论文

CheckRLM：通过 RAG 及时校验并修正事实错误的框架，有效提升推理过程的可靠性，大幅超越现有基线。CheckRLM is a framework that improves the reliability of the reasoning process through Retrieval-Augmented Generation (RAG) by timely checking and correcting factual errors, and substantially outperforms existing baselines.

本文提出基于检索增强生成的多模态大学聊天机器人，将 LLM 与语义检索相结合，从以学校为中心的资源（如大学手册）中生成基于上下文的回复。This work presents the multimodal university chatbot with retrieval-augmented generation, which combines the large language model with semantic retrieval to produce context-based responses from institution-centric resources, such as the university handbook.

提出 Logit 贡献度评分（LOCOS），一种可感知写入的检测器，通过将每个注意力头的 OV 电路输出投影到答案 token 的去嵌入方向进行打分，在单次前向传播中对比 needle 与非 needle 源位置。Logit-Contribution Scoring (LOCOS) is introduced, a write-aware detector that scores each head by the projection of its OV-circuit output onto the answer-token unembedding direction, contrasting needle and off-needle source positions in a single forward pass.

仅启用记忆（不修改模型的任务-动作行为）即可将基础 Agent 性能提升约 2–4 倍，使 32B 开源权重模型具备与 Claude Opus 4.5、Gemini 3.1 Pro Thinking 等前沿系统相竞争的能力。Opting memory alone--without modifying the model's task-action behavior--improved the base agent's performance ~2x-4x, bringing a 32B open-weight model competitive with frontier systems such as Claude Opus 4.5 and Gemini 3.1 Pro Thinking.

结果表明，在重建频繁或高维场景中（如 multiprobe grid 等）基于网格的方法可能具有竞争力，因为这些场景下索引成本与维度鲁棒性决定性能。The results suggest that grid-based methods such as multiprobe grid may be competitive in rebuild-heavy or high-dimensional settings where indexing cost and dimensional robustness dictate performance.

AdaTrans 是一个通过三大核心机制解决 C 代码到 Rust 自动转换的框架：策略驱动的检索增强生成（RAG）机制，用于将编译器错误映射到具体修复；错误分层转换策略（ESTS），可根据错误类型自适应调整行为；以及多阶段验证流水线，以确保可编译性与功能等价性。AdaTrans is a framework that addresses the automated transformation of C code to Rust through three core mechanisms: a Strategy-Driven Retrieval-Augmented Generation (RAG) mechanism to map compiler errors to specific repairs, an Error-Stratified Transformation Strategy (ESTS) that adapts its behavior based on error types, and a multi-stage validation pipeline to ensure both compilability and functional equivalence.

AGE 专注于预测关键节点以外的节点，采用可学习节点采样器，在基于非参数检索组件的 GraphQA 任务上取得显著提升，在四个具有不同特征的基准数据集上均达到更高的准确率。AGE focuses on predicting nodes apart from key nodes, utilizing a learnable node sampler, and significantly improves approaches using non-parametric search component in GraphQA tasks, achieving superior accuracy across four benchmark datasets with distinct characteristics.

本文提出 SHIFT，一种新颖的框架，将神经元级修改重构为可学习的门控调制，使 LLM 能够自适应地调节内部激活以解决知识冲突。SHIFT is introduced, a novel framework that reformulates neuron-level modification as learnable gate modulation, allowing LLMs to adaptively regulate internal activations for knowledge conflict resolution, allowing LLMs to adaptively regulate internal activations for knowledge conflict resolution.

提出 ZooClaw-FashionSigLIP2——一款面向时尚领域的专用 SigLIP2-base 模型，以简洁方案化解该权衡，性能上优于 LoRA、更大骨干网络以及外部训练数据。ZooClaw-FashionSigLIP2, a fashion-specialized SigLIP2-base model that resolves this tradeoff with a simple recipe and outperforms LoRA, larger backbones, and external training data, is presented.

核心结论是"过时事实错误率"：在被要求作答时，RAG 有 15%–40% 的概率输出已被取代的旧值；MemStrata 将该比率降至约 0%，而这一类失效是 RAG 本身无法规避的。The central result is the stale-fact-error rate: when required to answer, RAG serves superseded values 15-40% of the time; MemStrata drives this to ~0%, a failure class RAG cannot avoid.

提出一种面向 LCA 解释的视角条件化检索增强生成框架，在 AI 辅助的 LCA 中引入多视角检索与受控合成，以支持超出传统 LCA 研究的、面向落地的决策。A perspective-conditioned retrieval-augmented generation framework for LCA interpretation, where a multi-perspective retrieval and controlled synthesis is incorporated in the artificial intelligence (AI)-assisted LCA to support implementation-oriented decision-making beyond conventional LCA studies.

本文提出 TRACE——一种通过 token 影响归因追踪答案相关 token 来识别投毒攻击的轻量检测框架；该方法首先发现跨检索文档的反复出现的高影响关键词，再经二次验证确认其对模型预测的影响。TRACE is presented, a lightweight detection framework that identifies poisoning attacks by tracing answer-related tokens through token influence attribution, and first discovers recurrent high-influence keywords across retrieved documents and then performs a secondary verification to confirm their influence on model predictions.

本文全面审视集中式、设备端、联邦与混合范式下 RAG 系统的隐私与安全挑战，并勾勒出构建可信、安全、韧性 RAG 系统的开放性研究挑战。A comprehensive examination of privacy and security challenges across RAG systems deployed in centralized, on-device, federated, and hybrid paradigms is provided and open research challenges toward building trustworthy, secure, and resilient RAG systems are outlined.

提出一个简洁而高效的检索增强生成框架用于文生艺术图像任务，将艺术检索机制与基于 LoRA 的模型微调相结合，能够生成与用户输入高度匹配的艺术作品，性能显著优于现有方案。A simple yet efficient retrieval augmented generation framework for text-to-artistic image generation by integrating an art retrieval mechanism with LoRA-based model fine-tuning, which can generate artworks that closely match the user's input, significantly outperforming existing solutions.

概述了通过 LLM 进行空间推理所面临的挑战，并展望了搜索引擎与 LLM 集成、通过图增强推理来回答复杂空间问题的未来。The challenges associated with spatial reasoning through LLMs are outlined and a future in which search engines integrate with LLMs to answer complex spatial questions through graph-enhanced reasoning is envisioned.

本文提出一种将 LLM 与检索增强生成（RAG）相结合的混合方法，用于自动化跨版本 Qiskit 代码迁移，验证了这种以数据为中心的方法在促进技术独立性和提供缓解 API 过时问题的鲁棒智能助手方面的潜力。A hybrid approach integrating LLMs with Retrieval-Augmented Generation (RAG) to automate the migration of Qiskit code across versions and confirms the potential of this data-centric methodology to foster technological independence and provide robust, intelligent assistants that mitigate API obsolescence.

HACD-H 为建模自适应人-AI 社交交互与开发社交智能 AI 系统提供了统一的理论基础，并表明社交智能源自长期社交认知的协同进化，而非孤立的对话能力。The HACD-H provides a unified theoretical foundation for modeling adaptive human-AI social interaction and developing socially intelligent AI systems and suggests that social intelligence emerges from long-term social cognitive coevolution rather than isolated conversational capabilities.

提出 MCompassRAG，一种由元数据引导的检索框架，将主题级信号作为语义罗盘以选择相关证据，在同一 embedding 空间中以主题元数据丰富 chunk 表示，并通过 LLM 教师蒸馏训练轻量级检索器。MCompassRAG is introduced, a metadata-guided retrieval framework that uses topic-level signals as a semantic compass for selecting relevant evidence and enriches chunk representations with topic metadata in the same embedding space and trains a lightweight retriever through LLM-teacher distillation.

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.

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.

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.

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.

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.

一项受控消融实验揭示了机制：从检索到的文档中移除特定实体的临床证据，可彻底消除实体归因失败，使所有失败转移到虚构生成。A controlled ablation identifies the mechanism: removing entity-specific clinical evidence from retrieved documents eliminates entity-attribution failure entirely, shifting all failures to confabulation.

论文探讨了 LLM 为公司基本面分析各方面带来的机会，分析依据包括公司报告、描述宏观经济状况（如 GDP 和通胀变化）的数据与文件，以及提交至美国证券交易委员会（SEC）的文件。The opportunities brought by Large Language Models (LLMs) to various aspects of fundamental analysis of companies based on their reports as well as data and documents describing macroeconomic situation like GDP and inflation changes as well as documents filled to the U.S. Securities and Exchange Commission (SEC) are examined.

提出 EvoGraph-R1，一个自演化 GraphRAG 框架，将知识图谱重新概念化为由 Agent 交互塑造的动态环境，将自演化知识图谱确立为跨模态的基础范式。EvoGraph-R1 is introduced, a self-evolving GraphRAG framework that reconceptualizes knowledge graphs as dynamic environments shaped through agent interactions, establishing self-evolving knowledge graphs as a fundamental paradigm across modalities.

本文提出 Earthquaker-AI，一个混合式教育框架，在已有教育机器人项目基础上集成基于 RAG 的对话式 AI 助手，旨在提升小学生的地震应急准备与主动行动意识。该系统将曾获奖的 STEM 项目 Earthquaker 从 Lego WeDo2 的机械模拟拓展至认知与元认知层面：机器人组件利用 Lego WeDo2 自动化模拟地震响应，使学生能够与传感器和执行器进行交互。This paper presents Earthquaker-AI, a hybrid educational framework building upon a previously implemented educational robotics project by integrating a conversational AI assistant based on Retrieval-Augmented Generation. It aims to enhance earthquake preparedness and conscious action among primary-school students. The system extends the award-winning STEM project Earthquaker moving from mechanical simulation with Lego WeDo2 to cognitive and metacognitive processing. The robotics component uses Lego WeDo2 automation to simulate seismic response, letting students interact with sensors and actuat

提出 GRASP，一个用于训练智能体在多步推理过程中自适应协调互补检索工具的强化学习（RL）框架，并指出学会协调检索信号与上下文粒度对智能体的正确推理至关重要。GRASP is introduced, a reinforcement learning (RL) framework for training agents to adaptively coordinate complementary retrieval tools during multi-step reasoning, and it is suggested that learning to coordinate retrieval signals and context granularity is critical for agent's correct reasoning.

提出 Chat2Scenic，是首个以领域特定语言 (DSL) 生成场景脚本的迭代式检索增强框架，并构建了一个涵盖 NHTSA、联合国车辆法规及其他来源共 123 个场景的开源场景生成基准。Chat2Scenic is presented, the first iterative retrieval-augmented framework to generate scenario scripts in Domain Specific Language (DSL) and proposes an open benchmark for scenario generation comprising 123 scenarios from various regulations, including NHTSA and United Nations Vehicle Regulations, as well as other sources.

RAGU是一个开源模块化GraphRAG引擎，通过将抽取与整合分离来解决抽取-整合问题：实体和关系经过两阶段类型化抽取、基于DBSCAN的去重、LLM摘要和Leiden社区检测。RAGU, an open-source modular GraphRAG engine, addresses extraction from consolidation by separating extraction from consolidation: entities and relations pass through two-stage typed extraction, DBSCAN-backed deduplication, LLM summarization, and Leiden community detection.

本文介绍了 NOWJ 团队参加 COLIEE 2026 全部五项任务的方法与结果，采用基于稠密检索、注意力重排序和小样本提示 LLM 推理的检索增强生成框架。This paper presents the methodologies and results of the NOWJ team's participation across all five tasks of the COLIEE 2026 competition and adopts a retrieval-augmented generation framework with dense retrieval, attention-based reranking, and few-shot-prompted LLM reasoning.