论文

论文论证了物理先验提供了一种与机器人领域高度相关的归纳偏置，其作用是与数据驱动学习互补而非取代，从而为更通用、数据高效且可信的机器人系统铺平道路。It is argued that physics priors provide a particularly relevant robotics-specific inductive bias, complementing rather than replacing data-driven learning, and paving the way toward more generalizable, data-efficient, and trustworthy robotic systems.

结果支持将生成式记忆视为对直接检索的选择性修正，并强调何时、以何种方式、以何种强度进行路由干预是核心挑战。The results support generated memory as a selective correction to direct retrieval and highlight routing when, which, and how strongly to intervene as the central challenge.

本文提出 ZooWork-ShopRanker，一族对齐到裁判标注购物偏好的电商 reranker，以及 ShopRank-Bench，一个包含约 10,000 条私有流量偏好对的低污染 benchmark，按承诺该标注的裁判家族数量分档呈现，覆盖多种文本格式。ZooWork-ShopRanker, a family of e-commerce rerankers aligned to judge-labeled shopping preference, and ShopRank-Bench, a contamination-limited benchmark of ~10,000 private-traffic preference pairs in both text formats, tiered by how many judge families committed to each label.

QReason 是一种解耦框架，将面向 query 的推理与针对窗口的 passage 相关性评估分离，显著减少冗余推理，在取得与 reasoning-based reranker 相当乃至更优的排序性能的同时，超越了现有的 query rewriting 模型。QReason is a decoupled framework that separates query-focused reasoning from window-specific passage relevance assessment, and significantly reduces redundant reasoning, achieves ranking performance comparable to or better than strong reasoning-based rerankers, and outperforms existing query rewriting models.

SarseX 模型无关、无需训练，并与 Prefix Cache 兼容，可为多轮对话、检索增强生成 (RAG) 和 Agent 工作流等常见在线服务场景提供统一支持。SarseX is model-agnostic, training-free, and compatible with Prefix Cache, and it provides unified support for common online serving scenarios including multi-round chat, retrieval-augmented generation (RAG), and agent workflows.

BoundaryMORPH 是一种新算法，专门为 LLM 的上下文容量 k 分配 CE 预算，在多个模型与数据集上针对开放性查询取得了 SOTA 集合检索质量。BoundaryMORPH is introduced, a novel algorithm that allocates CE budget specifically for the LLM's context capacity $k$ and achieves state-of-the-art set retrieval quality across multiple models and datasets with open-ended queries.

本文提出一个新颖的初步框架，可定量评估输出格式不同（如列表与消息）的检索系统的 IR 准确度，为客观评估基于关键词与基于语义的对话式检索方法奠定坚实基础。This work introduces a novel, preliminary framework to quantitatively assess IR accuracy of search systems that produce different output formats, such as lists and messages, and provides a strong foundation for objectively assessing keyword-based and semantic chat-based search methods.

RAGScope 是一个泄漏受控的评估协议，用于评估仅使用任务输入、检索上下文和答案文本的本地证据门，结合了上下文分组划分、折范围预处理、组自举区间、部署工作点、端到端运行时以及显式的源偏移压力测试。RAGScope, a leakage-controlled protocol for evaluating local evidence gates that use only the task input, retrieved context, and answer text is presented, which combines context-grouped splits, fold-scoped preprocessing, group bootstrap intervals, deployment operating points, end-to-end runtime, and explicit source-shift stress tests.

本文提出一个 Agentic RAG 框架，使 LLM 能够使用逻辑表达式构建检索意图，同时将检索后端简化为基于倒排索引的系统，并表明将检索过程锚定在逻辑查询上可显著降低生成响应中的幻觉。This paper proposes an agentic RAG framework that enables LLMs to formulate retrieval intents using logical expressions while simplifying the retrieval backend to an inverted-index-based system, and shows that anchoring the retrieval process in logical queries substantially reduces hallucinations in generated responses.

提出一种自适应、自动化的数据提取攻击流程，在黑盒设置下针对 MRAG（其中检索到的视觉产物本身就是答案）发起攻击，表明亟需专门面向多模态数据设计的安全防护。An adaptive and automatic data extraction attack procedure operating in a black box setting against MRAG, a configuration in which the retrieved visual artifact is itself the response, and shows the urgent need for safeguards specifically designed for multimodal data.

提出 MMAgent-R$^2$，一种将视觉重排序与主动拒答作为内部验证机制的 Agentic mRAG 框架，并通过 GRPO 训练实现外部检索、内部验证与答案生成的联合优化。MMAgent-R$^2$, an agentic mRAG framework that integrates visual reranking and active rejection as its internal verification mechanism, is proposed and achieves joint optimization of external retrieval, internal verification, and answer generation via GRPO training.

MatRAG 在检索质量上优于其最强的竞争者；同时，通过避免 KG 构建和基于 LLM 的摘要降低了索引成本，并通过维度感知的相似度降低了查询成本。MatRAG outperforms its strongest competitors in terms of retrieval quality; furthermore, it reduces indexing costs by avoiding KG construction and LLM-based summarization, and lowers query-time costs through dimension-aware similarity.

提出一种基于不确定性感知框架的自适应问答方法，通过 LLM 内部表征中区分知识不足与知识歧义/冲突的显式信号，在单次前向传播中即可由隐状态高效估计。This work proposes an uncertainty-aware framework for adaptive QA based on explicit signals derived from LLM internal representations that distinguish between knowledge insufficiency and knowledge ambiguity or conflict, and efficiently estimate these from hidden states in a single forward pass.

在 Encyclopedic-VQA 和 InfoSeek 上的实验表明，CLIMB 持续优于基于检索增强的多模态基线；消融实验显示互补池化、基于评论家的打分以及迭代置信度控制的精炼各自对最终性能均有贡献。Experiments on Encyclopedic-VQA and InfoSeek show that CLIMB consistently improves over retrieval-augmented multimodal baselines, andlations indicate that complementary pooling, critic-based scoring, and iterative confidence-controlled refinement each contribute to the final performance.

提出一种"失败即关闭"的临时可见性协议：在截止时间前接纳新内容，隐藏验证未及时提交的项目，并支持按谱系范围的遏制，同时量化新鲜度与可用性之间的权衡。A fail-closed provisional-visibility protocol is presented that admits new content under a deadline, hides items whose verification has not committed in time, and supports lineage-scoped containment, and quantifies the freshness and availability trade-offs.

DynaKRAG 是一种统一的证据-动作框架,通过学习共享的状态条件策略来协调上述操作,证明统一的状态条件证据控制能在保证强回答质量的同时,实现高效检索与紧凑的答案生成上下文。DynaKRAG, a unified evidence-action framework that learns a shared state-conditioned policy for coordinating these operations, demonstrates that unified, state-conditioned evidence control supports strong answer quality, efficient retrieval, and compact answer-generation contexts.

提出一种新颖的进化系统，专注于利用 RAG 增强的 LLM 自动生成空域隐写的代码级代价函数；该方法在隐写安全性上一致优于现有自动设计方法，同时提高了平均代码执行率并降低了搜索成本。A novel evolutionary system focused on exploiting Retrieval-Augmented Generation enhanced LLMs for the automatic code-level generation of spatial steganography cost functions, which consistently achieves higher steganographic security than existing automatically designed methods and increases the average code execution rate while reducing the search cost.

该工作提出了一个端到端的档案处理与检索框架，将大语言模型（LLM）集成到档案流程中，并证明将 LLM 与成熟的文档处理与检索流程相结合，可将数字图书馆从静态存储库提升为可交互、可语义检索的档案系统。This work presents an end-to-end archival processing and retrieval framework that integrates large language models (LLMs) into the archival pipeline and demonstrates that integrating LLMs with established document processing and retrieval pipelines can elevate digital libraries from static repositories to interactive, semantically searchable archival systems.

将预测的味觉空间作为基于内容的检索索引，对 309 项条目池的排序比 CLAP-text 基线（处于随机水平）忠实得多；ridge probes 与 audio-bandstop knockout 在已记载的声-味对应关系上读出了最强表征。Operationalised as a content-based retrieval index, the predicted taste space ranks a 309-item pool far more faithfully than a CLAP-text baseline, which sits at chance; ridge probes and an audio-bandstop knockout read the strongest representations against documented sound-taste correspondences.

本文提出 SOLAR，一种学习增强框架，从 regret 累积中推导修改时机，并基于隐式检索反馈的贝叶斯在线学习进行内容选择，实现与缓存大小和时域无关的常数竞争比。SOLAR is proposed, a learning-augmented framework that derives modification timing from regret accumulation and content selection from Bayesian online learning over implicit retrieval feedback and achieves a constant competitive ratio, independent of cache size and horizon.

提出 MEDIAREF：源自网络文档的公共知识库，支持跨 200 个媒体来源、可复现且低成本的 MBC 生成评估；给出可复现的构建与更新方法，并系统评测主流 LLM 在 MBC 生成任务上的表现。MEDIAREF, a publicly available knowledge store of web-sourced documents that enables reproducible, low-cost evaluation of MBC generation across 200 media sources, is introduced, describing a reproducible methodology for constructing and updating the collection, and assessing widely used LLMs on the MBC generation task.

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.

提出 MIRROR——一个统一的跨表层框架，在显式新颖性约束下以检索到的上下文为条件生成候选，并执行记忆引导的蒙特卡洛树搜索，使检索可影响搜索先验，同时避免提示词级别的复制。MIRROR is presented, a unified cross-surface framework that performs memory-guided Monte Carlo tree search while conditioning candidate generation on retrieved context under an explicit novelty constraint, allowing retrieval to inform search priors without enabling prompt copying.

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.

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.