论文

VeriForge 是一种混合主动写作系统，通过划分认知劳动，使系统在领域发现上承担主动权，而作者保留对叙事合成的完全主动权；在受控的冷启动写作任务中，专家评审者认为其产出在领域扎根方面更强。VeriForge is a mixed-initiative writing system that divides cognitive labor so that the system assumes initiative over domain discovery while the author retains full initiative over narrative synthesis, and is perceived by expert raters to produce passages with stronger domain grounding in a controlled cold-start writing task.

本文倡导源对比评估，并构建了 Cultivar——FLORES 的本地化子集，可用于特定 locale 的翻译评估；研究发现：MT 专用模型鲁棒性较差，少数模型可能对 FLORES 存在过拟合，且模型普遍更擅长翻译美国 locale 的内容，而非其他 locale，无论语种如何。This work advocates for source-contrastive evaluation and instantiates Cultivar, a localised subset of FLORES, which enables locale-specific translation evaluation and finds that MT-specialised models are less robust, a few models potentially overfit FLORES, and models tend to translate US content better than that of other locales, regardless of language.

本文提出 Business Arena——一个受控环境，AI agent 在其中经营跨境店铺，在长周期内向供应商采购并向买家销售，迈出了构建面向端到端商业 agent 的真实可信测试床的第一步。Business Arena, a controlled environment where an AI agent runs a cross-border shop, buying from suppliers and selling to buyers over a long horizon, is introduced, taking a first step toward a realistic and trustworthy testbed for evaluating end-to-end business agents.

面向策略性优化下的可测性设计指南：保留探针仅在不可枚举轴上保持有效性；门控必须衡量保留集性能，而非仅正确性；迁移率只有在附带每类失败机制评级时才可解读。Design guidance for measurement under strategic optimization is distill design guidance for measurement under strategic optimization: held-out probes retain validity only on non-enumerable axes; gates must measure held-out performance, not just correctness; and a transfer rate is interpretable only with per-failure mechanism grades.

Atelier 是一种面向艺术家风格图像生成的捷径感知控制状态规划框架，可提升艺术家级风格保真度，更忠实地保持源结构，并相较提示工程、检索增强与通用 agent 基线大幅减少捷径替换。Atelier, a shortcut-aware control-state planning framework for artist-grounded image generation, improves artist-level style fidelity, preserves source structure more faithfully, and substantially reduces shortcut substitution compared with prompt-engineered, retrieval-augmented, and general-purpose agent baselines.

与现有句法语言模型在推理时对众多句法树求边际或在运行时丢弃句法不同，SiPE 以单一句法树为条件，在句法监督与推理成本之间建立了新的 Pareto 前沿。Unlike existing syntactic language models that marginalize over many parses at inference or discard syntax at runtime, SiPE conditions on a single parse, establishing a new Pareto frontier between syntactic supervision and inference cost.

本文提出 Combodied Agents——一种以人为中心的范式，借助软件工具、传感器、可穿戴设备、机器人与人工服务作为行动通道而非终极目标，在时间维度上感知、建模、预测并支持个体的人体状态轨迹。Combodied Agents is introduced, a human-centered paradigm that perceives, models, predicts, and supports individual human-state trajectories over time, using software tools, sensors, wearables, robots, and human services as action channels rather than end goals.

对抗性 Fréchet 距离 (AdvFD) 用经过校准的对抗性学习表示来补充 FD-Loss 中的静态表示目标，通过对抗方式最大化真实样本与生成样本之间的 Fréchet 差异；同时引入真实特征白化，对尺度与协方差几何进行归一化，从而稳定极小极大优化。Adversarial Fr\'echet Distance (AdvFD), which complements the static representation targets in FD-Loss with a calibrated adversarially learned representation that adversarially maximizes the Fr\'echet discrepancy between real and generated samples, and introduces real-feature whitening, which normalizes its scale and covariance geometry and stabilizes the min--max optimization.

本文研究基于开源大语言模型的无参考多语言机器翻译后训练，发现 on-policy 蒸馏能够达到但无法超越结合 checkpoint 插值的强化学习所确立的质量前沿。This work studies reference-free post-training for multilingual machine translation with open large language models and finds that on-policy distillation reaches, but does not surpass, the quality frontier achieved by RL with checkpoint interpolation.

本文提出 DistilVDR，一个 524M 的端到端 VDR 系统，通过逐点余弦对齐损失从一个 8B 视觉-语言教师模型进行双向蒸馏，并以非对称的纯编码器学生模型匹配 VDR 的文本查询与图像-文档输入不对称性，将视觉容量集中于文档端，查询端保持 70M 参数。This work presents DistilVDR, a 524M end-to-end VDR system distilled bilaterally from a single 8B vision-language teacher under a pointwise cosine alignment loss and matches VDR's text-query and image-document input asymmetry with an asymmetric encoder-only student that concentrates visual capacity on the document side and keeps the query side at 70M parameters.

结果表明，剪枝效果更取决于剪枝应用的位置，而非具体的评分规则：早期剪枝带来最大的端到端节省，后期剪枝主要用于细化最终的合成上下文。The results show that pruning effectiveness depends more on where pruning is applied than on the specific scoring rule: early pruning yields the largest end-to-end savings, while later pruning mainly refines the final synthesis context.

本文实现了对时间序列数据集相似度方法的系统化、可复现比较，提供灵活的扩展性以添加自定义数据集、相似度方法及下游时间序列任务，并通过集成的时间序列数据集归约器对数据集级和序列级相似度方法进行一致评估。This work enables systematic and reproducible comparisons of time-series dataset similarity methods, flexible extensibility for users to add customized datasets, similarity methods, and downstream time-series tasks, and consistent evaluation of both dataset-level and series-level similarity methods through integrated time-series dataset reducers.

iFAN 提出 Adjusted Probability-Mask Ranking (APMR)，将查询竞争与预测的掩码质量对齐，抑制高置信度但不准确的竞争者；同时采用 Cross-Layer Self-Distillation (CLSD) 将更强的中间预测传递至最终层。iFAN introduces Adjusted Probability-Mask Ranking (APMR), which aligns query competition with predicted mask quality and suppresses high-confidence but inaccurate competitors, and employs Cross-Layer Self-Distillation (CLSD) to transfer stronger intermediate predictions to the final layer.

本文引入静止状态 (rest-state) 公式化方法，从单一闭合构型重建关节物体——这是一种固有的不适定设定，几何、语义与运动先验在此弥补运动线索的缺失。This work introduces a rest-state formulation that reconstructs articulated objects from a single closed configuration, an inherently ill-posed setting where geometry, semantics, and motion priors compensate for the absence of motion cues.

本文提出 InSight-doc，一种智能体视觉感知框架，将视觉分辨率视为一种自适应的推理时资源，从低分辨率起步，选择性地放大高分辨率区域以获取更细粒度的证据，且不依赖任何外部检索器。This work proposes InSight-doc, an agentic visual perception framework that treats visual resolution as an adaptive reasoning-time resource that starts from low resolution and selectively zooms into high-resolution regions for finer evidence, without relying on any external retriever.

本文提出解码层禁忌 (Decoding-Level Taboo)，一种零提示的诊断式压力测试，在运行时直接干预 logit 空间，于词边界处动态遮蔽主要候选 token，强制模型进行迂回表达。Decoding-Level Taboo is introduced, a zero-prompt diagnostic stress test that intervenes directly in logit space at runtime, forcing models out of their nominal paths by dynamically masking primary candidate tokens at word boundaries, forcing machine circumlocution.

本文提出 UniMoMo，一种后训练压缩框架，将其形式化为约束图粗化问题，并引入分层自适应保护机制，根据路由暴露度限制对高流量 expert 的合并。UniMoMo, a post-training compression framework formulated as a constrained graph coarsening problem, is introduced, and a layer-adaptive protection mechanism that restricts the merging of high-traffic experts based on their routing exposure is introduced.

360CityArena 为真实感城市区域导航与空间推理提供了必要且具有挑战性的测试平台；基于 SOTA LMM 智能体的评估显示，即使是最强的模型 Gemini 2.5 Flash，其表现仍远低于人类水平。360CityArena provides a necessary and challenging testbed for photorealistic urban-district navigation and spatial reasoning, and evaluation using state-of-the-art LMM-based agents shows that even the strongest model, Gemini 2.5 Flash, performs far below human level.

这篇立场论文定义了可解释性，阐述了何时需要（以及何时不需要）可解释性，并提出了一种用于严格评估的分类法，同时指出了迈向更严谨的可解释机器学习科学所面临的开放性问题This position paper defines interpretability and describes when interpretability is needed (and when it is not), and suggests a taxonomy for rigorous evaluation and exposes open questions towards a more rigorous science of interpretable machine learning.

本工作提出在海量文本语料上使用新的自监督目标 PEGASUS 对大型 Transformer 编码器-解码器模型进行预训练，并证明其在所有 12 个下游数据集上按 ROUGE 分数衡量均取得 SOTA 性能This work proposes pre-training large Transformer-based encoder-decoder models on massive text corpora with a new self-supervised objective, PEGASUS, and demonstrates it achieves state-of-the-art performance on all 12 downstream datasets measured by ROUGE scores.

从算法建模、应用和理论分析角度对 MTL 的综述，给出了 MTL 的定义，并将不同 MTL 算法分为五类：特征学习方法、低秩方法、任务聚类方法、任务关系学习方法和分解方法A survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses, which gives a definition of MTL and classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach,task relation learning approach and decomposition approach.

尽管方法简单，Decision Transformer 在 Atari、OpenAI Gym 和 Key-to-Door 任务上达到或超过 SOTA 无模型离线 RL 基线的性能Despite its simplicity, Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks.

较为全面地列举了基于群体智能、生物启发、物理启发和化学启发（按灵感来源分类）的所有算法，这些算法已成为解决实际问题的流行工具A relatively comprehensive list of all the algorithms based on swarm intelligence, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration, that have become popular tools for solving real-world problems.

提出了能够平衡"延迟告警的上下文敏感成本"与"打断成本"的模型与推理流程，并通过对用户活动与通知内容的分析，描述了在不确定性下推理此类成本所面临的挑战Models and inference procedures that balance the context-sensitive costs of deferring alerts with the cost of interruption are presented and the challenge of reasoning about such costs under uncertainty via an analysis of user activity and the content of notifications is described.

引入了一个框架，通过提供简洁接口来跟踪实时能耗与碳排放、生成标准化的在线附录来简化核算，并为节能的强化学习算法建立排行榜以激励负责任的研究A framework is introduced that makes accounting easier by providing a simple interface for tracking realtime energy consumption and carbon emissions, as well as generating standardized online appendices, and creates a leaderboard for energy efficient reinforcement learning algorithms to incentivize responsible research.

本文提出连续性核 (Continuity Kernel, CK)，一种激活契约，将提交前候选评估与原子状态激活解耦，将连续性定义为已接受分支头的连续且经过授权的谱系。The Continuity Kernel (CK), an activation contract that decouples off-commit candidate evaluation from atomic state activation from atomic state activation is presented, defining continuity as an unbroken, authorized lineage of accepted branch heads.

本文提出一种框架，用于从系统描述自动构建 DML 模型并将其表示为知识图谱 (KG-DML)，以 RAG 和 LLM 作为使能工具。This study presents a framework for automated construction of DML models from system descriptions and their representation as Knowledge Graphs (KG-DML), using Retrieval-Augmented Generation and Large Language Models as enabling tools.

结果表明，长周期软件开发可以围绕持久化项目而非持久化智能体来组织；本文提出 EvoX Genesis，使软件项目保持持久，同时允许局部智能体保持有限生命周期。Results show that long-horizon software development can be organized around a persistent project rather than a persistent agent, and EvoX Genesis is introduced, which instead makes the software project persistent while allowing local agents to remain finite-lived.

本文表明，利用在大规模数据上预训练的 HPE 模型先验知识作为骨干网络可在该任务上取得高性能，并验证了手部可见性检测器在通过 2D 关键点多视角三角化进行 3D 手部姿态标注的下游任务中的有效性。It is shown that leveraging the prior knowledge of HPE models pretrained on large-scale data as a backbone yields high performance in this task, and the utility of Hand Visibility Detector on a downstream task of 3D hand pose annotation via multi-view triangulation of 2D keypoints.

本文将该挑战形式化为叙事承诺保持 (Narrative Commitment Preservation, NCP)，并提出 NCP-Bench：一个基于电影剧情梗概构建的包含 100 个叙事环境的基准，每个环境均提供可在玩家智能体与叙述者智能体交互过程中自动检查的结构化叙事规范。This work forms this challenge as Narrative Commitment Preservation (NCP), and introduces NCP-Bench, a benchmark of 100 narrative environments derived from movie synopses that each environment includes a structured narrative specification that can automatically check throughout the interaction between the player agent and the narrator agent.

AtlasVLA 是一种新框架，通过持久化的世界-自我状态从直接反应式操作转向主动推理，显著优于多视角基线，在 LIBERO-Long 上取得 9.4% 的绝对成功率提升，在真实世界长周期任务中取得 17.5% 的提升。AtlasVLA is a novel framework that transitions from direct reactive manipulation to proactive reasoning through a persistent world-ego state and decisively outperforms multi-view baselines, yielding absolute success rate improvements of 9.4% on LIBERO-Long and 17.5% in real-world long-horizon tasks.

尽管聚合准确率有所提升，但视觉工具使用在广泛的 rollout 中并不具备因果有效性。Despite aggregate accuracy gains, visual tool-use is not causally effective across a broad range of rollouts: despite aggregate accuracy gains, visual tool-use is not causally effective across a broad range of rollouts.

本文提出进化式马尔可夫超图攻击（EMHA），这是一种黑盒策略，通过协调授权状态转移执行反馈驱动的环境演化，无需参数更新，并将 OpenART 确立为在复杂演化环境中研究 Agent 安全性的可扩展基础。This work proposes the Evolutionary Markov Hypergraph Attack (EMHA), a black-box policy that performs feedback-driven environment evolution by coordinating authorized state transitions without requiring parameter updates, and establishes OpenART as a scalable foundation for studying agent safety in complex, evolving environments.

Mechanist 是一个 agentic 系统，将 AI 作为科学仪器，用于自主发现 AI 内在机制，揭示模型如何表征世界知识、形成信念、推断他人信念，以及这些机制如何在预训练中涌现。Mechanist is an agentic system that uses AI as a scientific instrument for the autonomous discovery of mechanisms underlying AI, revealing how models represent world knowledge, form beliefs, infer the beliefs of others, and how these mechanisms emerge during pretraining.

两项研究为 GPU Agent 控制建立了两个可度量的门槛：deadline 可达的 cohort 供给与观测放置，并使用固定分区份额 F、精确离线份额 P*、局部上界 U 和在线达成份额 A 对 ready-cohort 边界进行了形式化。Two studies establish two measurable gates for GPU agent control: deadline-feasible cohort supply and observation placement and formalize the ready-cohort boundary using fixed-partition share F, exact offline share P*, local upper bound U, and online achieved share A.