论文

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.

自组织临界性作为一种具有内在序参数的唯象框架登场，使开放性主张得以转化为可证伪的猜想。Self-organized criticality enters as a phenomenological framework with an intrinsic order parameter; open claims become falsifiable conjectures.

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 智能背后的机制，并发展了关于 belief 的机制理论，揭示了模型如何表征世界知识、形成 belief、推断他人 belief，以及这些机制如何在预训练过程中涌现。Mechanist is an agentic system that uses AI as a scientific instrument for the autonomous discovery of mechanisms underlying AI intelligence, and develops a mechanism theory of belief, 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.

本文提出 SHAPER，一种免训练具身自适应的自演化框架，保持模型参数冻结，通过目标环境 rollout 演化可复用的 skills 和 context-code harness 来改进非参数化 Agent 系统。This work proposes SHAPER, a self-evolving framework for train-free embodied adaptation that keeps model parameters frozen and improves the non-parametric agent system by evolving reusable skills and a context-code harness through target-environment rollouts.

本文提出 Simplax，一种精确的 Dirichlet-类别增强方法，将每个被破坏的类别状态与一个辅助的 simplex 值变量耦合，同时保留原始均匀扩散过程作为其类别边缘分布。Simplax is introduced, an exact Dirichlet--categorical augmentation that couples each corrupted categorical state with an auxiliary simplex-valued variable while preserving the original uniform diffusion process as its categorical marginal.

本文提供了参数空间探索能够改进 LLM 强化学习的证据，并提出称为扰动参数策略优化（3PO）的方法族，使用不同的采样策略和不同的 rollout 分组进行 reward 估计。Evidence that parameter-space exploration can improve reinforcement learning for LLMs is presented, and a family of methods called Perturbed Parameter Policy Optimization (3PO) is introduced which use different sampling strategies and different rollout grouping for reward estimation.

本文提出 SkillZip，一种执行感知的程序化抽象框架，对 section 级图执行保持契约的压缩，加载一个紧凑、依赖闭合的 context，并仅在需要时展开宏。SkillZip is proposed, an execution-aware procedural abstraction framework that performs contract-preserving compression over section-level graphs that hydrates a compact, dependency-closed context and expands macros only when required.

一个仅依赖字符级输入的简单神经语言模型，仅从字符即可编码语义和正字法信息，表明在许多语言中，字符输入足以完成语言建模。A simple neural language model that relies only on character-level inputs that is able to encode, from characters only, both semantic and orthographic information and suggests that on many languages, character inputs are sufficient for language modeling.

描述了一种无监督学习通用分布式句子编码器的方法，利用书籍文本的连续性，训练编码器-解码器模型以重建编码段落的周围句子。The approach for unsupervised learning of a generic, distributed sentence encoder is described, using the continuity of text from books to train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage.

本工作训练了一个预测的计算最优模型 Chinchilla，使用与 Gopher 相同的计算预算，但参数量为 70B、数据量为 4 倍，达到 SOTA 平均准确率，比 Gopher 提升超过 7%。This work trains a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4$\times$ more more data, and reaches a state-of-the-art average accuracy, greater than a 7% improvement over Gopher.

本工作综述了可用于推断用户需求的贝叶斯用户模型研究，这些模型综合考虑用户的背景、操作和查询，并提出了一种智能用户界面的整体架构。This work reviews work on Bayesian user models that can be employed to infer a user's needs by considering a users' background, actions, and queries and proposes an overall architecture for an intelligent user interface.

探索以交错方式使用 LLM 同时生成推理轨迹和任务特定动作，使两者产生更大协同：推理轨迹帮助模型归纳、跟踪和更新动作计划以及处理异常，而动作使其与外部源交互以获取额外信息。The use of LLMs are explored to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external sources to gather additional information.

本文提出提示式注视目标估计（PGE）任务，一种用于注视分析的端到端、概念驱动新范式，并推出首个为 PGE 设计的模型 GazeAnywhere，它使用基于 Transformer 的检测器融合冻结编码器特征，同时解决主体定位、画内/画外存在性以及注视目标热图估计。The Promptable Gaze Target Estimation (PGE) task is introduced, a new end-to-end, concept-driven paradigm for gaze analysis and GazeAnywhere, the first model designed for PGE, uses a transformer-based detector to fuse features from frozen encoders and simultaneously solves subject localization, in/out-of-frame presence, and gaze target heatmap estimation.

ParliamentRAG 是一种主题相关的权威模型，根据当前 query 估计每位发言者的权威性，结合职业、教育和此前发言等可解释组件，以应对政治敏感文本中最高频发言者主导、无法按主题专长加权发言者以及引用归属错误的风险。ParliamentRAG is a topic-dependent authority model that estimates each speaker's authority as a function of the current query, combining interpretable components such as profession, education, and previous interventions that addresses risks of dominance of the most frequent speakers, inability to weight speakers according to topical expertise, and citation misattribution in politically sensitive text.

本文提出一种用于机器人操作的动作条件视频世界模型，给定观测帧、语言指令以及由末端执行器位姿和夹爪状态组成的预定动作序列，预测对应的未来观测结果。An action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction, and a prescribed action sequence comprising end-effector poses and gripper states, predicts the resulting future observations is presented.

本文提出 UniSwap，这是首个用于说话视频中流式联合音视频身份替换的框架，并引入 swap-and-reconstruct 流程，从真实片段中移除视觉和声音身份，同时使用原始片段作为重建目标。This work presents UniSwap, the first framework for streaming joint audio-visual identity replacement in talking videos, and introduces a swap-and-reconstruct pipeline that removes visual and vocal identity from real clips and uses the original clips as reconstruction targets.

本文提出 LiveAnimate，据作者所知是首个将实时流式生成与十亿参数规模下的稳定长视频生成相结合的系统，基于 140 亿参数的视频 Diffusion Transformer（DiT）。This work presents LiveAnimate, to their knowledge the first animation system to combine real-time streaming with stable long-form generation at billion scale, built on a 14B-parameter video Diffusion Transformer (DiT).

H2R-Bench 提供了一个系统性诊断框架，用于评估视频世界模型能否跨越 human-to-robot 具身差距，并将人类操作观测转化为以机器人为中心的训练资源。H2R-Bench provides a systematic diagnostic framework for evaluating whether video world models can bridge the human-to-robot embodiment gap and convert human manipulation observations into robot-centric training resources.

SKILLER 是一个由自然语言驱动的强化学习框架，旨在为小模型自动生成执行器特定的 skills，使用强模型作为 actor 和 critic，将小模型 Agent 系统视为环境，并通过自然语言完全传递所有强化学习信号。SKILLER is a natural-language-driven reinforcement learning framework designed to automatically generate executor-specific skills for small models, which employs a strong model as the actor and critic, treats the small-model agent system as the environment, and propagates all reinforcement learning signals entirely via natural language.

本文给出了 LLM routing 的统一形式化，将其刻画为由五个组件构成的序贯决策过程：context 编码器、模型编码器、评分函数、决策规则和学习信号，涵盖单轮、多轮和个性化 routing。This work presents a unified formulation of LLM routing as a sequential decision process characterized by five components: context encoders, model encoders, scoring functions, decision rules, and learning signals, covering single-turn, multi-turn, and personalized routing.

本文认为关键在于设计合适的 search-state 表示，将 LLM 的内部知识与 visual-token 剪枝的结构要求和约束相连接，并提出 AutoPrune，一种用于 LLM 驱动的 visual-token 剪枝策略设计的免训练框架。This paper argues that the key lies in designing an appropriate search-state representation that connects the internal knowledge of LLMs with the structural requirements and constraints of visual-token pruning, and proposes AutoPrune, a training-free framework for LLM-driven visual-token pruning policy design.