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10月12日 周一
arXiv cs.LG71

OpenCollab: A Multi-Agent Coding Framework with Programmable Collaboration and Controllable Runtime

arXiv:2609.38345v2 Announce Type: replace-cross Abstract: Multi-agent coding systems are designed to tackle complex software engineering tasks through collaboration. However, existing evaluations typically assume configured organizations are followed faithfully, whereas reality differs. This behavioral gap, combined with differences in underlying system components, prevents clear attribution of observed gains. To this end, we introduce OpenCollab, a multi-agent coding framework that provides a unified infrastructure for programmable collaboration and controllable runtime. Specifically, OpenCollab unifies organization design, enforces experimental control on a shared runtime, and tracks execution through fine-grained event streams. On this basis, we define Adherence to quantify whether the declared organization is actually realized. Our experiments reveal that agents collaborate very differently across configurations: changing any single dimension shifts Adherence, from 47.2% to as high as 97.2%. Furthermore, extensive agentic coding benchmarks show that OpenCollab (Duo) establishes new SOTA performance compared to the mainstream harnesses such as Mini-SWE-agent, OpenHands, Codex CLI, and Claude Code, showing that a well-designed organization can outperform strong existing harnesses, while OpenCollab's single-agent configuration uses the fewest tokens across all evaluated suites. OpenCollab establishes a unified multi-agent infrastructure for easy programmable collaboration and controlled causal evaluation.

arXiv cs.LG55

Robust LassoNet: Enhancing Feature Selection in Neural Networks via Robust Loss Functions

arXiv:2609.38263v2 Announce Type: replace-cross Abstract: Feature selection in neural networks remains a challenging problem, particularly in the presence of noisy or contaminated data. LassoNet is a recent approach that addresses this issue by combining neural networks with hierarchical sparsity constraints, enabling simultaneous prediction and variable selection. However, its standard formulation relies on the mean squared error (MSE) loss, which is known to be highly sensitive to outliers. In this paper, we present Robust LassoNet, an extension of LassoNet that incorporates robust loss functions, such as Huber, Cauchy, Tukey's bisquare, and Nonnegative Garrote, to mitigate the effect of extreme observations. The proposed approach preserves the original optimization framework while improving stability under data contamination. Through experiments on synthetic and real datasets, we show that robust LassoNet significantly improves both predictive performance and feature selection accuracy in the presence of outliers or heavy tailed noise, while maintaining comparable performance in clean settings. These results highlight the importance of robustness in neural network based feature selection and suggest practical guidelines for choosing appropriate loss functions.

arXiv cs.LG64

ThinkNet: Evolutionary Deep Ensembles for Efficient Motor-Imagery EEG Classification

arXiv:2609.33967v2 Announce Type: replace-cross Abstract: Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to new users under limited target-user data and constrained compute. However, held-out-subject performance can be overstated when test-subject information influences preprocessing, model selection, or ensemble selection. We present \textit{ThinkNet}, a validation-controlled framework that combines train-only normalization, validation-guided evolutionary search, and validation-gated inference to identify compact decoders and inference policies for held-out subjects. We evaluate four-class BCI Competition IV-2a (session T) decoding with nine Leave-One-Subject-Out (LOSO) folds, three seeds, seven fixed decoder entries, and a broader search over ten representative decoder families; the held-out subject is never used for normalization, hyperparameter, architecture, or ensemble-policy selection. In the fixed benchmark, the validation-selected compact decoder achieved 44.35$\pm$15.41\% accuracy with 4.9K parameters, 19 KB FP32 weights, and 0.99 ms batch-1 Orin CUDA inference. Across the broader search, compact models ($\leq$25K parameters) achieved higher mean held-out accuracy than mid-size and large alternatives after selected retraining (40.10\% vs. 35.09\% and 34.78\%). Validation-gated ensembling improved over validation-selected single-model inference, reaching 43.98$\pm$16.25\% in the fixed benchmark and 43.31$\pm$15.88\% for the compact six-family ensemble. A non-deployable oracle analysis revealed a 6.1-point family-selection gap and near-zero validation--test correlation, showing that validation reliability remains a key bottleneck under subject shift. Thus, ThinkNet is a validation-controlled framework for compact MI-EEG model and inference-policy selection, rather than a single-architecture benchmark.

arXiv cs.LG73

信任，但要验证测量工具：在人工编写的安全回归代理测试上审计 AI 生成的 RTL 验证计划

arXiv:2609.19844v2 公告类型：replace-cross 摘要：AI 生成的 RTL 验证计划可能满足服务提供方的模式要求，却在进入可信执行环节的边界处失效。我们提出 SecTB-RTL，这是一个可审计的框架，涵盖 31 项任务和 124 个人工编写的硬件安全回归测试。在逐步提高资源限制的条件下，确定性的非 AI 基线分别杀死了 36、75 和 78 个变异体。首次确认性运行（C1-R2）在模型执行前便告失败，原因是服务提供方拒绝了其响应模式。在未查看结果的情况下仅修复模式后，一次单独冻结配置的后续运行（C1-R3）完成了 1,860 次调用。服务提供方接受了 1,857 个响应，但只有 9 个通过了生产环境语义验证器。生成规则与执行规则并不匹配。因此，我们将此次运行保留为一起测量工具验证事件，不报告任何提示词效应估计。该事件表明，服务提供方接受响应或响应符合模式，并不能证明其执行有效性。编译和覆盖率仅是诊断手段；实际保存的产物必须通过完整的生产执行流程。此后的一次跟进运行因未满足预注册的证据完整性门槛而被排除，仅作为未来工作处理。我们发布了该基准、保留失败信息的契约、事件溯源记录，以及防止将基础设施行为误报为模型行为所需的治理控制措施。

arXiv cs.LG78

用于循证孤独症谱系障碍筛查的多模态大语言模型

arXiv:2609.16464v2 公告类型：replace-cross 摘要：孤独症谱系障碍（ASD）的临床管理在早期筛查方面面临瓶颈，主要原因是受过培训的专科人员稀缺，且传统评估工具具有主观性。在此，我们提出 ASDchat，这是一种专为循证 ASD 筛查设计的多模态大语言模型，以视频、音频和对话作为输入。ASDchat 采用双分支架构，其中决策分支生成筛查概率，证据分支生成可追溯、带时间戳且与孤独症诊断观察量表（ADOS-2）相对应的行为证据。该模型在来自中国 27 个研究中心、包含 1,035 名参与者的数据集上进行了训练和评估，参与者涵盖典型发育（TD）儿童、ASD 儿童及患有其他发育障碍的儿童。在区分 ASD 与 TD 时，ASDchat 的受试者工作特征曲线下面积（AUC）达到 0.953 $\pm$ 0.021。在未用于训练的 6 个留出研究中心上，平均 AUC 为 0.961。此外，对行为维度进行无监督聚类，将 ASD 病例划分为六种具有不同表型特征的亚型，ASDchat 为每种亚型提出相应的干预建议。ASDchat 为临床实践中开展大规模循证 ASD 筛查提供了一条可行路径。

arXiv cs.LG56

Predicting Social Media Engagement using Machine Learning

arXiv:2609.16082v2 Announce Type: replace-cross Abstract: Social media platforms are popular channels for disseminating information, owing to their large user bases and ease of access. Companies also use social media as an important aspect of the advertising process. By creating high-quality posts, companies can strengthen their engagement metrics and increase their follower count. While a growing body of research has examined social media engagement, fewer studies have jointly examined the visual, textual, and temporal features of image posts, even though these features collectively determine the performance of content on social media. To understand the important drivers of social media engagement, we collect image posts of furniture firms on Facebook and extract visual, temporal, and textual features from them using text and image analytics methods. We evaluate several machine learning models--including Random Forest, Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost)--to assess the drivers and the prediction power of social media engagement using the features from our data. Our primary model, LightGBM, explains 81.8% of the variance in the engagement score, 2.3 times that of our baseline model. We observed that brand-relative deviation features, which capture how far a post departs from its brand's norms, are the strongest predictors, accounting for 51.3% of total importance. Our research quantifies the extent to which these features are associated with interactions and provides recommendations that organizations may consider.

arXiv cs.LG77

通过正交化动量进行非光滑优化

arXiv:2609.13677v2 公告类型：replace-cross 摘要：现代实际应用问题涉及矩阵值参数，但传统优化器将其视为向量，这促使人们开发利用输入—输出几何结构的矩阵感知方法，例如在参数更新前对动量矩阵进行正交化的 Muon。它在实践中的成功引出了一个概念性问题：正交化动量在非光滑优化中能否依然有效？本文使用与反向传播兼容的广义导数框架，针对局部 Lipschitz 函数研究这一问题。我们的第一项贡献是揭示了一个关键局限：对于任意固定的动量因子 $\beta\in[0,1)$，当步长根据完整的梯度历史自适应调整时，Muon 从几乎所有初始化出发都可能无法趋近某个凸 Lipschitz 目标函数的全局最优解。即使迭代序列有界，也可能出现这种失败。我们的例子受到 Parshakova 等人的例子的启发，而后者仅涵盖 $\beta\in[0,\frac{1}{2})$。随后，我们证明，障碍在于固定动量，而非正交化。事实上，当动量因子自适应调整并趋近于 1，同时步长趋于零时，在有界性和正则性条件下，Muon 可恢复在非凸非光滑优化中的渐近收敛性。此外，我们提出了 MAGD，它将正交化动量与梯度相结合，并根据二者的相对进展进行加权。MAGD 在非凸情形下保持渐近收敛性，并在凸情形下达到 $O(\min\{m,n\}\epsilon^{-2})$ 的收敛速率。一个下界证明了其维度依赖关系的最优性。在合成问题、图像分类和 LLM 预训练上的实验表明，MAGD 是一种简单实用的 Muon 替代方案。总体而言，我们的结果刻画了缺乏光滑性时正交化动量何时会失效，以及如何使其变得可靠；我们希望这些分析能在更广泛的情形下发挥作用。

arXiv cs.LG79

是什么使对抗样本能够跨深度伪造检测器迁移？

arXiv:2609.10002v2 公告类型：替换并交叉收录 摘要：深度伪造检测器仍然容易受到基于迁移的黑盒攻击。在此类攻击中，对抗样本在源替代模型上生成，并迁移至攻击者未知的目标模型。然而，源模型与目标模型之间的兼容性如何影响攻击成功率，目前仍缺乏深入理解。既有研究评估的检测器范围有限，且很少区分架构因素与训练因素的影响。我们对涵盖六种骨干网络、两种预训练方案和五种训练数据配置的60个检测器开展了对抗迁移性的受控评估，使用两种攻击方法：AutoAttack（AA）以及结合变换期望的 Carlini--Wagner 攻击（CW--EOT）。匹配比较表明，当源模型与目标模型具有完全相同的骨干网络、相同的架构家族、预训练方案或训练数据时，迁移性显著提高。这种兼容性结构取决于攻击方法：在 AA 下，骨干网络完全一致的影响最大；而在 CW--EOT 下，共享预训练方案和训练数据的影响最大。对非目标源模型的迁移结果取平均时，AA 下的平均攻击成功率（ASR）为 $7.21\%$，CW--EOT 下为 $19.52\%$。相比之下，在排除骨干网络完全一致和训练数据匹配的情况后，结合两种攻击的多源预言机仍达到 \(64.48\%\) 的平均 ASR，表明对源模型取平均可能会大幅低估目标模型的脆弱性。我们公开了240,000张经对抗扰动的图像、完整的两两迁移结果、检测器配置和评估代码。这些发现确立了源模型与目标模型的兼容性以及源模型选择在可信的基于迁移的黑盒鲁棒性评估中的核心地位。

arXiv cs.LG76

基于实验数据学习与优化化工传递过程的可微混合建模

arXiv:2609.04011v2 公告类型：replace-cross 摘要：传递模型是化工过程设计与优化的基础，但其本构定律通常依靠人工选定，实验初始条件被假定为精确值，而计算成本又使优化仅限于少数过程变量。本文提出一种可微混合建模框架，将神经网络嵌入 GPU 加速的群体平衡方程求解器，直接从结晶实验数据中发现本构定律并同化初始条件。利用十三组包含多维尺寸与形状测量结果的真实实验，我们表明，将测得的初始条件视为精确值，尽管能提高预测精度，却会得到物理上不一致的速率定律；而对初始条件进行同化，则能恢复物理上一致的非线性定律，并将轨迹误差降至传统建模方法的七分之一。端到端可微性还支持对高维操作曲线进行基于梯度的优化，找到了一条由 300 个变量描述的温度曲线，在质量不变的条件下将晶体长宽比降低了 25%，从而改善产品属性。我们的框架是对机理建模的增强，而非替代，为从数据中学习传递模型并利用这些模型优化过程提供了一条通用路径。

arXiv cs.LG76

用于策略优化的偏好获取及其在使心脏移植符合人类价值观中的应用

arXiv:2608.28620v2 公告类型：replace-cross 摘要：偏好获取对于使 AI 系统符合人类价值观至关重要。以往的方法（例如用于器官分配的方法）通常要求利益相关者比较算法的决策（例如选择患者 A 还是患者 B）。这种决策层面的方法混淆了手段与目的。与之不同，我们直接获取对分配结果的偏好，以学习用于策略优化的效用函数。我们构建了一种针对线性效用的新型偏好获取算法，其实际表现优于以往技术。我们的算法分为两个阶段。第一阶段通过成对比较学习切割平面，以快速缩小可能的属性权重空间，并通过排除被支配区域为第二阶段提供热启动。随后，第二阶段以可证明的方式收敛到用户的效用函数。我们将该技术应用于心脏移植分配，这一场景中的策略必须平衡移植后结局、等待名单死亡率、地理便利性和公平性等相互竞争的目标。我们使用该算法开展了一项用户研究，以学习并聚合出符合人类价值观的效用函数。我们将这种线性表示与基于 LLM 的方法进行比较，后者获得了用户提供的自然语言解释，结果发现，这些方法均未比学习得到的线性效用更准确。我们使用得到的效用函数优化心脏移植策略，使其显著更符合人类价值观。与事后最优方案相比，现行策略的竞争比仅为 0.66，而我们的方法以 0.95 的竞争比接近最优。

arXiv cs.LG77

DRIFT：通过对抗性贴片攻击使流匹配 VLA 的去噪轨迹偏离

arXiv:2608.03207v2 公告类型：replace-cross 摘要：pi0 等流匹配视觉-语言-动作（VLA）模型通过对学习得到的去噪速度场进行积分来生成机器人动作，据报道，这类模型能够抵御那些轻易欺骗自回归 VLA 的对抗性扰动。我们表明，这种鲁棒性在很大程度上只是假象：它源于以往的攻击忽略了多步去噪常微分方程（ODE）。我们提出 DRIFT（Denoising Redirection via Input perturbation of the Flow-matching Trajectory），这是一种在测试时放置于机器人夹爪上的通用对抗性贴片，用于攻击现成策略的去噪速度场。我们的核心发现有悖直觉：仅攻击第一个去噪步骤，比攻击更大范围的多个步骤效果更强、成本更低。我们通过输入空间优化所特有的梯度冲突来解释这一现象，而这一现象与训练时的后门攻击情形恰好相反。在四个 LIBERO 测试套件中针对 pi0 和 pi0.5 开展的实验表明，DRIFT 仅用一张小贴片，就能使几乎所有原本可完成的任务失败，其效果远超动作空间和嵌入空间中的攻击基线。

arXiv cs.LG79

基于粒子的广义随机优化

arXiv:2608.02844v2 公告类型：replace-cross 摘要：我们针对梯度难以计算的损失函数，提出了一类基于扩散的通用随机粒子优化方法。具体而言，我们考虑损失梯度可表示为关于参数依赖分布的积分的问题，这类结构涵盖生成模型训练、微调以及潜变量模型学习。我们引入平均场动力学及其相互作用粒子近似，其中包含若干现有算法作为特例，并为构建新方法提供了途径。在适定性和联合收缩性假设下，我们证明了指数收敛性，并表明连续时间粒子系统具有非渐近误差界。我们通过开发高阶 Langevin 优化器和动量优化器，并在最大边际似然估计和基于能量的模型训练任务上对其进行评估，展示了这一框架的应用。

arXiv cs.LG75

MIRROR：从另一视角学习以实现多模态推理

arXiv:2607.21552v2 公告类型：replace-cross 摘要：对于同一个底层问题，采用文本形式还是视觉形式呈现，可能导致视觉语言模型（VLMs）的表现截然不同。这种不一致性表明，不同视角会显露出不同的推理路径和失效模式，而标准的多模态后训练尚未充分利用这些差异。为研究并利用这一现象，我们构建了 ODA-Data，这是一个高质量的配对多模态几何数据集，针对相同问题提供文本主导、图像主导以及图像+文本组合视角，并划分了用于训练和评估视角依赖推理行为的子集。随后，我们提出 Modality-Informed Reciprocal Reasoning Optimization（MIRROR），这是一种强化学习方法，可从模型自身依赖于模态的能力中提取跨视角自蒸馏信号。对于每个问题，MIRROR 会评估模型在所有视角下的表现，选择表现最佳的视角作为教师，并使用 GRPO 任务奖励目标来训练其余视角，同时引入以该教师为目标的反向 KL 正则项。在评估多模态数学问题的各项推理基准上，MIRROR 优于标准强化学习，并使模型在不同输入模态表示下的表现更加准确、一致。

arXiv cs.LG85

医学基础模型在标签监督下的趋同性较低

arXiv:2607.20274v3 公告类型：替换并交叉列出 摘要：诊断分类器和影像生物标志物基于医学基础模型的嵌入表示进行拟合。随着新版本出现，这些模型会被替换。这种做法假定不同模型对图像的表示方式相似。我们使用来自5种成像模态、14个数据集的超过750,000张图像、18个公开模型，以及101个在胸部X线片和组织病理图像上训练的模型，检验了这一假设。这101个模型在预训练目标、标签类型、规模、随机种子、初始化、训练所用患者或训练方案方面存在差异。我们以相互k近邻重叠率衡量一致性，即一张图像在两个模型中的最近邻集合共有元素所占的比例。对于一张胸部X线片，两个公开模型的10个最近邻平均重叠比例为0.091。随机初始化的网络与这些模型的重叠比例为0.036。在其余四种模态中，两个公开模型的重叠比例最高为0.300。在胸部X线片上，一致性更多地取决于来源数据集和摄片体位，而非影像所见，且与准确率无显著关联。利用4,096张无标签X线片拟合两个模型嵌入表示之间的线性映射后，可迁移针对14种胸部影像所见的分类器，其受试者工作特征曲线下面积达到原始值的0.987。趋同性取决于预训练目标。在胸部X线摄影和组织病理学中，标签监督模型在两种模型规模下的趋同性均最低。在胸部X线片上，仅随机种子不同的两个标签监督模型，其最近邻重叠比例为0.062至0.100。而采用自蒸馏、掩码图像建模或对比预训练的两个模型，其最近邻重叠比例为0.411至0.802。因此，开发者在选择预训练目标时，也可以选择模型的趋同性。在替换模型之前，用户可以使用无标签图像检验后继模型是否返回相同的最近邻。

arXiv cs.LG66

H-OPD: Confidence Aware Heterogeneous Multi-Teacher Multimodal On-policy Distillation

arXiv:2607.02592v3 Announce Type: replace-cross Abstract: On-policy distillation (OPD) has recently emerged as an effective post-training paradigm by providing supervision on student-generated trajectories. However, existing OPD methods for multimodal reasoning usually rely on a static teacher routing, assigning each sample to a single teacher based on modality or task type. This ignores that visual grounding and abstract reasoning may dominate different decoding steps, making a single teacher insufficient for the full trajectory. To this end, H-OPD is proposed as a confidence-aware heterogeneous multi-teacher OPD framework for multimodal reasoning. By verifying the complementarity of heterogeneous teachers in the same reasoning process, H-OPD replaces task or sample level teacher routing with token-level teacher arbitration along the shared student trajectory. H-OPD employs vision-to-language description transfer to enable text-only teachers to access key visual semantics, and uses a confidence-aware arbitration mechanism to dynamically combine vision-language teacher and text-only teachers at each token. Extensive evaluations over 11 widely-used reasoning benchmarks showcase the superior performance of our method.

arXiv cs.LG68

Hierarchical Experimentalist Agents

arXiv:2606.29315v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to act in the real world and augment human decision-making, yet most systems rely on parametric knowledge acquired through imitation, optionally improved by post-training, retrieval, or search over fixed data. This paradigm struggles in novel domains or on complex queries that require information unavailable from prior knowledge alone; knowing the laws of physics, for example, does not by itself enable reliable reasoning or long-horizon actions in a complex physical system. We argue that agents need active experimentation: the ability to gather targeted query-specific evidence, discover general principles of unseen environments, and convert interaction experience into reusable skills. We introduce Hierarchical Experimentalist Agents (HExA), an in-context, experiment-centric self-improvement framework that (1) iteratively designs and refines query-relevant experiments, (2) incrementally distills experience into reusable and composable skills that accelerate experimentation within and across tasks, and (3) integrates experimental evidence to act or answer queries. HExA can work with any LLM including frontier black-box models, and requires no external supervision, oracle solutions, or offline data. We evaluate HExA on multi-step reasoning, interactive partially observable games, and physics law discovery tasks. To further evaluate when active experimentation is necessary, we introduce Interphyre, a set of hard procedural physics tasks where we enable tool-calling and intervention APIs. Current frontier LLMs including GPT-6 Astra fail in these settings: on the most challenging Interphyre tasks, we show that HExA can enable such models to near 100% success. Moreover, HExA improves token efficiency compared to naive agentic and memory baselines and enables generalization via hierarchical skill transfer from easier to harder tasks.

arXiv cs.LG75

RoboMME-Interference：干扰条件下的机器人记忆基准评测

arXiv:2606.22338v4 公告类型：替换并交叉列入 摘要：部署在真实环境中的机器人会在部署期间跨多个会话和任务积累经验。机器人的任务可能经常要求其记住多个会话之前的信息，因此，长上下文机器人记忆对于现实世界中的部署十分重要。然而，目前大多数机器人记忆基准都基于单个回合或较短的上下文。为了衡量当前机器人记忆系统在更长、干扰更多的会话中的表现，我们推出了 RoboMME-Interference，这是一个基于 RoboMME（Dai 等，2026）构建的跨会话基准。对于每个查询回合，我们使用与该查询相关的先前演示，并在其后接入数量可控的无关会话，构建会话历史，将其作为记忆提供给 VLA，并测量准确率。我们将 RoboMME 已发布的记忆增强型 $\pi_{0.5}$ 变体不加修改地用于这一基准，发现感知记忆变体在获得不含任何干扰项的历史时能够提高成功率，但随着无关会话的累积，其成功率会持续大幅下降。子目标变体使用视觉语言模型读取历史，并将文字形式的子目标传递给策略；这类变体在最佳情况下的提升幅度较小，但随着干扰项累积，能够保留更多的提升效果。我们为表现最强的感知记忆变体添加了检索步骤：从历史中选取与机器人当前视图在视觉上最相似的片段，并仅将该片段传递给策略。这使其在所有干扰水平下都恢复到了无干扰项时的成功率。通过此次发布，我们强调了长上下文记忆和抗干扰鲁棒性的重要性，并表明当前系统在这些能力上普遍表现不佳。项目主页、视频、代码和数据见 https://robotmemorybench.com。

arXiv cs.LG67

Decoupling the Declarative from the Procedural in Vision-Language-Action Models

arXiv:2606.21496v2 Announce Type: replace-cross Abstract: Deploying generalist robotic agents in the real world requires transferable skills. Specifically, a policy trained to clone a behavior from object-specific demonstrations must generalize beyond that object, otherwise data collection requirements become intractable. Recently, fine-tuning of pre-trained billion-parameter Vision-Language Models (VLMs), initially on large-scale robot datasets and then on fewer scenario-specific demonstrations, has emerged as the predominant paradigm for designing Vision-Language-Action (VLA) models. While these policies achieve state-of-the-art manipulation performance in-distribution, they remain brittle to minor spatial, semantic, and task variations. In this work, we address the inability of current models to decouple the declarative (i.e., concepts and entity semantics) from the procedural knowledge (i.e., how to do something) encoded in their parameters, which is a fundamental bottleneck for zero-shot skill transfer to novel objects. To address this, we propose w$^{2}$VLA, a new VLA model with restructured information flow. Rather than feeding all multimodal tokens from the VLM encoder into a large, opaque transformer-based action expert, our approach modulates the robot state sequence with visual, spatial, and skill information in a compositional and interpretable manner. Unlike popular, state-of-the-art VLAs, we show that our modular approach successfully decouples knowledge representations, enabling robust behavior cloning and unprecedented zero-shot skill transfer capabilities across dissimilar, unseen objects.

arXiv cs.LG70

TelcoAgent: A Scalable 5G Multi-KPM Forecasting With 3GPP-Grounded Explainability

arXiv:2606.19821v2 Announce Type: replace-cross Abstract: Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks. However, existing machine learning (ML) approaches face significant limitations in scalability and explainability, restricting their effectiveness in real-world deployments. We propose TelcoAgent, a foundation model-based framework that enables accurate, scalable, and explainable forecasting of multiple KPMs across diverse network cells without the need for site-specific training. Specifically, the framework comprises three key components: (i) an automated three-agent pipeline that constructs a 3rd Generation Partnership Project (3GPP) knowledge graph directly from specification documents, (ii) a scalable, time-series foundation model (TSFM)-based prediction pipeline to deliver accurate, zero-shot forecasting, and finally (iii) a reasoning and explanation pipeline that provides actionable, domain-grounded diagnostics. Evaluated using a 3-month, real-world, city-scale 5G KPM dataset from a U.S.-based network operator, TelcoAgent demonstrates high forecasting accuracy for all 7 considered KPMs per cell across 200 cells, while delivering explainable insights and actionable instructions to address network degradations.

arXiv cs.LG70

Bistable by Construction: Wall-Clock-Calibrated State Monitors Have No Moment-Detection Regime at Agent Cadence

arXiv:2606.19386v2 Announce Type: replace-cross Abstract: Runtime monitors for autonomous agents commonly threshold an accumulated internal state - a behavioural baseline, a drift statistic, or, in our prior work, a modelled affective state. We previously reported a State Saturation Trap: threshold-on-state triggers over a continuous affect engine become near-constant alarms on SWE-bench debugging agents (Modgil 2026). A post-release audit found the engine received dt=0 between actions, so its exponential decay never operated: the published trap is a pure-accumulator result. We correct the record (erratum, v2) and treat the flaw as an experiment. The key variable it exposes is whether a monitor's dynamics are calibrated in sample time (per observation, as in CUSUM) or wall-clock time (half-lives in seconds, as in affect models and EMA baselines). On fixed-rate streams these coincide; on agent streams, where inter-action time varies by orders of magnitude, they do not. A pre-registered sweep over uniform intervals (dt in {0..600}s) on 20 trajectories shows the wall-clock level trigger has two regimes: at dt<=1s a constant alarm (20/20; median 18 firings); at dt>=60s silent. Every critical dt lies in (1,30]s. A scriptable gpt-5.4-mini agent loop measures latency at median 1.53s (p90 2.33s), inside the trap regime, vindicating the empirical finding under a corrected mechanism. The structure is a property of the calibration class, not the engine: a minimal wall-clock accumulator over the raw error stream reproduces the same cliff, while a sample-time CUSUM over the identical stream is exactly dt-invariant (20/20). A rising-edge trigger with hysteresis fires 0-3 times per trajectory in every condition. We conclude that wall-clock-calibrated leaky-integrator monitors admit no regime in which they act as moment detectors on agent streams; transition detection escapes the trap at every cadence, but does not recover human intervention timing.

arXiv cs.LG73

PACT：面向多轮工具使用智能体的特权轨迹协同训练

arXiv:2606.16215v2 公告类型：替换并交叉发布 摘要：多轮工具使用智能体必须在多轮交互中进行推理、调用工具并根据观测结果作出调整。对此类智能体进行后训练颇具挑战：强化学习虽然与仅依赖提示的推理设置相匹配，却常常面临奖励稀疏和信用分配能力不足的问题；而基于专家轨迹的监督微调虽能提供密集的过程监督，却可能使模型过度受限于固定轨迹。为解决这些问题，我们提出 PACT，一种面向多轮工具使用智能体的特权轨迹协同训练框架。其核心思想是仅将专家轨迹用作训练时的优化信号，而非轨迹生成时的提示。PACT 在生成交互轨迹时仍仅使用提示，随后通过两种互补信号利用专家轨迹指导优化：一种是以轨迹为条件的强化学习代理目标，在专家轨迹上下文下评估仅基于提示生成的交互轨迹；另一种是组件感知的监督微调损失，对推理和工具调用组件提供逐步退火的过程监督。为减少对仅在训练时可用的轨迹上下文的过度依赖，PACT 引入了仅提示锚定机制，采用标准的仅提示强化学习更新。在 FTRL、BFCL 和 ToolHop 上的实验表明，PACT 始终优于基于监督微调和强化学习的强基线。进一步分析证实了这两种训练目标的互补性，以及组件感知监督和仅提示锚定机制的益处。这些结果彰显了特权轨迹协同训练在多轮工具使用学习中的有效性。项目页面：https://zhenbangdu.github.io/pact-project-page。

arXiv cs.LG72

Lesion-DDPM：用于多发性硬化 MRI 合成的病灶增强型三维扩散

arXiv:2606.15457v2 公告类型：replace-cross 摘要：三维 FLAIR MRI 被广泛推荐为多发性硬化（MS）脑成像的标准 MRI 序列之一，但公开可用的 MS 数据集规模仍相对较小，且在扫描仪、采集协议和病灶模式方面存在差异。这种数据稀缺性和变异性阻碍了稳健的神经影像机器学习模型的开发，对于旨在合成图像的同时保留微小、稀疏病灶的生成模型而言，挑战尤为突出。我们提出 Lesion-DDPM，这是一种用于病灶感知 FLAIR 合成的三维条件扩散框架，将多层级解剖掩膜注入与病灶加权重建损失相结合，在保持全局脑结构的同时强化对病灶体素的关注。我们使用经过整理的 MSLesSeg 数据集子集，将 Lesion-DDPM 与具有代表性的最先进 GAN 模型和扩散模型进行比较，同时评估图像生成指标和下游 3D U-Net 分割表现。在实验中，Lesion-DDPM 在所有方法中取得了最低的病灶区域重建误差。在下游 3D U-Net 病灶分割任务中，仅使用 Lesion-DDPM 生成的扫描图像训练、并在真实 MRI 上评估的模型达到了 0.616 的 Dice 分数，而表现最好的对比合成数据集对应的分数为 0.569。将 Lesion-DDPM 图像加入真实训练集后，Dice 分数进一步提升至 0.685。

arXiv cs.LG77

智能体的最后一场考试

arXiv:2606.05405v3 公告类型：replace-cross 摘要：近期，AI 系统在众多基准测试中取得了优异成绩，但这些进展尚未转化为众多专业领域中具有经济意义的实际部署。我们认为，这一差距在很大程度上源于评估问题：广泛使用的基准缺乏对真实且具有经济价值的工作流程的持续性能衡量。本文介绍 Agents' Last Exam (ALE)，这是一个旨在评估 AI 智能体完成长周期、具有经济价值且结果可验证的真实世界任务的基准。ALE 与 250 多位行业专家合作开发，涵盖参照 O*NET / SOC 2018（美国联邦职业分类体系）界定的非体力劳动行业。其任务分类体系包含 55 个细分领域，归为 13 个行业集群，覆盖 1,000 多项任务。当前结果表明，最困难层级仍远未达到性能饱和：在各种主流运行框架与骨干模型配置下，平均完全通过率低于 1%。ALE 被设计为一个持续演进的基准：随着新工作流程和行业的纳入，其任务池不断扩展。更广泛而言，ALE 不仅旨在提供又一个排行榜，更希望成为缩小基准测试成功与 GDP 相关实际影响之间差距的工具。

arXiv cs.LG74

物理学的无形之手：当视频扩散模型知道的比展现的更多

arXiv:2606.05328v2 公告类型：replace-cross 摘要：现代视频扩散模型生成的视频日益逼真，且具有越来越强的时间连贯性，这促使人们将其视为世界模拟器的候选方案。然而，这些模型究竟是在内部编码了物理结构，还是仅仅复现了训练中见过的运动模式，目前仍不清楚。我们沿着与物理合理性已知的真实视频相对应的潜在轨迹，对视频扩散模型进行探测，以研究这一问题。为获得这些轨迹，我们从无噪声的视频潜在表示出发，对学习到的速度场进行反向积分直至噪声，从而近似逆转确定性采样过程，进而获取模型的中间状态和注意力图。利用这些恢复的轨迹，我们表明，在 IntPhys 和 InfLevel 上，均可从扩散 Transformer 的状态中线性解码出物理合理性，平均准确率达到约 81.27%，优于 V-JEPA 和 VideoMAE 等专门的表征学习基线。令人惊讶的是，VAE 的潜在输入中并不存在这一信号，它是在去噪 Transformer 内部涌现的，尽管该模型并未采用自监督预测目标进行训练。这些发现表明，具有物理意义的表征可以作为生成式去噪的副产物而产生。

arXiv cs.LG79

微调过程中虚假相关性的无监督识别与去除

arXiv:2605.27676v2 公告类型：替换并交叉归类 摘要：在经过筛选整理的数据集上微调预训练语言模型，可能会在微调任务与非预期的潜在因素（如失准人格或政治倾向）之间产生虚假相关性，因为数据筛选整理过程已将这些因素与任务纠缠在一起。模型可能会依赖这些虚假相关性，从而产生偏见并降低分布外泛化能力。我们证明，在对任务复杂度和虚假相关性作出合理假设的情况下，可以在没有监督的条件下，从朴素 LoRA 微调所得的权重中识别这些潜在因素。现有的偏见去除方法（如激活引导）会在推理或训练期间，从残差流激活中移除已识别的因素。然而，我们认为，目标应当是去除虚假相关性，而不是潜在因素本身，因为预训练模型可能依赖该因素来获取真正的任务信号。为此，我们提出 GRASP（GRadient projection of Associated Spurious Patterns），在保留预训练模型沿该潜在因素方向已有内容的同时，防止模型对已识别的潜在因素形成新的依赖。我们在三个微调任务上进行了验证。前两个任务涉及涌现性失准，即在狭窄任务上进行微调（在我们的实验中，分别是编写不安全代码和提供不当医疗建议），会导致模型在无关话题上给出失准回答。在这两种情形下，我们的方法分别完全消除了不安全代码场景中的失准，并将不当医疗建议场景中的失准降至约五分之一，在减少失准与保留任务能力之间的权衡上优于所有基线。最后一个任务是一项新的政治偏见实验：在右倾的 Reddit 财务建议数据上进行微调，会导致模型在无关话题上的政治倾向发生漂移。在该实验中，我们的方法将漂移幅度降低了一半以上，同时提升了财务任务表现，优于所有基线。

arXiv cs.LG72

TabPFN 是保险定价的万能解法吗？

arXiv:2605.22892v3 公告类型：replace-cross 摘要：非寿险定价中的索赔频率和索赔损失程度建模主要依赖广义线性模型（GLM），而梯度提升机是主要的机器学习替代方案。表格基础模型（TFM）提出了一种根本不同的推理范式。通过在大量合成数据集上进行预训练，TFM 能够借助上下文学习对新数据进行推理，无须针对特定数据集进行预处理、拟合或超参数调优。本文首次对 TabPFN 在机动车保险定价中的应用进行了实证评估，在两个公开可用的机动车第三者责任险（MTPL）数据集上，将两个版本的 TabPFN 与 GLM 和 XGBoost 进行了基准比较。结果表明，TabPFN 并未始终优于成熟的基线模型：在索赔频率建模方面，其表现不及 GLM，且性能取决于上下文训练集的规模；但在数据更为稀缺的索赔损失程度建模方面，它具有竞争力。其推理耗时则明显更长。尽管表格基础模型前景可期，尤其是在数据稀缺的场景下，但 TabPFN 尚不足以取代成熟的索赔频率精算建模方法。

arXiv cs.LG70

Cross-View Attention Fusion Net: A Prior-Guided Dual-View Representation Learning for Cardiac Output Estimation from Short-Term PPG Signals

arXiv:2605.19666v2 Announce Type: replace-cross Abstract: Accurate cardiac output (CO) estimation from photoplethysmography (PPG) is promising for unobtrusive hemodynamic monitoring, but remains difficult since CO is jointly determined by cardiac function and vascular tone. Conventional feature-based models use physiologically meaningful PPG descriptors, yet depend on accurate pulse detection and may miss latent temporal relationships. In contrast, fully end-to-end deep learning models learn directly from raw PPG but often underuse established PPG-derived prior information. Here, we introduce the Cross-View Attention Fusion Network (CVAF-Net), a prior-guided dual-view deep learning model for CO estimation from short, fixed-length PPG segments. CVAF-Net processes raw PPG as a temporal view and a feature sequence map (FSM) as a structured prior-guided view, and fuses the two representations through cross-view attention. The model was independently evaluated using 5-, 15-, and 30-s segments from three datasets: simulated pulse waves (3323 subjects), vasoconstriction provocation (79 subjects), and resting/cycling activities (10 subjects), and was compared with multiple machine learning and deep learning benchmarks. CVAF-Net outperformed most benchmark methods and achieved performance comparable to a state-of-the-art Transformer-based model, with a mean absolute error (MAE) of 0.19 L/min (MAPE: 3.95%) on simulated data and high accuracy in real-world settings (minimum MAE: 1.20 L/min). Importantly, CVAF-Net reduced FLOPs by twelvefold compared with the leading Transformer-based model. Plausibility analysis showed physiologically consistent CO estimates, with expected correlations with age ($\rho = -0.274$), heart rate ($\rho = 0.894$), and systemic vascular resistance ($\rho = -0.740$). These findings indicate that CVAF-Net provides an accurate, computationally efficient, and generalizable approach for continuous wearable-based CO monitoring.

arXiv cs.LG65

Stochastic Compositional Optimization via Hybrid Momentum Frank--Wolfe

arXiv:2605.15350v2 Announce Type: replace-cross Abstract: Stochastic compositional optimization minimizes $F(f(x), x)$ over a compact convex set when the inner map $f$ is available only through noisy samples. We propose Hybrid Momentum Stochastic Frank--Wolfe, a projection-free method for outer functions $F$ that are jointly convex and Lipschitz but not necessarily differentiable, such as maxima, CVaR, and norms. The method tracks the Jacobian and the value of $f$ with momentum, using a Taylor correction for the value, and passes the tracked linearization to a generalized linear minimization oracle. Under finite-variance noise, it attains an expected generalized Frank--Wolfe gap of order $K^{-1/4}$ for non-convex objectives and, when the linearized models minorize the objective, a last-iterate primal error of order $K^{-1/3}$. The analysis extends to heavy-tailed noise without clipping, with separate moment orders for values and Jacobians. With an exact oracle, the method reduces to the deterministic method of Vladarean et al. (2023).

arXiv cs.LG77

Vision2Code：用于评估图像到代码生成的多领域基准

arXiv:2605.11307v2 公告类型：replace-cross 摘要：我们研究视觉语言模型（VLM）能否通过编写可执行代码来生成图像，从而重建结构化的图形类图像。现有图像到代码生成基准通常覆盖的领域有限，依赖参考源代码进行评估，且未提供可量化领域特定问题的指标。为此，我们提出 Vision2Code，这是一个无需参考代码的多领域基准，包含来自 15 个不同数据集的 539 个测试样例，涵盖图表与绘图、几何图形、图结构、科学示意图、文档和三维空间场景。评估时，我们执行生成的程序，并让 VLM 评判模型依据针对各数据集和领域制定的评分准则，将程序的渲染输出与源图像进行比较。我们发现，以这种方式获得的最终分数与人类评分的相关性，显著高于使用通用 VLM 评分准则，或标准的基于像素、基于嵌入及感知相似性指标所获得的分数。我们进一步证明，在对 VLM 评判模型进行重复采样、调整评分准则权重以及引入受控错误的情况下，我们的评估仍然可靠。结果表明，现有图像到代码生成模型在不同领域的表现存在差异，其中在图表和图结构上的准确率显著高于其他领域。最后，我们展示了该评分方法在通过拒绝采样筛选生成程序以用于微调方面的实用价值：与各自的基础模型检查点相比，Qwen3.5-9B 的表现提升了 16%，Phi-4-MM 提升了 28%。我们的代码和数据已公开发布于 https://image2code.github.io/vision2code/。

arXiv cs.LG68

UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence

arXiv:2605.09040v4 Announce Type: replace-cross Abstract: Modeling ultra-long user sequences involves a difficult trade-off between efficiency and effectiveness. While current paradigms rely on either item-specific search or item-agnostic compression, we propose UxSID, a framework exploring a third path: semantic-group shared interest memory. By utilizing Semantic IDs (SIDs) and a dual-level attention strategy, UxSID captures target-aware preferences without the heavy cost of item-specific models. This end-to-end architecture balances computational parsimony with semantic awareness, achieving state-of-the-art performance and a 0.337% revenue lift in large-scale advertising A/B test.

arXiv cs.LG75

具有全局最优性与近似保证的脉冲神经网络凸优化

arXiv:2605.08022v3 公告类型：replace-cross 摘要：脉冲神经网络（SNN）被提出作为深度神经网络的节能替代方案。其训练是非凸的，并且涉及不可微的脉冲函数，因此现有方法无法保证达到全局最优。我们通过将 SNN 优化表述为等价的凸优化问题来解决这一问题。具体而言，我们证明，对于一类阈值 RNN（包括漏积分发放 SNN），其带权重衰减正则化的训练问题具有零对偶间隙，且最优值与一个凸规划相同。由于所得凸规划的变量数量可能随数据量呈指数级增长，我们引入了一种实用的随机化替代方案，并对其近似质量给出了高概率保证。实验表明，表现最佳的凸优化变体在加法和 first-last-XOR 任务上优于替代梯度训练，在序列 MNIST 任务上与其表现相当，但在三进制和五进制加法任务上落后于储备池基线。

arXiv cs.LG77

FactoryBench：评估工业机器理解能力

arXiv:2605.07675v2 公告类型：替换并交叉列出 摘要：我们提出 FactoryBench，这是一个基于工业机器人遥测数据、用于评估时间序列模型和大语言模型（LLM）机器理解能力的基准。问答对按照四个因果层级（状态、干预、反事实、决策）组织，具体体现 Pearl 的因果阶梯，并涵盖五种答案格式：四种结构化格式采用确定性方式评分，自由形式答案则通过以 LLM 为裁判的投票协议评分。我们提出了一个围绕结构化问题模板构建的可扩展问答生成框架，介绍了 FactoryWave（一个从 UR3 协作机器人和 KUKA KR10 工业机械臂采集的高密度、多任务、多变量传感器数据集），并基于 FactoryWave、AURSAD 和 voraus-AD 中约 1.5 万个标准化片段，构建了包含超过 7 万个问答条目的大规模基准 FactoryBench。对六个前沿 LLM 的零样本评估表明，没有任何模型在结构化层级上的得分超过 50%，也没有任何模型在决策层级上的得分超过 18%，这揭示了当前模型与实际运行所需的机器理解能力之间的巨大差距。

arXiv cs.LG62

Fairness-Guided DQN-Based TXOP Control for NR-U/Wi-Fi Coexistence

arXiv:2605.00457v5 Announce Type: replace-cross Abstract: New Radio operating in unlicensed spectrum (NR-U) and Wi-Fi employ different contention parameters and channel-occupancy durations, which can produce severe coexistence imbalance. This correspondence develops a fairness-guided deep Q-network (DQN) that adjusts the NR-U transmission opportunity (TXOP) using the observed per-node throughput ratio. Two reward designs, Q1 and Q2, expose complementary operating regimes. Q1 promotes strict per-node throughput balance, whereas Q2 admits a broader inter-radio-access-technology fairness region and rewards throughput improvement in an intermediate fairness band. The policies are compared with the default Third Generation Partnership Project (3GPP) listen-before-talk (LBT), an adapted multi-armed-bandit method, a utility-fairness-based tabular Q-learning comparator, and a default-referenced fixed-TXOP baseline. Experiments across four priority classes and asymmetric populations report aggregate throughput, user-level Jain fairness, and concave throughput utility over 20 independent trials. The results characterize the cost of strict fairness, efficiency recovery under moderate relaxation, and transfer from symmetric to asymmetric deployments.

arXiv cs.LG73

奥卡姆剃刀的锋利程度取决于你的 ELBO

arXiv:2604.25984v2 公告类型：replace-cross 摘要：边际似然又称证据，被视为奥卡姆剃刀原则的数学体现，能够实现避免过拟合的超参数选择。变分推断中的证据下界（ELBO）目标也被用于类似目的。本文根据控制信号和噪声方差的关键超参数，定义了贝叶斯线性回归的信噪比（SNR）。随后，我们研究了基于 ELBO 的超参数学习如何因高斯近似后验所假设的协方差矩阵结构不同而低估或高估 SNR。我们证明，假设协方差矩阵为对角矩阵或低秩加对角矩阵会低估 SNR，而假设其为低秩矩阵则会高估 SNR。低估 SNR 会使训练集上的均方误差高于最优水平（欠拟合），而高估 SNR 则会使误差更低（过拟合）。希望将贝叶斯方法扩展至大型模型的实践者应谨慎考虑：为确保计算可行性而采用的协方差假设可能如何影响模型选择。

arXiv cs.LG70

MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Data

arXiv:2604.00333v3 Announce Type: replace-cross Abstract: Collective behaviors that emerge from interactions are fundamental to numerous biological systems. To learn such interacting forces from observations, we introduce a measure-valued neural network that infers measure-dependent interaction (drift) terms directly from particle-trajectory observations. The proposed architecture generalizes standard neural networks to operate on probability measures by learning cylindrical features, using an embedding network that produces scalable distribution-to-vector representations. On the theory side, we establish well-posedness of the resulting dynamics and prove propagation-of-chaos for the associated interacting-particle system. We further show universal approximation and quantitative approximation rates under a low-dimensional measure-dependence assumption. Numerical experiments on first and second order systems, including deterministic and stochastic Motsch-Tadmor dynamics, two-dimensional attraction-repulsion aggregation, Cucker-Smale dynamics, and a hierarchical multi-group system, demonstrate accurate prediction and strong out-of-distribution generalization.

arXiv cs.LG68

Locally Private Parametric Methods for Change-Point Detection

arXiv:2602.13619v2 Announce Type: replace-cross Abstract: We study parametric change-point detection under local differential privacy, focusing on the offline localization of a single change point in a sequence of independent observations.~In the non-private setting, we derive improved finite-sample accuracy guarantees for a generalized likelihood-ratio estimator using martingale methods.~We establish finite-sample minimax lower bounds in both the non-private and locally private settings, showing that localization accuracy is fundamentally limited by the Chernoff information between the pre- and post-change distributions.~Guided by this, we design locally private CPD algorithms based on aggregated randomized response and binary mechanisms, which maximize the Chernoff information between the induced output distributions in low- and high-privacy regimes, respectively, and derive their finite-sample guarantees. Together, our upper and lower bounds quantify the statistical cost of privacy in change-point localization.~As part of our analysis, we also show that strong data processing inequality coefficients of R\'enyi divergence and Jeffreys--R\'enyi divergence of order infinity can be computed by restricting to pairs of input distributions with a common support of at most two symbols.~We corroborate our results through empirical evaluation on synthetic data.

arXiv cs.LG75

RanSOM：用于约束与无约束优化的随机缩放二阶动量

arXiv:2602.06824v3 公告类型：替换并交叉列出 摘要：二阶动量通过 Hessian 向量积（HVP）修正动量相对于当前梯度的滞后，但现有修正方法要么要求 Hessian 满足 Lipschitz 条件，要么需要在辅助点进行额外的前向和反向传播。我们证明，随机化步长可以消除这一权衡：如果步长服从均值为 $\eta$ 的指数分布，则一个 Stein 型恒等式表明，在新迭代点计算的一次 HVP 乘以 $\eta$，就是梯度变化的一个估计，且该估计关于步长的期望是无偏的；此外，该 HVP 与下一次梯度计算共享前向传播。由此得到的方法 RanSOM-E 适用于归一化步、符号步和 Muon 型步，而采用 Beta 分布的变体 RanSOM-B 则可使 Frank-Wolfe 迭代点保持可行。对于梯度满足 Lipschitz 条件、且梯度噪声和 Hessian 噪声具有重尾分布、尾指数为 $p, q \in (1,2]$ 的目标函数，RanSOM-E 无需 Hessian 满足 Lipschitz 条件，也无需裁剪，即可对每一对 $(p,q)$（包括 $p \neq q$）达到最优的预言机复杂度指数（当 $p = q = 2$ 时为 $\mathcal{O}(\varepsilon^{-3})$）。在 CIFAR-10 上使用 ResNet-20 的实验中，我们未检测到其与现有二阶动量方法之间的准确率差异，且其平均更新时间低于辅助点方法。

arXiv cs.LG71

非平稳加权风险最小化的快速收敛速率

arXiv:2602.05742v3 公告类型：replace-cross 摘要：加权经验风险最小化是分布漂移条件下进行预测的一种常用方法。本文研究其在非平稳条件下的样本外预测误差。我们给出将超额风险分解为学习项和分布漂移相关误差项的一般形式，并证明混合条件下学习误差的预言不等式。学习误差界对任意权重类一致成立，并考虑了权重向量所诱导的有效样本量、权重类与假设类的复杂度，以及潜在的数据依赖性。我们还分析了平方损失情形下的漂移误差，并为结构突变过程和平滑漂移过程给出显式界。随后，我们在采用线性模型、基函数近似和神经网络的（自）回归问题中，将学习误差分析与漂移误差分析相结合。在所有情形下，最优权重均由学习与漂移之间的偏差—方差权衡刻画；而在无权重且平稳的特殊情形下，则可恢复极小极大最优速率（相差至多对数因子）。

arXiv cs.LG77

HumanX：从人类视频中学习敏捷且可泛化的人形机器人交互技能

arXiv:2602.02473v2 公告类型：replace-cross 摘要：让人形机器人执行敏捷且自适应的交互，一直是机器人领域的核心挑战。当前方法受限于逼真交互数据的稀缺，或依赖精细的任务专用奖励设计，这限制了其可扩展性。为缩小这一差距，我们提出 HumanX，这是一个统一框架，可将人类视频转化为人形机器人可泛化的现实世界交互技能，无需针对每项任务单独设计。HumanX 集成了两个协同设计的组件：XGen 是一条以锚点为中心的数据生成流水线，从单目视频中生成符合物理规律的机器人交互数据，并通过数据增强构建多样化、可扩展的训练数据集；XMimic 是一个交互优先的学习框架，可从数秒的演示中学习可泛化的交互技能。我们在篮球、足球、羽毛球、货物拾取和反应式格斗五个不同领域进行评估，HumanX 成功习得了 9 种不同技能，并将其零样本迁移至实体 Unitree G1 人形机器人。习得的能力包括在不依赖任何外部感知的情况下完成投篮假动作接转身后仰跳投等复杂动作，以及仅从单段视频演示中学会连续超过 10 个回合的人机传球等交互任务。实验表明，HumanX 的泛化成功率高于以往方法，展示了在一类可锚定的交互中学习多样化现实世界机器人交互技能的可扩展路径。

arXiv cs.LG70

A Generalizable Machine Learning Framework for Half-Heusler Thermoelectrics

arXiv:2602.01149v2 Announce Type: replace-cross Abstract: Machine learning (ML) has emerged as a powerful approach for accelerating the discovery of thermoelectric (TE) materials, yet many models suffer from limited generalizability. Here we present a robust ML framework for predicting the thermoelectric figure of merit (zT) in half-Heusler (hH) compounds, emphasizing prediction of previously unseen compositions. A principal component analysis (PCA)-based train/test splitting strategy is introduced to generate representative partitions of chemical space. Bayesian hyperparameter optimization is combined with correlation-based k-best feature selection for Random Forest, XGBoost, and Neural Network models, together with the interpretable SISSO symbolic-regression method. Relative to the full set of 118 input features, the optimized feature subsets reduce the test RMSE by 16-27%. SHAP and SISSO analyses identify temperature, A-site dopant concentration, and A-site heat of vaporization as the most important descriptors of zT. The framework is validated through prediction of previously unexplored compositions, recovery of literature-reported optimal zT values for established hH systems, and first-principles calculations for the new compounds CeCoBi and CeIrBi, which exhibit favorable electronic transport properties. The framework is further applied to high-throughput screening of the doped hH chemical space, covering about 2.46 million elemental substitution combinations across 631 thermodynamically screened parent matrices under stability constraints. This screening yields a concise set of promising candidates for future study. Overall, the work establishes a generalizable workflow for reliable data-driven thermoelectric materials discovery.