiv.org/abs/2507.17775
摘要：几何深度学习是人工智能（AI）驱动的化学信息学中的一种新兴技术，然而，对于这个领域，不同的图形神经网络（GNN）架构的独特含义却很少被探索。该研究比较了图卷积网络（GCN），图注意力网络（GAT）和图同构网络（GIN）的性能，应用于不同数据丰度和终点的7个不同毒理学检测数据集，以执行检测激活的二进制分类。在对分子图进行预处理、实施类别平衡并对所有数据集进行5倍分层后，对应用于每个测定数据集的每个GNN进行了贝叶斯优化（产生21个唯一的贝叶斯优化）。优化的GNN在曲线下面积（AUC）评分范围为0.728-0.849（所有倍数的平均值）下进行，在特定测定和GNN之间自然变化。在7种数据最丰富的毒理学试验中，GIN的表现始终优于GCN和GAT。然而，GAT显著优于其余2种数据最缺乏的测定。这表明GIN是数据丰富环境的更优架构，而GAT是数据稀缺环境的更优架构。随后对探索的高维超参数空间以及优化的超参数状态进行分析，发现与GIN相比，GCN和GAT彼此达到了可测量的更接近的优化状态，进一步表明GIN作为GNN算法的独特性质。
摘要：Geometric deep learning is an emerging technique in Artificial Intelligence (AI) driven cheminformatics, however the unique implications of different Graph Neural Network (GNN) architectures are poorly explored, for this space. This study compared performances of Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs) and Graph Isomorphism Networks (GINs), applied to 7 different toxicological assay datasets of varying data abundance and endpoint, to perform binary classification of assay activation. Following pre-processing of molecular graphs, enforcement of class-balance and stratification of all datasets across 5 folds, Bayesian optimisations were carried out, for each GNN applied to each assay dataset (resulting in 21 unique Bayesian optimisations). Optimised GNNs performed at Area Under the Curve (AUC) scores ranging from 0.728-0.849 (averaged across all folds), naturally varying between specific assays and GNNs. GINs were found to consistently outperform GCNs and GATs, for the top 5 of 7 most data-abundant toxicological assays. GATs however significantly outperformed over the remaining 2 most data-scarce assays. This indicates that GINs are a more optimal architecture for data-abundant environments, whereas GATs are a more optimal architecture for data-scarce environments. Subsequent analysis of the explored higher-dimensional hyperparameter spaces, as well as optimised hyperparameter states, found that GCNs and GATs reached measurably closer optimised states with each other, compared to GINs, further indicating the unique nature of GINs as a GNN algorithm.
其他(28篇)
【1】Moving Out: Physically-grounded Human-AI Collaboration
标题：移动：物理接地的人类-人工智能协作
链接：https://arxiv.org/abs/2507.18623
备注 ：24 pages, 8 figures
摘要：适应环境中的物理动作和约束的能力对于具体代理（例如，机器人（robots）与人类有效合作。这种基于物理的人类-人工智能协作必须考虑到连续状态-动作空间的复杂性增加以及物理约束导致的约束动态。在本文中，我们介绍了\textit{Moving Out}，这是一种新的人类-人工智能协作基准，类似于受物理属性和约束影响的各种协作模式，例如将沉重的物品一起移动，并保持一致的动作以将大物品移动到角落。使用Moving Out，我们设计了两个任务，并收集了人与人之间的交互数据，以评估模型适应不同人类行为和不可见物理属性的能力。为了解决物理环境中的挑战，我们提出了一种新的方法，BASS（行为增强，模拟和选择），以提高代理的多样性和他们的行动结果的理解。我们的实验表明，BASS在AI与AI和人类与AI协作方面优于最先进的模型。项目页面位于\href{https：//live-robotics-uva.github.io/movingout_ai/}{https：//live-robotics-uva.github.io/movingout\_ai/}。
摘要：The ability to adapt to physical actions and constraints in an environment is crucial for embodied agents (e.g., robots) to effectively collaborate with humans. Such physically grounded human-AI collaboration must account for the increased complexity of the continuous state-action space and constrained dynamics caused by physical constraints. In this paper, we introduce \textit{Moving Out}, a new human-AI collaboration benchmark that resembles a wide range of collaboration modes affected by physical attributes and constraints, such as moving heavy items together and maintaining consistent actions to move a big item around a corner. Using Moving Out, we designed two tasks and collected human-human interaction data to evaluate models' abilities to adapt to diverse human behaviors and unseen physical attributes. To address the challenges in physical environments, we propose a novel method, BASS (Behavior Augmentation, Simulation, and Selection), to enhance the diversity of agents and their understanding of the outcome of actions. Our experiments show that BASS outperforms state-of-the-art models in AI-AI and human-AI collaboration. The project page is available at \href{https://live-robotics-uva.github.io/movingout_ai/}{https://live-robotics-uva.github.io/movingout\_ai/}.
【2】Linear Memory SE(2) Invariant Attention
标题：线性记忆SE（2）不变的注意力
链接：https://arxiv.org/abs/2507.18597
备注：Best paper award, Equivariant Systems Workshop at RSS
摘要：处理空间数据是自动驾驶许多学习任务的关键组成部分，如运动预测、多智能体仿真和规划。先前的工作已经证明了使用SE（2）不变网络架构的价值，该架构仅考虑对象之间的相对姿态（例如，其他代理，场景特征，如交通车道）。然而，这些方法显式地计算所有对象对的相对姿态，需要二次存储器。在这项工作中，我们提出了一种机制SE（2）不变的缩放点积注意，需要线性记忆相对于场景中的对象的数量。我们的SE（2）不变的Transformer架构具有与近年来大型语言模型相同的缩放特性。我们的实验表明，我们的方法是切实可行的实施和提高性能相比，可比的非不变的架构。
摘要：Processing spatial data is a key component in many learning tasks for autonomous driving such as motion forecasting, multi-agent simulation, and planning. Prior works have demonstrated the value in using SE(2) invariant network architectures that consider only the relative poses between objects (e.g. other agents, scene features such as traffic lanes). However, these methods compute the relative poses for all pairs of objects explicitly, requiring quadratic memory. In this work, we propose a mechanism for SE(2) invariant scaled dot-product attention that requires linear memory relative to the number of objects in the scene. Our SE(2) invariant transformer architecture enjoys the same scaling properties that have benefited large language models in recent years. We demonstrate experimentally that our approach is practical to implement and improves performance compared to comparable non-invariant architectures.
【3】DRWKV: Focusing on Object Edges for Low-Light Image Enhancement
标题：DRWKN：聚焦对象边缘以增强弱光图像
链接：https://arxiv.org/abs/2507.18594
摘要：微光图像增强仍然是一项具有挑战性的任务，特别是在极端光照退化的情况下保持物体边缘连续性和精细结构细节。在本文中，我们提出了一种新的模型DRWKV（详细接收加权关键值），它集成了我们提出的全局边缘Retinex（GER）理论，能够有效地解耦照明和边缘结构，以增强边缘保真度。其次，我们引入了Evolving WKV Attention，这是一种螺旋扫描机制，可以更有效地捕获空间边缘连续性并对不规则结构进行建模。第三，我们设计了双边频谱对齐器（Bi-SAB）和定制的MS 2-Loss来联合对齐亮度和色度特征，提高视觉自然度并减轻伪影。在五个LLIE基准测试上的大量实验表明，DRWKV在PSNR，SSIM和NIQE方面取得了领先的性能，同时保持了较低的计算复杂度。此外，DRWKV增强了低光多目标跟踪任务的下游性能，验证了其泛化能力。
摘要：Low-light image enhancement remains a challenging task, particularly in preserving object edge continuity and fine structural details under extreme illumination degradation. In this paper, we propose a novel model, DRWKV (Detailed Receptance Weighted Key Value), which integrates our proposed Global Edge Retinex (GER) theory, enabling effective decoupling of illumination and edge structures for enhanced edge fidelity. Secondly, we introduce Evolving WKV Attention, a spiral-scanning mechanism that captures spatial edge continuity and models irregular structures more effectively. Thirdly, we design the Bilateral Spectrum Aligner (Bi-SAB) and a tailored MS2-Loss to jointly align luminance and chrominance features, improving visual naturalness and mitigating artifacts. Extensive experiments on five LLIE benchmarks demonstrate that DRWKV achieves leading performance in PSNR, SSIM, and NIQE while maintaining low computational complexity. Furthermore, DRWKV enhances downstream performance in low-light multi-object tracking tasks, validating its generalization capabilities.
【4】Beyond Internal Data: Constructing Complete Datasets for Fairness Testing
标题：超越内部数据：构建完整数据集以进行公平性测试
链接：https://arxiv.org/abs/2507.18561
备注：9 pages, 6 figures
摘要：随着人工智能在高风险领域和决策中变得普遍，测试潜在的危害和偏见至关重要。这种紧迫性反映在全球出现的人工智能法规中，这些法规强调公平和充分的测试，其中一些法规要求进行独立的偏见审计。然而，获取公平性测试所需的数据仍然是一项重大挑战。特别是在行业环境中，法律和隐私问题限制了评估群体差异所需的人口统计数据的收集，审计人员在获取数据方面面临着实际和文化挑战。此外，内部历史数据集通常不足以代表识别现实世界的偏见。这项工作的重点是评估分类器的公平性时，完整的数据集，包括人口统计是不可访问的。我们建议利用单独的重叠数据集来构建完整的合成数据，其中包括人口统计信息，并准确反映受保护的属性和模型特征之间的潜在关系。我们通过将合成数据与真实数据进行比较来验证合成数据的保真度，并根据经验证明，从此类合成数据的测试中获得的公平性度量与从真实数据中获得的公平性度量是一致的。因此，这项工作提供了一条克服真实世界公平性测试数据稀缺的途径，实现了独立的、与模型无关的公平性评估，并在真实数据有限的情况下充当可行的替代品。
摘要 ：As AI becomes prevalent in high-risk domains and decision-making, it is essential to test for potential harms and biases. This urgency is reflected by the global emergence of AI regulations that emphasise fairness and adequate testing, with some mandating independent bias audits. However, procuring the necessary data for fairness testing remains a significant challenge. Particularly in industry settings, legal and privacy concerns restrict the collection of demographic data required to assess group disparities, and auditors face practical and cultural challenges in gaining access to data. Further, internal historical datasets are often insufficiently representative to identify real-world biases. This work focuses on evaluating classifier fairness when complete datasets including demographics are inaccessible. We propose leveraging separate overlapping datasets to construct complete synthetic data that includes demographic information and accurately reflects the underlying relationships between protected attributes and model features. We validate the fidelity of the synthetic data by comparing it to real data, and empirically demonstrate that fairness metrics derived from testing on such synthetic data are consistent with those obtained from real data. This work, therefore, offers a path to overcome real-world data scarcity for fairness testing, enabling independent, model-agnostic evaluation of fairness, and serving as a viable substitute where real data is limited.
【5】On the Performance of Concept Probing: The Influence of the Data (Extended Version)
标题：概念探索的性能：数据的影响（扩展版本）
链接：https://arxiv.org/abs/2507.18550
备注：Extended version of the paper published in Proceedings of the European Conference on Artificial Intelligence (ECAI 2025)
摘要：概念探测作为一种帮助解释人工神经网络的方式，最近引起了越来越多的兴趣，既要处理它们通常的大尺寸，又要处理它们的次符号性质，这最终使它们无法直接进行人类解释。概念探测通过训练额外的分类器来将模型的内部表示映射到人类定义的感兴趣的概念，从而允许人类窥视人工神经网络内部。概念探测的研究主要集中在被探测的模型或探测模型本身，很少关注训练这种探测模型所需的数据。在本文中，我们解决这个差距。专注于图像分类任务的背景下的概念探测，我们调查的数据用于训练探测模型对它们的性能的影响。我们还为两个广泛使用的数据集提供了概念标签。
摘要：Concept probing has recently garnered increasing interest as a way to help interpret artificial neural networks, dealing both with their typically large size and their subsymbolic nature, which ultimately renders them unfeasible for direct human interpretation. Concept probing works by training additional classifiers to map the internal representations of a model into human-defined concepts of interest, thus allowing humans to peek inside artificial neural networks. Research on concept probing has mainly focused on the model being probed or the probing model itself, paying limited attention to the data required to train such probing models. In this paper, we address this gap. Focusing on concept probing in the context of image classification tasks, we investigate the effect of the data used to train probing models on their performance. We also make available concept labels for two widely used datasets.
【6】AI/ML Life Cycle Management for Interoperable AI Native RAN
标题：可互操作AI Native RAN的AI/ML生命周期管理
链接：https://arxiv.org/abs/2507.18538
备注：8 pages, 4 figures, 2 table. This work has been submitted to the IEEE for possible publication
摘要：人工智能（AI）和机器学习（ML）模型正在迅速渗透到5G无线接入网络（RAN）中，为波束管理、信道状态信息（CSI）反馈、定位和移动性预测提供动力。然而，如果没有标准化的生命周期管理（LCM）框架，模型漂移、供应商锁定和有限的透明度等挑战将阻碍大规模采用。3GPP版本16 - 20逐步将AI/ML从实验性功能发展为可管理的、可互操作的网络功能。从Rel-16中的网络数据分析功能（NWALK）开始，后续版本引入了用于模型传输，执行，性能监控和闭环控制的标准化接口，最终在Rel-20的双侧CSI压缩工作项和与供应商无关的LCM配置文件中达到高潮。本文回顾了由此产生的五块LCM体系结构、KPI驱动的监控机制和供应商间协作方案，同时确定了资源高效监控、环境漂移检测、智能决策和灵活模型训练方面的开放性挑战。这些发展为AI原生收发器作为6G的关键推动因素奠定了基础。
摘要：Artificial intelligence (AI) and machine learning (ML) models are rapidly permeating the 5G Radio Access Network (RAN), powering beam management, channel state information (CSI) feedback, positioning, and mobility prediction. However, without a standardized life-cycle management (LCM) framework, challenges, such as model drift, vendor lock-in, and limited transparency, hinder large-scale adoption. 3GPP Releases 16-20 progressively evolve AI/ML from experimental features to managed, interoperable network functions. Beginning with the Network Data Analytics Function (NWDAF) in Rel-16, subsequent releases introduced standardized interfaces for model transfer, execution, performance monitoring, and closed-loop control, culminating in Rel-20's two-sided CSI-compression Work Item and vendor-agnostic LCM profile. This article reviews the resulting five-block LCM architecture, KPI-driven monitoring mechanisms, and inter-vendor collaboration schemes, while identifying open challenges in resource-efficient monitoring, environment drift detection, intelligent decision-making, and flexible model training. These developments lay the foundation for AI-native transceivers as a key enabler for 6G.
【7】Remembering the Markov Property in Cooperative MARL
标题：记住合作MARL中的马尔科夫性质
链接：https://arxiv.org/abs/2507.18333
备注：RLC Finding the Frame Workshop Camera-Ready, 8 pages
摘要：协作多智能体强化学习（MARL）通常被形式化为分散式部分可观察马尔可夫决策过程（Dec-POMDP），其中智能体必须对环境和其他智能体的行为进行推理。在实践中，目前的无模型MARL算法使用简单的递归函数逼近器来解决使用部分信息推理他人的挑战。在这份立场文件中，我们认为，这些方法的经验成功是不是由于有效的马尔可夫信号恢复，而是学习简单的公约，绕过环境的观察和记忆。通过有针对性的案例研究，我们表明，共同适应代理可以学习脆性公约，然后失败时，与非自适应代理合作。至关重要的是，当任务设计需要时，相同的模型可以学习接地策略，这表明问题不是学习模型的根本限制，而是基准设计的失败。我们的分析还表明，现代MARL环境可能无法充分测试Dec-POMDP的核心假设。因此，我们倡导建立在两个核心原则基础上的新的合作环境：（1）基于观察的行为和（2）基于记忆的关于其他代理人的推理，确保成功需要真正的技能，而不是脆弱的，共同适应的协议。
摘要：Cooperative multi-agent reinforcement learning (MARL) is typically formalised as a Decentralised Partially Observable Markov Decision Process (Dec-POMDP), where agents must reason about the environment and other agents' behaviour. In practice, current model-free MARL algorithms use simple recurrent function approximators to address the challenge of reasoning about others using partial information. In this position paper, we argue that the empirical success of these methods is not due to effective Markov signal recovery, but rather to learning simple conventions that bypass environment observations and memory. Through a targeted case study, we show that co-adapting agents can learn brittle conventions, which then fail when partnered with non-adaptive agents. Crucially, the same models can learn grounded policies when the task design necessitates it, revealing that the issue is not a fundamental limitation of the learning models but a failure of the benchmark design. Our analysis also suggests that modern MARL environments may not adequately test the core assumptions of Dec-POMDPs. We therefore advocate for new cooperative environments built upon two core principles: (1) behaviours grounded in observations and (2) memory-based reasoning about other agents, ensuring success requires genuine skill rather than fragile, co-adapted agreements.
【8】Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods
标题：重新审视推动：基准和推进基于LP的Ensemble方法
链接：https://arxiv.org/abs/2507.18242
摘要：尽管在理论上很有吸引力，但基于线性规划的完全纠正的提升方法只受到了有限的经验关注。在本文中，我们在20个不同的数据集上对六种基于LP的boosting公式进行了首次大规模实验研究，其中包括两种新方法，NM-Boost和QRLP-Boost。我们评估使用这些配方中的启发式和最佳基础学习者，并分析不仅准确性，而且集成稀疏性，保证金分布，随时性能和超参数敏感性。我们表明，完全正确的方法可以优于或匹配最先进的算法，如XGBoost和LightGBM使用浅树时，同时产生显着稀疏的合奏。我们进一步表明，这些方法可以在不牺牲性能的情况下细化预训练的集合，并且我们强调了在这种情况下使用最优决策树的优势和局限性。
摘要 ：Despite their theoretical appeal, totally corrective boosting methods based on linear programming have received limited empirical attention. In this paper, we conduct the first large-scale experimental study of six LP-based boosting formulations, including two novel methods, NM-Boost and QRLP-Boost, across 20 diverse datasets. We evaluate the use of both heuristic and optimal base learners within these formulations, and analyze not only accuracy, but also ensemble sparsity, margin distribution, anytime performance, and hyperparameter sensitivity. We show that totally corrective methods can outperform or match state-of-the-art heuristics like XGBoost and LightGBM when using shallow trees, while producing significantly sparser ensembles. We further show that these methods can thin pre-trained ensembles without sacrificing performance, and we highlight both the strengths and limitations of using optimal decision trees in this context.
【9】Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions
标题：自治系统中可编程智能的神经形态计算：当前趋势、挑战和未来方向
链接：https://arxiv.org/abs/2507.18139
备注：To appear at the 31st IEEE International Symposium on On-Line Testing and Robust System Design (IOLTS), Ischia, Italy, July 2025
摘要：对智能、自适应和节能自主系统的需求日益增长，这些系统跨越了机器人、移动代理（例如，无人机）和自动驾驶车辆正在推动对神经形态计算的兴趣。通过从生物神经系统中汲取灵感，神经形态方法为增强自主平台的感知、决策和响应能力提供了有前途的途径。本文综述了神经形态算法、专用硬件和跨层优化策略的最新进展，重点介绍了它们在现实世界自主场景中的部署。特别注意基于事件的动态视觉传感器及其在实现快速，有效的感知中的作用。讨论强调了通过将尖峰神经网络集成到自治系统架构中来提高能效、鲁棒性、适应性和可靠性的新方法。我们整合了机器学习，机器人技术，神经科学和神经形态工程的观点，以提供该领域状态的全面视图。最后，探讨了新出现的趋势和开放的挑战，特别是在实时决策，持续学习和安全，弹性自主系统的发展领域。
摘要：The growing need for intelligent, adaptive, and energy-efficient autonomous systems across fields such as robotics, mobile agents (e.g., UAVs), and self-driving vehicles is driving interest in neuromorphic computing. By drawing inspiration from biological neural systems, neuromorphic approaches offer promising pathways to enhance the perception, decision-making, and responsiveness of autonomous platforms. This paper surveys recent progress in neuromorphic algorithms, specialized hardware, and cross-layer optimization strategies, with a focus on their deployment in real-world autonomous scenarios. Special attention is given to event-based dynamic vision sensors and their role in enabling fast, efficient perception. The discussion highlights new methods that improve energy efficiency, robustness, adaptability, and reliability through the integration of spiking neural networks into autonomous system architectures. We integrate perspectives from machine learning, robotics, neuroscience, and neuromorphic engineering to offer a comprehensive view of the state of the field. Finally, emerging trends and open challenges are explored, particularly in the areas of real-time decision-making, continual learning, and the development of secure, resilient autonomous systems.
【10】A New Pair of GloVes
标题：一双新手套
链接：https://arxiv.org/abs/2507.18103
摘要：本报告记录、描述和评估了新的2024年英语GloVe（单词表示的全局向量）模型。虽然2014年构建的原始GloVe模型已被广泛使用并发现有用，但语言和世界仍在不断发展，我们认为当前的使用可以从更新的模型中受益。此外，2014年的模型没有仔细记录所使用的确切数据版本和预处理，我们通过记录这些新模型来纠正这一点。我们使用Wikipedia、Gigaword和Dolma的一个子集训练了两组词嵌入。通过词汇比较、直接测试和NER任务进行的评估表明，2024个向量包含了新的文化和语言相关词汇，对类比和相似性等结构性任务进行了优化，并在最近的时间依赖性NER数据集（如非西方新闻专线数据）上表现出更好的性能。
摘要：This report documents, describes, and evaluates new 2024 English GloVe (Global Vectors for Word Representation) models. While the original GloVe models built in 2014 have been widely used and found useful, languages and the world continue to evolve and we thought that current usage could benefit from updated models. Moreover, the 2014 models were not carefully documented as to the exact data versions and preprocessing that were used, and we rectify this by documenting these new models. We trained two sets of word embeddings using Wikipedia, Gigaword, and a subset of Dolma. Evaluation through vocabulary comparison, direct testing, and NER tasks shows that the 2024 vectors incorporate new culturally and linguistically relevant words, perform comparably on structural tasks like analogy and similarity, and demonstrate improved performance on recent, temporally dependent NER datasets such as non-Western newswire data.
【11】Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover
标题：土地利用土地覆盖遥感分割方法比较
链接：https://arxiv.org/abs/2507.18099
摘要：土地利用/土地覆被（LULC）制图是城市和资源规划的基础，也是发展智能和可持续城市的关键要素之一。本研究评估了先进的LULC制图技术，重点是基于查找表（LUT）的大气校正应用于Cartosat多光谱（MX）传感器图像，其次是监督和半监督学习模型用于LULC预测。我们探索DeeplabV 3+和交叉伪监督（CPS）。CPS模型通过动态加权进一步细化，增强了训练过程中的伪标签可靠性。这种综合方法分析了LULC制图技术在各种城市规划应用中的准确性和实用性。印度海得拉巴的一项案例研究表明，由于快速城市化，土地使用发生了重大变化。通过分析Cartosat MX图像随时间的变化，我们强调了城市扩张，绿色空间缩小和工业区扩大等变化。这表明了这些技术对城市规划者和决策者的实际效用。
摘要：Land Use Land Cover (LULC) mapping is essential for urban and resource planning, and is one of the key elements in developing smart and sustainable cities.This study evaluates advanced LULC mapping techniques, focusing on Look-Up Table (LUT)-based Atmospheric Correction applied to Cartosat Multispectral (MX) sensor images, followed by supervised and semi-supervised learning models for LULC prediction. We explore DeeplabV3+ and Cross-Pseudo Supervision (CPS). The CPS model is further refined with dynamic weighting, enhancing pseudo-label reliability during training. This comprehensive approach analyses the accuracy and utility of LULC mapping techniques for various urban planning applications. A case study of Hyderabad, India, illustrates significant land use changes due to rapid urbanization. By analyzing Cartosat MX images over time, we highlight shifts such as urban sprawl, shrinking green spaces, and expanding industrial areas. This demonstrates the practical utility of these techniques for urban planners and policymakers.
【12】Does visualization help AI understand data?
标题：可视化是否有助于AI理解数据？
链接：https://arxiv.org/abs/2507.18022
备注：5 pages, 6 figures
摘要：图表和图形帮助人们分析数据，但它们对AI系统也有用吗？为了研究这个问题，我们用两个商业视觉语言模型进行了一系列实验：GPT 4.1和Claude 3.5。在三个有代表性的分析任务中，当原始数据伴随着散点图时，这两个系统更精确和准确地描述了合成数据集，特别是当数据集复杂性增加时。通过比较两个基线-一个空白图表和一个数据不匹配的图表-可以看出，业绩的提高是由于图表的内容。我们的研究结果是人工智能系统和人类一样可以从可视化中受益的初步证据。
摘要 ：Charts and graphs help people analyze data, but can they also be useful to AI systems? To investigate this question, we perform a series of experiments with two commercial vision-language models: GPT 4.1 and Claude 3.5. Across three representative analysis tasks, the two systems describe synthetic datasets more precisely and accurately when raw data is accompanied by a scatterplot, especially as datasets grow in complexity. Comparison with two baselines -- providing a blank chart and a chart with mismatched data -- shows that the improved performance is due to the content of the charts. Our results are initial evidence that AI systems, like humans, can benefit from visualization.
【13】Improving the Computational Efficiency and Explainability of GeoAggregator
标题：提高地理聚合器的计算效率和解释性
链接：https://arxiv.org/abs/2507.17977
备注：4 pages, 3 figures
摘要：准确的建模和解释地理空间表格数据（GTD）是理解地理空间现象及其底层过程的关键。最近的工作提出了一种新的基于transformer的深度学习模型，称为GeoAggregator（GA），并已证明它优于其他统计和机器学习方法。在这篇简短的论文中，我们通过以下方式进一步改进GA：1）开发一个优化的管道，加速数据加载过程并简化GA的前向传递，以实现更好的计算效率; 2）基于GeoShapley框架，结合模型集成策略和事后模型解释功能，以增强模型的可解释性。我们通过将改进的GA模型应用于合成数据集，验证了所提出的策略的功能和效率。实验结果表明，我们的实现提高了预测精度和推理速度的GA相比，原来的实现。此外，解释实验表明，遗传算法可以有效地捕捉所设计的合成数据集的内在空间效应。完整的管道已经公开供社区使用（https：//github.com/ruid7181/GA-sklearn）。
摘要：Accurate modeling and explaining geospatial tabular data (GTD) are critical for understanding geospatial phenomena and their underlying processes. Recent work has proposed a novel transformer-based deep learning model named GeoAggregator (GA) for this purpose, and has demonstrated that it outperforms other statistical and machine learning approaches. In this short paper, we further improve GA by 1) developing an optimized pipeline that accelerates the dataloading process and streamlines the forward pass of GA to achieve better computational efficiency; and 2) incorporating a model ensembling strategy and a post-hoc model explanation function based on the GeoShapley framework to enhance model explainability. We validate the functionality and efficiency of the proposed strategies by applying the improved GA model to synthetic datasets. Experimental results show that our implementation improves the prediction accuracy and inference speed of GA compared to the original implementation. Moreover, explanation experiments indicate that GA can effectively captures the inherent spatial effects in the designed synthetic dataset. The complete pipeline has been made publicly available for community use (https://github.com/ruid7181/GA-sklearn).
【14】Fourier Neural Operators for Non-Markovian Processes:Approximation Theorems and Experiments
标题：非马尔科夫过程的傅里叶神经运算符：逼近理论和实验
链接：https://arxiv.org/abs/2507.17887
摘要：本文介绍了一种基于算子的神经网络，镜像填充傅立叶神经算子（MFNO），旨在学习随机系统的动力学。MFNO通过引入镜像填充扩展了标准傅立叶神经运算符（FNO），使其能够处理非周期性输入。我们严格证明了MFNOs可以近似解的路径依赖的随机微分方程和Lipschitz变换的分数布朗运动到任意程度的准确性。我们的理论分析建立在黄-Zakai型定理和各种近似技术。从经验上讲，MFNO表现出很强的分辨率泛化能力--这在LSTM、TCN和DeepONet等标准架构中很少见。此外，我们的模型实现的性能是可比的或优于这些基线，同时提供显着更快的样本路径生成比经典的数值方案。
摘要：This paper introduces an operator-based neural network, the mirror-padded Fourier neural operator (MFNO), designed to learn the dynamics of stochastic systems. MFNO extends the standard Fourier neural operator (FNO) by incorporating mirror padding, enabling it to handle non-periodic inputs. We rigorously prove that MFNOs can approximate solutions of path-dependent stochastic differential equations and Lipschitz transformations of fractional Brownian motions to an arbitrary degree of accuracy. Our theoretical analysis builds on Wong--Zakai type theorems and various approximation techniques. Empirically, the MFNO exhibits strong resolution generalization--a property rarely seen in standard architectures such as LSTMs, TCNs, and DeepONet. Furthermore, our model achieves performance that is comparable or superior to these baselines while offering significantly faster sample path generation than classical numerical schemes.
【15】Look the Other Way: Designing 'Positive' Molecules with Negative Data via Task Arithmetic
标题：换个角度看：通过任务算术用负数据设计“正”分子
链接：https://arxiv.org/abs/2507.17876
摘要：具有所需性质的分子的稀缺性（即，“正”分子）是生成性分子设计的固有瓶颈。为了避开这个障碍，我们提出了分子任务算法：在不同的和丰富的负样本上训练一个模型来学习“属性方向"，而不访问任何正标记的数据，并在相反的属性方向上移动模型来生成正分子。当对20个zero-shot设计实验进行分析时，分子任务算法产生的设计比在正分子上训练的模型更多样化和成功。此外，我们采用分子任务算法在双目标和Few-Shot设计任务。我们发现，分子任务算术可以不断增加设计的多样性，同时保持理想的设计性能。由于其简单性、数据效率和性能，分子任务算法有可能成为从头分子设计的迁移学习策略。
摘要：The scarcity of molecules with desirable properties (i.e., 'positive' molecules) is an inherent bottleneck for generative molecule design. To sidestep such obstacle, here we propose molecular task arithmetic: training a model on diverse and abundant negative examples to learn 'property directions' $--$ without accessing any positively labeled data $--$ and moving models in the opposite property directions to generate positive molecules. When analyzed on 20 zero-shot design experiments, molecular task arithmetic generated more diverse and successful designs than models trained on positive molecules. Moreover, we employed molecular task arithmetic in dual-objective and few-shot design tasks. We find that molecular task arithmetic can consistently increase the diversity of designs while maintaining desirable design properties. With its simplicity, data efficiency, and performance, molecular task arithmetic bears the potential to become the $\textit{de-facto}$ transfer learning strategy for de novo molecule design.
【16】GenSelect: A Generative Approach to Best-of-N
标题：Genselect：N中最佳的生成方法
链接：https://arxiv.org/abs/2507.17797
备注：Presented at the 2nd AI for MATH Workshop @ ICML
摘要：具有并行采样的生成奖励模型已经为推理任务实现了有效的测试时间缩放。目前的方法采用逐点评分的个别解决方案或成对比较。然而，逐点方法未充分利用LLM的比较能力，而成对方法在较大的采样预算下效率低下。我们引入GenSelect，其中LLM使用长推理在N个候选者中选择最佳解决方案。这利用了LLM的比较优势，同时在并行采样预算中有效地扩展。对于数学推理，我们证明了推理模型，如QwQ和DeepSeek-R1-0528，在GenSelect中表现出色，优于现有的简单提示的评分方法。
摘要 ：Generative reward models with parallel sampling have enabled effective test-time scaling for reasoning tasks. Current approaches employ pointwise scoring of individual solutions or pairwise comparisons. However, pointwise methods underutilize LLMs' comparative abilities, while pairwise methods scale inefficiently with larger sampling budgets. We introduce GenSelect, where the LLM uses long reasoning to select the best solution among N candidates. This leverages LLMs' comparative strengths while scaling efficiently across parallel sampling budgets. For math reasoning, we demonstrate that reasoning models, such as QwQ and DeepSeek-R1-0528, excel at GenSelect, outperforming existing scoring approaches with simple prompting.
【17】Knowledge Abstraction for Knowledge-based Semantic Communication: A Generative Causality Invariant Approach
标题：基于知识的语义沟通的知识抽象：生成因果不变方法
链接：https://arxiv.org/abs/2507.17784
备注：13 pages, 12 figures, 4 tables
摘要：在这项研究中，我们设计了一个低复杂度和广义的AI模型，可以捕获共同的知识，以改善数据重构的信道解码器的语义通信。具体来说，我们提出了一个生成对抗网络，它利用因果不变学习从数据中提取因果和非因果表示。因果表示是不变的，包含了识别数据标签的关键信息。它们可以封装语义知识，并促进有效的数据重建在接收端。此外，因果机制确保了学习的表示在不同的领域保持一致，即使用户从不同的领域收集数据，系统也是可靠的。由于用户收集的数据随着时间的推移而演变，导致用户之间的知识分歧，我们设计稀疏更新协议，以提高知识的不变属性，同时最大限度地减少通信开销。从我们的经验评估中得出了三个关键的观察结果。首先，尽管训练数据多种多样，但容差不变知识确保了不同设备之间的一致性。其次，不变知识在分类任务中有很好的表现，这是面向目标的语义通信的关键。第三，我们基于知识的数据重建突出了我们的解码器的鲁棒性，这超过了其他国家的最先进的数据重建和语义压缩方法的峰值信噪比（PSNR）。
摘要：In this study, we design a low-complexity and generalized AI model that can capture common knowledge to improve data reconstruction of the channel decoder for semantic communication. Specifically, we propose a generative adversarial network that leverages causality-invariant learning to extract causal and non-causal representations from the data. Causal representations are invariant and encompass crucial information to identify the data's label. They can encapsulate semantic knowledge and facilitate effective data reconstruction at the receiver. Moreover, the causal mechanism ensures that learned representations remain consistent across different domains, making the system reliable even with users collecting data from diverse domains. As user-collected data evolves over time causing knowledge divergence among users, we design sparse update protocols to improve the invariant properties of the knowledge while minimizing communication overheads. Three key observations were drawn from our empirical evaluations. Firstly, causality-invariant knowledge ensures consistency across different devices despite the diverse training data. Secondly, invariant knowledge has promising performance in classification tasks, which is pivotal for goal-oriented semantic communications. Thirdly, our knowledge-based data reconstruction highlights the robustness of our decoder, which surpasses other state-of-the-art data reconstruction and semantic compression methods in terms of Peak Signal-to-Noise Ratio (PSNR).
【18】PolyServe: Efficient Multi-SLO Serving at Scale
标题：PolyServe：大规模高效的多SLO服务
链接：https://arxiv.org/abs/2507.17769
摘要：大型语言模型（LLM）的进步导致了LLM驱动的应用程序的激增。这些应用程序具有不同的令牌生成延迟要求。因此，简单地将工作负载分类为延迟敏感（LS）或尽力而为（BE）忽略了延迟敏感类别中的细微差别，并导致次优的用户体验和调度机会。然而，高效地服务具有多个SLO要求的请求提出了重大挑战。首先，批处理中的所有请求同时生成新令牌，这可能会使它们与其不同的SLO要求不一致。此外，虽然现有系统专注于自动缩放以处理各种总体请求速率，但SLO的多样性需要在这些SLO层之间进行细粒度的自动缩放。最后，与LS/BE场景不同，在LS/BE场景中，BE请求可以在任何时候被中止以确保LS请求的SLO实现，具有不同延迟敏感SLO的那些场景不能容忍长时间的延迟，并且必须控制尾部延迟。 为了解决这些挑战，我们提出了PolyServe，一种新的多SLO调度策略，在保持高SLO的实现，同时最大限度地提高吞吐量。PolyServe首先根据每个令牌的延迟要求将请求分组到多个bin中，然后将每个bin调度到服务器队列的一个子集。PolyServe将请求路由到负载最高但SLO仍可达到的服务器，以创建有助于自动缩放的负载梯度。为了提高利用率，PolyServe允许宽松的SLO请求在自己的服务器饱和时共享严格的SLO实例。PolyServe使用分析数据来指导调度决策，并通过请求等待时间感知调度、动态分块和连续分块预填充预测来管理尾部延迟。与现有策略相比，PolyServe实现了1.23倍的有效吞吐量增益，实现了高达92.5%的最佳有效吞吐量。
摘要：Advances in Large Language Models (LLMs) have led to a surge of LLM-powered applications. These applications have diverse token-generation latency requirements. As a result, simply classifying workloads as latency-sensitive (LS) or best-effort (BE) overlooks the nuances within the latency-sensitive category and results in suboptimal user experiences and scheduling opportunities. However, efficiently serving requests with multiple SLO requirements poses significant challenges. First, all requests within a batch generate new tokens simultaneously, which can misalign them with their distinct SLO requirements. Moreover, while existing systems focus on auto-scaling for handling various overall request rates, the diversity of SLOs necessitates fine-grained auto-scaling among these SLO tiers. Finally, unlike LS/BE scenarios, where BE requests can be aborted at any time to ensure the SLO attainment of LS requests, those with different latency-sensitive SLOs cannot tolerate prolonged delays, and tail latency must be controlled. To tackle these challenges, we propose PolyServe, a novel multi-SLO scheduling policy at scale that maintains high SLO attainment while maximizing throughput. PolyServe first groups requests into multiple bins based on their per-token latency requirement, then schedules each bin to a subset of the server fleet. PolyServe routes requests to the highest-load but still SLO-attainable server to create a load gradient that facilitates auto-scaling. To increase utilization, PolyServe permits looser-SLO requests to share tighter-SLO instances when their own servers are saturated. PolyServe uses profiling data to guide scheduling decisions and manage tail latency through request-wait-time-aware scheduling, dynamic chunking, and continuous chunked prefill prediction. PolyServe achieves 1.23x goodput gain compared to existing policies, achieving up to 92.5% of optimal goodput.
【19】Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction
标题：通过相对熵核心集选择和级联层纠正增强边缘设备上的量化感知训练
链接：https://arxiv.org/abs/2507.17768
摘要：随着移动和边缘计算的发展，对边缘设备上的低比特量化模型的需求正在增加，以实现高效部署。为了提高性能，通常需要使用边缘数据重新训练量化模型。然而，由于隐私问题，某些敏感数据只能在边缘设备上处理。因此，在边缘设备上采用量化感知训练（QAT）已成为一种有效的解决方案。然而，传统的QAT依赖于完整的数据集进行训练，这会产生巨大的计算成本。核心集选择技术可以通过在最具代表性的子集上进行训练来缓解这个问题。然而，当使用小规模数据集（例如，只有10%的数据），导致显著的性能下降。为了解决这些问题，我们提出了一个在边缘设备上使用coreset的QAT框架，它包括两个主要阶段：在coreset选择阶段，coreset引入了“相对熵分数”来识别最有效地捕获模型量化误差的子集。在训练阶段，BRAC采用级联层校正策略将量化模型的中间层输出与全精度模型的中间层输出对齐，从而有效地减少中间层中的量化误差。实验结果证明了该方法的有效性。例如，当使用1%的数据子集将ResNet-18量化为2位时，与最先进的技术相比，PARC RC在ImageNet-1 K数据集上的Top-1准确度提高了5.72%。
摘要 ：With the development of mobile and edge computing, the demand for low-bit quantized models on edge devices is increasing to achieve efficient deployment. To enhance the performance, it is often necessary to retrain the quantized models using edge data. However, due to privacy concerns, certain sensitive data can only be processed on edge devices. Therefore, employing Quantization-Aware Training (QAT) on edge devices has become an effective solution. Nevertheless, traditional QAT relies on the complete dataset for training, which incurs a huge computational cost. Coreset selection techniques can mitigate this issue by training on the most representative subsets. However, existing methods struggle to eliminate quantization errors in the model when using small-scale datasets (e.g., only 10% of the data), leading to significant performance degradation. To address these issues, we propose QuaRC, a QAT framework with coresets on edge devices, which consists of two main phases: In the coreset selection phase, QuaRC introduces the ``Relative Entropy Score" to identify the subsets that most effectively capture the model's quantization errors. During the training phase, QuaRC employs the Cascaded Layer Correction strategy to align the intermediate layer outputs of the quantized model with those of the full-precision model, thereby effectively reducing the quantization errors in the intermediate layers. Experimental results demonstrate the effectiveness of our approach. For instance, when quantizing ResNet-18 to 2-bit using a 1% data subset, QuaRC achieves a 5.72% improvement in Top-1 accuracy on the ImageNet-1K dataset compared to state-of-the-art techniques.
【20】Incentivised Orchestrated Training Architecture (IOTA): A Technical Primer for Release
标题：受激励的激励训练架构（IOTA）：发布的技术入门
链接：https://arxiv.org/abs/2507.17766
摘要：2024年8月，Bittensor的Subnet 9（SN 9）证明，一个由激励的、无需许可的参与者组成的分布式网络可以预训练7亿到140亿个参数的大型语言模型（LLM），同时超过了既定的基线。虽然这项工作验证了基于区块链的去中心化预训练是可行的，但它包含了核心问题：（i）每个矿工都必须在本地适应整个模型，（ii）“赢家通吃”的奖励鼓励了模型囤积。 在这里，我们介绍了IOTA（激励式分级培训架构），该架构通过将SN 9之前孤立的竞争对手转变为一个可以任意扩展的单一合作单元，同时仍然公平地奖励每个贡献者，从而解决了这些限制。 主要初步结果：（1）数据和流水线并行SWARM架构-协调器将模型层分布在异构矿工之间，并在它们之间传输激活，使模型大小能够随着参与者的数量而扩展，而不是受到单个机器的VRAM的限制;（2）颗粒状，连续的激励-验证器测量每个矿工的贡献并按比例分配令牌排放;（3）激活压缩-我们使用模型瓶颈将激活的通信带宽减少了128倍，大大提高了训练速度;（4）Butterfly All-Reduce - Miners在O（1）带宽内平均不相交的参数切片，提供线性可扩展性，冗余和内置的共谋检测;（5）CLASP（通过路径采样进行贡献损失评估）-公平的归因方案将与矿工的边际效用成比例的信用分配给矿工，并检测漏洞，即使贡献在整个管道中相互依赖。
摘要：In August 2024, Bittensor's Subnet 9 (SN9) demonstrated that a distributed network of incentivized, permissionless actors could each pretrain large language models (LLMs) ranging from 700 million to 14 billion parameters, while surpassing established baselines. While that work validated blockchain-based decentralized pretraining as viable, it contained core issues: (i) every miner had to fit an entire model locally, and (ii) "winner-takes-all" rewards encouraged model hoarding. Here we introduce IOTA (Incentivized Orchestrated Training Architecture), an architecture that addresses these limitations by transforming SN9's previously isolated competitors into a single cooperating unit that can scale arbitrarily while still rewarding each contributor fairly. Key preliminary results: (1) Data- and Pipeline-parallel SWARM architecture - An orchestrator distributes model layers across heterogeneous miners and streams activations between them, enabling model sizes to scale with the number of participants rather than being constrained by the VRAM of a single machine; (2) Granular, continuous incentives - Validators measure each miner's contribution and allocate token emissions proportionally; (3) Activation compression - We used model-bottlenecks to cut communication bandwidths of activations by up to 128x, vastly improving training speed; (4) Butterfly All-Reduce - Miners average disjoint parameter slices in O(1) bandwidth, offering linear scalability, redundancy and built-in collusion detection; (5) CLASP (Contribution Loss Assessment via Sampling of Pathways) - A fair attribution scheme assigns credit to miners proportional to their marginal utility and detects exploits, even when contributions are interdependent across the pipeline.
【21】Mapping Technological Futures: Anticipatory Discourse Through Text Mining
标题：绘制技术未来：通过文本挖掘的预期性话语
链接：https://arxiv.org/abs/2504.02853
备注：Accepted to Humanities and Social Sciences Communications. arXiv admin note: text overlap with arXiv:2407.17522
摘要：人工智能（AI）等新兴技术的波动性和不可预测性产生了巨大的不确定性，这在社交媒体上被广泛讨论。这项研究通过分析在X平台上发布的400位关键意见领袖（KOL）的150万篇帖子（从2021年到2023年），研究了围绕技术未来的预期话语。使用先进的文本挖掘技术，包括BERTopic建模，情感，情绪和态度分析，该研究确定了100个不同的主题，反映了预期的技术驱动的未来。我们的研究结果强调了KOL在构建“现在的未来”（对人工智能和物联网等变革性技术的乐观愿景）和影响“未来的未来”（这些预测塑造了当代社会和地缘政治辩论）方面的双重作用。希望等积极情绪占主导地位，超过焦虑，特别是在“机器学习，数据科学和深度学习”等主题中，而围绕“气候变化”和“战争，乌克兰和特朗普人”的讨论引发了焦虑。通过将技术框架作为社会挑战的解决方案，KOL充当社会叙事的媒介，在想象的未来和当前现实之间架起桥梁。这些见解强调了它们在高度不确定性时期引导公众关注新兴技术方面的关键作用，促进了我们对技术介导背景下预期话语的理解。
摘要：The volatility and unpredictability of emerging technologies, such as artificial intelligence (AI), generate significant uncertainty, which is widely discussed on social media. This study examines anticipatory discourse surrounding technological futures by analysing 1.5 million posts from 400 key opinion leaders (KOLs) published on the X platform (from 2021 to 2023). Using advanced text mining techniques, including BERTopic modelling, sentiment, emotion, and attitude analyses, the research identifies 100 distinct topics reflecting anticipated tech-driven futures. Our findings emphasize the dual role of KOLs in framing \textit{present futures} -- optimistic visions of transformative technologies like AI and IoT -- and influencing \textit{future presents}, where these projections shape contemporary societal and geopolitical debates. Positive emotions such as Hope dominate, outweighing Anxiety, particularly in topics like ``Machine Learning, Data Science, and Deep Learning,'' while discussions around ``Climate Change'' and ``War, Ukraine, and Trump People'' elicit \textit{Anxiety}. By framing technologies as solutions to societal challenges, KOLs act as mediators of societal narratives, bridging imagined futures and current realities. These insights underscore their pivotal role in directing public attention with emerging technologies during periods of heightened uncertainty, advancing our understanding of anticipatory discourse in technology-mediated contexts.
【22】Deep Variational Free Energy Calculation of Hydrogen Hugoniot
标题：氢Hugoniot的深度变分自由能计算
链接：https://arxiv.org/abs/2507.18540
备注：7+17 pages, 5+14 figures, for source code and raw data, see this https URL
摘要：我们发展了一个深层变分自由能框架来计算热密物质区氢的状态方程。该方法使用三种深度生成模型参数化有限温度下氢核和电子的变分密度矩阵：表示经典核的Boltzmann分布的归一化流模型，模拟激发态电子分布的自回归Transformer，以及构造Hartree-Fock轨道中电子回流坐标的置换等变流模型。通过对三个神经网络进行联合优化，以最小化变分自由能为目标，得到了稠密氢的物态方程和相关热力学性质。我们比较我们的结果与其他理论和实验结果的氘Hugoniot曲线，旨在解决现有的差异。计算结果提供了一个有价值的基准在温暖的稠密物质区的氘。
摘要：We develop a deep variational free energy framework to compute the equation of state of hydrogen in the warm dense matter region. This method parameterizes the variational density matrix of hydrogen nuclei and electrons at finite temperature using three deep generative models: a normalizing flow model that represents the Boltzmann distribution of the classical nuclei, an autoregressive transformer that models the distribution of electrons in excited states, and a permutational equivariant flow model that constructs backflow coordinates for electrons in Hartree-Fock orbitals. By jointly optimizing the three neural networks to minimize the variational free energy, we obtain the equation of state and related thermodynamic properties of dense hydrogen. We compare our results with other theoretical and experimental results on the deuterium Hugoniot curve, aiming to resolve existing discrepancies. The calculated results provide a valuable benchmark for deuterium in the warm dense matter region.
【23】Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise
标题：多维异方差噪音下的欧几里得距离缩窄
链接：https://arxiv.org/abs/2507.18520
摘要：成对欧氏距离计算是许多机器学习和数据分析算法中的基本步骤。然而，在现实世界的应用中，这些距离经常被异方差噪声扭曲，这是一种普遍存在的非均匀腐败形式，其特征在于数据观测中的可变噪声幅度。这种噪声以一种非平凡的方式膨胀了计算的距离，导致底层数据几何的错误表示。在这项工作中，我们解决的任务，估计每个观察的噪声幅度和校正下的异方差噪声的成对欧几里德距离。也许令人惊讶的是，我们表明，在一般的高维设置中，并且在不假设有关干净数据结构或噪音分布的先验知识的情况下，即使噪音水平变化很大，这两项任务也可以可靠地执行。具体来说，我们开发了一种原则性的，无超参数的方法，联合估计噪声幅度和校正的距离。我们为我们的方法提供理论保证，建立噪声幅度和距离的估计误差的概率界。这些界限，在规范化的$\ell_1$范数测量，收敛到零的多项式率的特征维数和数据集大小的增加。在合成数据集上的实验表明，我们的方法在具有挑战性的制度中准确地估计距离，显着提高了后续基于距离的计算的鲁棒性。值得注意的是，当应用于单细胞RNA测序数据时，我们的方法产生与已建立的原型模型一致的噪声幅度估计，从而实现准确的最近邻识别，这对于许多下游分析至关重要。
摘要：Pairwise Euclidean distance calculation is a fundamental step in many machine learning and data analysis algorithms. In real-world applications, however, these distances are frequently distorted by heteroskedastic noise$\unicode{x2014}$a prevalent form of inhomogeneous corruption characterized by variable noise magnitudes across data observations. Such noise inflates the computed distances in a nontrivial way, leading to misrepresentations of the underlying data geometry. In this work, we address the tasks of estimating the noise magnitudes per observation and correcting the pairwise Euclidean distances under heteroskedastic noise. Perhaps surprisingly, we show that in general high-dimensional settings and without assuming prior knowledge on the clean data structure or noise distribution, both tasks can be performed reliably, even when the noise levels vary considerably. Specifically, we develop a principled, hyperparameter-free approach that jointly estimates the noise magnitudes and corrects the distances. We provide theoretical guarantees for our approach, establishing probabilistic bounds on the estimation errors of both noise magnitudes and distances. These bounds, measured in the normalized $\ell_1$ norm, converge to zero at polynomial rates as both feature dimension and dataset size increase. Experiments on synthetic datasets demonstrate that our method accurately estimates distances in challenging regimes, significantly improving the robustness of subsequent distance-based computations. Notably, when applied to single-cell RNA sequencing data, our method yields noise magnitude estimates consistent with an established prototypical model, enabling accurate nearest neighbor identification that is fundamental to many downstream analyses.
【24】DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts
标题：DriftMoE：处理概念漂移的专家混合方法
链接：https://arxiv.org/abs/2507.18464
备注：Accepted at the SYNDAiTE@ECMLPKDD 2025 workshop
摘要：从受概念漂移影响的非平稳数据流中学习，需要模型能够在保持资源效率的同时进行动态调整。现有的自适应集成方法通常依赖于粗粒度的自适应机制或简单的投票方案，无法最佳地利用专业知识。本文介绍了漂移MoE，一个在线的混合专家（MoE）架构，通过一个新的协同训练框架，解决了这些限制。DriftMoE的特点是一个紧凑的神经路由器，它与一个增量Hoeffding树专家池共同训练。关键的创新在于一个共生学习循环，使专家专业化：路由器选择最合适的专家进行预测，相关专家使用真实标签进行增量更新，路由器使用多热正确性掩码来改进其参数，以加强每个准确的专家。这种反馈回路为路由器提供了清晰的训练信号，同时加速了专家的专业化。我们评估了DriftMoE在九个最先进的数据流学习基准测试中的性能，这些基准测试跨越了突然的、渐进的和真实世界的漂移，测试了两种不同的配置：一种是专家专注于数据体系（多类变体），另一种是他们专注于单类专业化（基于任务的变体）。我们的研究结果表明，DriftMoE实现了具有竞争力的结果与国家的最先进的流学习自适应合奏，提供了一个原则和有效的方法来适应概念漂移。所有代码、数据管道和可重复性脚本都可以在我们的公共GitHub存储库中找到：https://github.com/miguel-ceadar/drift-moe。
摘要：Learning from non-stationary data streams subject to concept drift requires models that can adapt on-the-fly while remaining resource-efficient. Existing adaptive ensemble methods often rely on coarse-grained adaptation mechanisms or simple voting schemes that fail to optimally leverage specialized knowledge. This paper introduces DriftMoE, an online Mixture-of-Experts (MoE) architecture that addresses these limitations through a novel co-training framework. DriftMoE features a compact neural router that is co-trained alongside a pool of incremental Hoeffding tree experts. The key innovation lies in a symbiotic learning loop that enables expert specialization: the router selects the most suitable expert for prediction, the relevant experts update incrementally with the true label, and the router refines its parameters using a multi-hot correctness mask that reinforces every accurate expert. This feedback loop provides the router with a clear training signal while accelerating expert specialization. We evaluate DriftMoE's performance across nine state-of-the-art data stream learning benchmarks spanning abrupt, gradual, and real-world drifts testing two distinct configurations: one where experts specialize on data regimes (multi-class variant), and another where they focus on single-class specialization (task-based variant). Our results demonstrate that DriftMoE achieves competitive results with state-of-the-art stream learning adaptive ensembles, offering a principled and efficient approach to concept drift adaptation. All code, data pipelines, and reproducibility scripts are available in our public GitHub repository: https://github.com/miguel-ceadar/drift-moe.
【25】A Two-armed Bandit Framework for A/B Testing
标题：A/B测试的双臂强盗框架
链接：https://arxiv.org/abs/2507.18118
摘要：A/B测试广泛应用于现代技术公司的策略评估和产品部署，目的是将新开发的策略与标准控制下的结果进行比较。在文献中开发的各种因果推理和强化学习方法适用于A/B测试。本文介绍了一个双臂强盗框架，旨在提高现有的方法的权力。所提出的方法包括三个主要步骤：（i）采用双重鲁棒估计产生伪结果，（ii）利用双臂强盗框架来构造检验统计量，以及（iii）应用基于置换的方法来计算$p$-值。我们通过渐近理论、数值实验和来自拼车公司的真实数据证明了所提出的方法的有效性，与现有方法相比显示出其优越的性能。
摘要：A/B testing is widely used in modern technology companies for policy evaluation and product deployment, with the goal of comparing the outcomes under a newly-developed policy against a standard control. Various causal inference and reinforcement learning methods developed in the literature are applicable to A/B testing. This paper introduces a two-armed bandit framework designed to improve the power of existing approaches. The proposed procedure consists of three main steps: (i) employing doubly robust estimation to generate pseudo-outcomes, (ii) utilizing a two-armed bandit framework to construct the test statistic, and (iii) applying a permutation-based method to compute the $p$-value. We demonstrate the efficacy of the proposed method through asymptotic theories, numerical experiments and real-world data from a ridesharing company, showing its superior performance in comparison to existing methods.
【26】Zeroth-order log-concave sampling
标题：零阶log-凹抽样
链接：https://arxiv.org/abs/2507.18021
备注：30 pages
摘要：We study the zeroth-order query complexity of log-concave sampling, specifically uniform sampling from convex bodies using membership oracles. We propose a simple variant of the proximal sampler that achieves the query complexity with matched R\'enyi orders between the initial warmness and output guarantee. Specifically, for any $\varepsilon>0$ and $q\geq2$, the sampler, initialized at $\pi_{0}$, outputs a sample whose law is $\varepsilon$-close in $q$-R\'enyi divergence to $\pi$, the uniform distribution over a convex body in $\mathbb{R}^{d}$, using $\widetilde{O}(qM_{q}^{q/(q-1)}d^{2}\,\lVert\operatorname{cov}\pi\rVert\log\frac{1}{\varepsilon})$ membership queries, where $M_{q}=\lVert\text{d}\pi_{0}/\text{d}\pi\rVert_{L^{q}(\pi)}$. We further introduce a simple annealing scheme that produces a warm start in $q$-R\'enyi divergence (i.e., $M_{q}=O(1)$) using $\widetilde{O}(qd^{2}R^{3/2}\,\lVert\operatorname{cov}\pi\rVert^{1/4})$ queries, where $R^{2}=\mathbb{E}_{\pi}[|\cdot|^{2}]$. This interpolates between known complexities for warm-start generation in total variation and R\'enyi-infinity divergence. To relay a R\'enyi warmness across the annealing scheme, we establish hypercontractivity under simultaneous heat flow and translate it into an improved mixing guarantee for the proximal sampler under a logarithmic Sobolev inequality. These results extend naturally to general log-concave distributions accessible via evaluation oracles, incurring additional quadratic queries.
摘要：We study the zeroth-order query complexity of log-concave sampling, specifically uniform sampling from convex bodies using membership oracles. We propose a simple variant of the proximal sampler that achieves the query complexity with matched R\'enyi orders between the initial warmness and output guarantee. Specifically, for any $\varepsilon>0$ and $q\geq2$, the sampler, initialized at $\pi_{0}$, outputs a sample whose law is $\varepsilon$-close in $q$-R\'enyi divergence to $\pi$, the uniform distribution over a convex body in $\mathbb{R}^{d}$, using $\widetilde{O}(qM_{q}^{q/(q-1)}d^{2}\,\lVert\operatorname{cov}\pi\rVert\log\frac{1}{\varepsilon})$ membership queries, where $M_{q}=\lVert\text{d}\pi_{0}/\text{d}\pi\rVert_{L^{q}(\pi)}$. We further introduce a simple annealing scheme that produces a warm start in $q$-R\'enyi divergence (i.e., $M_{q}=O(1)$) using $\widetilde{O}(qd^{2}R^{3/2}\,\lVert\operatorname{cov}\pi\rVert^{1/4})$ queries, where $R^{2}=\mathbb{E}_{\pi}[|\cdot|^{2}]$. This interpolates between known complexities for warm-start generation in total variation and R\'enyi-infinity divergence. To relay a R\'enyi warmness across the annealing scheme, we establish hypercontractivity under simultaneous heat flow and translate it into an improved mixing guarantee for the proximal sampler under a logarithmic Sobolev inequality. These results extend naturally to general log-concave distributions accessible via evaluation oracles, incurring additional quadratic queries.
【27】On the Energy Distribution of the Galactic Center Excess' Sources
标题：银河系中心过剩源的能量分布
链接：https://arxiv.org/abs/2507.17804
备注：7+20 pages, 2+20 figures, comments welcome
摘要：银河系中心过剩（GCE）仍然是费米伽马射线太空望远镜发现的一个决定性的谜团。尽管这可能预示着暗物质湮灭的发现，但与这一结论相反的是，分析显示，发射的空间结构似乎与暗淡点源的数量更一致。技术上的限制限制了以前的分析，研究纯粹的空间点源假说。所有有助于将GCE从复杂和不确定的天体物理发射中解脱出来的光谱信息都被丢弃了。我们证明，神经网络辅助的基于模拟的推理方法可以克服这些限制，从而面对点源解释的GCE与空间和光谱数据。这一补充意义深远：能量信息使得假定的点源明显变暗，这表明GCE在本质上是真正的漫射，或者是由异常大量的源构成的。从数量上看，对于我们最适合的背景模型，过量基本上与暗物质预测的泊松发射一致。如果过量是由于点源，我们的预测中值是银河系中心的${\cal O}（10^5）$源，或者在90%的置信度下超过35，000个源，两者都明显大于GCE早期点源分析所首选的数百个源。
摘要：The Galactic Center Excess (GCE) remains one of the defining mysteries uncovered by the Fermi $\gamma$-ray Space Telescope. Although it may yet herald the discovery of annihilating dark matter, weighing against that conclusion are analyses showing the spatial structure of the emission appears more consistent with a population of dim point sources. Technical limitations have restricted prior analyses to studying the point-source hypothesis purely spatially. All spectral information that could help disentangle the GCE from the complex and uncertain astrophysical emission was discarded. We demonstrate that a neural network-aided simulation-based inference approach can overcome such limitations and thereby confront the point source explanation of the GCE with spatial and spectral data. The addition is profound: energy information drives the putative point sources to be significantly dimmer, indicating either the GCE is truly diffuse in nature or made of an exceptionally large number of sources. Quantitatively, for our best fit background model, the excess is essentially consistent with Poisson emission as predicted by dark matter. If the excess is instead due to point sources, our median prediction is ${\cal O}(10^5)$ sources in the Galactic Center, or more than 35,000 sources at 90% confidence, both significantly larger than the hundreds of sources preferred by earlier point-source analyses of the GCE.
【28】ASR-Guided Speaker-Role Diarization and Diarization-Guided ASR Decoding
标题：ASB引导的扬声器角色分区和分区引导的ASB解码
链接：https://arxiv.org/abs/2507.17765
备注：Interspeech 2025 Submission
摘要：从应用的角度来看，说话者角色日记（RD），如医生与患者，主人与客人等，通常比传统的说话者日记（SD）更有用，后者分配通用标签，如说话者-1，说话者-2等。在联合自动语音识别（ASR）+ SD（谁说了什么？）的上下文中，最近的端到端模型采用与ASR换能器同步的辅助SD换能器来预测每个单词的说话者。在本文中，我们将这一框架扩展到RD，并做出了三个关键贡献：（1）我们通过强制对齐和交叉熵损失而不是RNNT损失来简化训练，（2）我们表明单词预测和角色预测需要不同数量的预测器上下文，导致单独的任务特定预测器，与现有的共享预测器模型不同，以及（3）我们提出了一种利用RD后验活动来影响ASR解码并减少小词删除错误的方法。
摘要：From an application standpoint, speaker-role diarization (RD), such as doctor vs. patient, host vs. guest, etc. is often more useful than traditional speaker diarization (SD), which assigns generic labels like speaker-1, speaker-2 etc. In the context of joint automatic speech recognition (ASR) + SD (who spoke what?), recent end-to-end models employ an auxiliary SD transducer, synchronized with the ASR transducer, to predict speakers per word. In this paper, we extend this framework to RD with three key contributions: (1) we simplify the training via forced alignment and cross-entropy loss instead of RNNT loss, (2) we show that word prediction and role prediction require different amounts of predictor's context, leading to separate task-specific predictors, unlike existing shared-predictor models, and (3) we propose a way to leverage RD posterior activity to influence ASR decoding and reduce small-word deletion errors.
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