论文

提出 HistoRAG，一个将史学原则转化为具体架构干预的框架，为特定领域认识论承诺如何转化为 RAG 设计决策提供模型，并可迁移至其他处理大规模语料的诠释性学科。HistoRAG is introduced, a framework that translates historiographical principles into concrete architectural interventions and offers a model for how domain-specific epistemological commitments can be translated into RAG design decisions, and may transfer to other interpretive disciplines working with large corpora.

实现了一个 RAG 框架，包含面向 LLM 的错误追踪与反例生成以提供更优上下文，并兼容最新版 Isabelle 与 Sledgehammer 以提升效率。A Retrieval-Augmented Generation framework, Error tracing and counterexample generation for improved context supplied to the Large Language Model, and Compatibility with the latest version of Isabelle and Sledgehammer is implemented for improved efficiency.

提出 HyGRAG，一种分层图 RAG 框架，通过解决三个核心挑战超越源文档限制：构建真正融合上下文与关系信息的摘要、利用这些综合表示在检索阶段访问涌现知识、以及为动态语料高效更新分层结构。HyGRAG is proposed, a hierarchical graph RAG framework that transcends source documents by addressing three core challenges: constructing summaries that genuinely integrate contextual and relational information, leveraging these synthesized representations to access emergent knowledge during retrieval, and efficiently updating hierarchical structures for dynamic corpora.

提出一种基于优化的方法，通过精炼现有内容排序来提升社交媒体信息流中新闻内容的可信度；同时构建一条鲁棒的半自动化流水线，基于检索增强打分与人工事实核查的混合方式为内容赋予可信度分数。An optimization-based method to improve the credibility of news content on social media feeds by refining existing content rankings is presented and a robust semi-automated pipeline for assigning credibility scores to content based on a mixture of retrieval-augmented score assignments and human-generated fact-checks is proposed.

介绍 Stanford EDGAR Filings Dataset（SEFD），一个将 SEC filings 开放重建为版面保真 MultiMarkdown 的数据集，用于金融语言建模与评估；同时推出两个基于 SEFD 的基准：EDGAR-Forecast，用于评估模型知识截止后基于 filings 的数值预测；EDGAR-OCR，用于评估复杂金融表格的转录质量。The Stanford EDGAR Filings Dataset (SEFD), an open reconstruction of SEC filings into layout-faithful MultiMarkdown for financial language modeling and evaluation, is introduced and two SEFD-derived benchmarks are introduced: EDGAR-Forecast, which evaluates filing-grounded numerical forecasting after model knowledge cutoffs, and EDGAR-OCR, which evaluates transcription of complex financial tables.

结果表明，在基于 MCP 的 Agent 中，来源归因是事实性验证的一个独立维度；提出 ProvenanceGuard，一种针对 MCP 依据回答的来源感知验证器。Results show that source attribution is an independent axis for factuality verification in MCP-based agents, and ProvenanceGuard, a source-aware verifier for MCP-grounded answers is introduced.

本文介绍 Cordon，一个事务性运行时系统，用于在提交前暂存并验证 Agent 的不可逆操作；其在保持良性任务完成的同时降低不可逆操作失败率，且仅带来适度的审批与时延开销。This paper introduces Cordon, a transactional runtime system for staging and validating irreversible agent effects before commit and reduces irreversible-effect failures while preserving benign task completion with modest approval and latency overhead.

提出一种针对 LLM Agent 的自动化提示优化框架，将"观测到动作"流水线分解为目标条件描述子 Agent 与动作选择 Agent，并通过由 LLM 驱动、以环境回报为指导的进化循环迭代优化各模块提示。An automated prompt optimization framework for LLM agents that decomposes the observation-to-action pipeline into a goal-conditioned descriptor agent and an action selection agent, and iteratively refines each module's prompt through an LLM-driven evolutionary loop guided by environment returns is introduced.

本文提出 MedEasy，一个多 Agent 系统，通过患者对话、临床操作、决策提交、文档记录与反馈来组织虚拟患者练习，为使用案例特定标准连接情境化实践的 AI 辅助职业训练系统贡献了设计启示。MedEasy is presented, a multi-agent system that organizes virtual-patient practice through patient dialogue, clinical actions, decision submission, documentation, and feedback that contributes design implications for AI-supported professional training systems that use case-specific standards to connect situated practice.

本文提出一个面向规则驱动查询改写的自进化框架，无需任何参数训练即可增强 BM25，并揭示 LLM 利用先前实验结果的能力以及其对规则消除的内在知识，在通过自进化精炼规则集方面起到关键作用。This work proposes a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training, and reveals that LLM's capabilities to leverage previous experimental results and its intrinsic knowledge of rule elimination play critical roles in refining the rule set via self-evolution.

RL-Index 被提出，是一个将检索索引推理建模为强化学习问题的索引框架，能持续提升检索与下游问答性能，同时显著降低在线推理延迟。RL-Index is proposed, an indexing framework that formulates retrieval index reasoning as a reinforcement learning problem that consistently improves both retrieval and downstream question-answering performance, while significantly reducing online inference latency.

本文认为，Agentic AI 的近期价值不在于完全自主或削减人力，而在于面向简单与中等复杂度业务流程的受控部分自主。It is argued that the near term value of Agentic AI does not lie in full autonomy or workforce reduction, but in controlled partial autonomy for simple and medium complexity business processes.

本文提出 MAGE-RAG，一个面向长文档多模态问答的多粒度自适应图证据框架，并建立了涵盖 Direct MLLM、Text RAG、Page-level Visual RAG 与 Graph/Agentic RAG 的统一比较与分析协议。This paper proposes MAGE-RAG, a multigranular adaptive graph evidence framework for long-document multimodal QA, and establishes a unified comparison and analysis protocol covering Direct MLLM, Text RAG, Page-level Visual RAG, and Graph/Agentic RAG.

本文理论上证明，归一化离散 Ricci Flow 可通过识别边权中的不同渐近行为来检测社区结构，并支持移除相对于 query 节点具有大权重与负 Ricci 曲率的"噪声"文档片段。It is theoretically prove that normalized discrete Ricci flow can detect community structures by identifying distinct asymptotic behaviors in edge weights, and supports the removal of ``noisy''document chunks characterized by large weights and negative Ricci curvature relative to the query node.

CQC-RAG 框架被提出，它协同设计查询级多样性注入与跨查询一致性评估，无需外部监督即可实现自我评估，验证了跨查询一致性在过滤噪声引发幻觉方面的有效性。CQC-RAG, a framework that co-designs query-level diversity injection with cross-query consistency evaluation and enables self-evaluation without external supervision, is introduced, validating the effectiveness of cross-query consistency for filtering noise-induced hallucinations.

AgentOdyssey 被提出，这是一个新颖的评估框架，通过程序化方式生成包含丰富实体、世界动态和长视野任务的开放式文本游戏，并发现短期记忆对多种智能体范式均有益，是智能体测试时训练的重要组成部分。AgentOdyssey is introduced, a novel evaluation framework that procedurally generates open-ended text games with rich entities, world dynamics, and long-horizon tasks and finds that short-term memory benefits multiple agent paradigms and is an important component of agent test-time training.

结果表明，交易历史中编码的行为知识可以被语言模型有效学习，为客户数字孪生和行为模拟提供了可扩展的基础。The results demonstrate that behavioral knowledge encoded in transaction histories can be effectively learned by language models, providing a scalable foundation for customer digital twins and behavior simulation.

提出 Single-rollout Asynchronous Optimization（SAO），用于解决异步 RL 中的稳定性与 off-policy 难题，可稳定训练一千步，并在 Agentic 编码与推理基准上一致优于 GRPO 及其变体。Single-rollout Asynchronous Optimization (SAO) is presented to address the stability and off-policy challenges in asynchronous RL and is able to train stably for one thousand steps and consistently outperform GRPO and its variants on agentic coding and reasoning benchmarks.

提出 Sparse Delta Memory，一种通过稀疏寻址方案将门控线性 RNN 隐状态容量扩展数个数量级的架构，在上下文学习与长上下文检索任务上显著提升性能。Sparse Delta Memory is introduced, an architecture that scales the hidden state of gated linear RNNs to orders of magnitude higher capacity using a sparse addressing scheme and significantly improves performance on in-context learning and long-context retrieval tasks.

提出 RoboTALES，一个学习任务对齐模拟未来并据此训练机器人策略的单阶段框架，引入两项关键创新：基于 LLM 的分层规划器，将复杂任务拆解为子目标序列以引导模型的"想象"；基于 VLM 的评判器，用于评估这些"想象"出的未来。This work proposes RoboTALES, a single-stage framework that learns task-aligned simulated futures and uses them to train robot policies and introduces two key innovations: a hierarchical LLM-based planner that breaks complex tasks into a sequence of subgoals to guide the model's imagination and a VLM-based critic that evaluates these ``imagined'' futures.

一种以 Token 为中心的双视图学习框架，在冻结的视觉 Transformer 主干中统一基于 prompt 的适配与跨视图融合，相比线性探测、仅 prompt 适配以及传统融合基线均取得稳定提升。A token-centric dual-view learning framework that unifies prompt-based adaptation and cross-view fusion within a frozen vision transformer backbone and demonstrates consistent improvements over linear probing, prompt-only adaptation, and conventional fusion baselines.

提出 OmniTacTune，一种策略无关的真实世界 RL 流程，通过残差修正将触觉反馈适配到预训练视觉策略，并在多种接触丰富任务、视觉基础策略与触觉表征间实现泛化。OmniTacTune is introduced, a policy-agnostic real-world RL pipeline that adapts tactile feedback to pretrained visual policies through residual correction and generalizes across diverse contact-rich tasks, visual base policies, and tactile representations.

给出一个具体且有实证支撑的像素级 EO 基础模型扩展方案：训练大型 encoder，按下游性能筛选，再蒸馏为灵活的学生模型。A concrete, empirically grounded recipe for scaling pixel-wise EO foundation models: train large encoders, select by downstream performance, and distil into flexible student models is given.

[摘要] Splash 是一个面向 MLLMs 的掩码隔离触觉对齐学习框架，它量化每个预训练参数的重要性，并将参数空间划分为休眠子空间与关键子空间，从而有效防止灾难性遗忘，确保非破坏性的模态扩展。Splash is presented, a mask-isolated tactile alignment learning framework for MLLMs that quantifies the significance of each pretrained parameter, and partitions the parameter space into a dormant and critical subspace, which effectively prevents catastrophic forgetting and ensures non-destructive modality expansion.

提出 TokenWall，一种作用于 agent token 流的语义防火墙式运行时防御框架，证明语义运行时约束可在持久化 AI agents 上实现实用的安全性与效用性权衡。TokenWall is proposed, a runtime defense framework that acts as a semantic firewall over agent token flows, demonstrating that semantic runtime containment can achieve a practical security-utility trade-off for persistent AI agents.

提出 Contextuality（上下文性）——即 AI 系统自主访问用户累积知识资本的程度——作为 AI 介导不平等的一个维度，补充但不可化约为 Sharp 等人的框架。Contextuality -- the degree to which an AI system autonomously accesses a user's accumulated knowledge capital -- is proposed as a dimension of AI-mediated inequality that complements, but is not reducible to, the Sharp et al. framework.

本文提出一种面向历史数字图书馆管理的文档分析系统，支持即时知识建模，并促进生成更丰富、更全面的信息。This article presents a document analysis system designed for the management of historical digital libraries that supports on-the-fly knowledge modeling and facilitates the generation of richer and more comprehensive information.

PolyUQuest 是一个基于异构图构建的可验证、结构感知 Web RAG 框架，统一了页面间超链接拓扑、页面内 DOM 层级以及跨页面实体-关系知识，在答案正确性、覆盖度和忠实度上优于现有 RAG 系统，且每次查询消耗的 LLM tokens 显著更少。PolyUQuest, a verifiable, structure-aware web RAG framework built on a heterogeneous graph that unifies hyperlink topology between pages, DOM hierarchy within pages, and entity-relation knowledge across pages, outperforms existing RAG systems in answer correctness, coverage, and faithfulness, while consuming significantly fewer LLM tokens per query.

CineMobile 采用三重优化策略，通过蒸馏引导的剪枝方法得到一个紧凑而高效的模型，保留实现电影级效果所需的核心视频生成能力，证明了其在移动端图像到视频创作中的实用可行性。CineMobile adopts a three-fold optimization strategy, leveraging a distillation-guided pruning approach to derive a compact yet efficient model that retains the essential video generation capabilities required for cinematic effects, demonstrating its practical applicability for mobile-based image-to-video creation.

本文提出 LongE2V，一种利用预训练视频扩散先验来联合处理基于事件视频重建、预测与帧间插值的新方法，并引入自回归展开与自适应上下文切换机制，以缓解超长序列中的时序漂移问题。This work proposes LongE2V, a novel approach that leverages pre-trained video diffusion priors to jointly handle event-based video reconstruction, prediction, and frame interpolation, and introduces Autoregressive Unrolling and Adaptive Context Switching to mitigate temporal drift in extremely long sequences.

借助强大的全景先验，Canvas360 构建了一个统一的上下文全景生成框架，通过 token 级拼接支持多样化下游任务，在任务覆盖范围与建模灵活性上均超越已有方法。Empowered by strong panoramic priors, Canvas360 enables a unified in-context panoramic generation framework that supports diverse downstream tasks via token-level concatenation, surpassing prior methods in both task coverage and modeling flexibility.

本文对 softmax 注意力与四种近期的循环线性注意力架构（DeltaNet、Gated DeltaNet、Kimi Delta Attention 与 Gated DeltaNet-2）进行对比研究，明确阐述它们在表达能力、记忆衰减、擦写控制、训练吞吐量与实现复杂度上的差异。A comparative study of softmax attention and four recent recurrent linear-attention architectures: DeltaNet, Gated DeltaNet, Kimi Delta Attention, and Gated DeltaNet-2 is presented, making explicit how they differ in expressivity, memory decay, erase and write control, training throughput, and implementation complexity.

消融实验表明，选择性干预优于被动记忆库暴露、常驻注入、仅顾问引导和通用检索。Ablations show that selective intervention outperforms passive bank exposure, always-on injection, advisor-only guidance, advisor-only guidance, and general retrieval, and general retrieval and that selective intervention outperforms passive bank exposure, always-on injection, advisor-only guidance, and general retrieval.

SAM-MT 成功将延迟与目标数量解耦，在保持 SAM2 鲁棒视频分割性能的同时，实现了与单目标基线相当的实时速度。SAM-MT successfully decouples latency from the number of targets, achieving real-time speed on par with single-target baselines while maintaining SAM2's robust video segmentation performance.

提出 PAST-TIDE，在官方排行榜上 Subtask A 取得 0.75 的 macro-F1，Subtask B 取得 0.74，表明对预训练模型仅做极少的架构改动即可在低资源场景下保持竞争力。PAST-TIDE is introduced, PAST-TIDE achieves macro-F1 scores of 0.75 for Subtask A and 0.74 for Subtask B on the official leaderboard, indicating that minimal architectural additions to a pre-trained model can remain competitive in low-resource settings.

通过将疾病特异性上下文整合到分子生成中，DrugGen-2 推动了 AI 辅助药物发现，为 de novo 设计和药物再利用提供了强大工具，可同时考虑疾病与分子靶点之间的复杂相互作用。By integrating disease-specific context into molecular generation, DrugGen-2 advances AI-assisted drug discovery, offering a powerful tool for de novo design and drug repurposing that accounts for the complex interplay between diseases and molecular targets.