论文

基于欧盟法院判决构建的大规模法律 IR 数据集，支持法律信息检索方法的开发与严格评估，覆盖多种查询-文档粒度（案例对案例、段落对案例、段落对段落检索）。A large-scale legal IR dataset constructed from Court of Justice of the European Union judgments, which supports both the development and rigorous evaluation of legal IR methods, at multiple query-document levels (case-to-case, paragraph-to-case, and paragraph-to-paragraph retrieval).

配对 MEG 掩蔽实验表明，19 个刺激特征中有 15 个有贡献，其中静音、声音强度、元音和声学起始的影响最大；说明缺乏叙事结构的神经活动相比连贯语音下的活动，可恢复的信息更少。Paired MEG occlusion shows that 15 of 19 stimulus features contribute, with the largest effects for silence, sound intensity, vowels, and acoustic onsets, indicating that activity without narrative structure carries less recoverable information than activity during coherent speech.

本工作提出一种通过引入自注意力来提取可解释句子嵌入的新模型，使用一个二维矩阵表示嵌入，其中矩阵的每一行关注句子的不同部分。A new model for extracting an interpretable sentence embedding by introducing self-attention is proposed, which uses a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence.

提出 READ（Reliable Embedding-free Agentic Document-search）：Agent 通过三种确定性操作——归一化词法搜索、结构导航与有界片段读取——直接读取原始文档，这些操作通过 Model Context Protocol 暴露，使轨迹成为可回放的审计轨迹，而非不透明相似度分数。This work proposes READ (Reliable Embedding-free Agentic Document-search), in which an agent reads the raw document through three deterministic operations -- normalized lexical search, structural navigation, and bounded span reads -- exposed over the Model Context Protocol, so a trajectory is a replayable audit trail, not an opaque similarity score.

本文提出 Atlas，一个经过精心设计并预训练的检索增强大语言模型，能以极少训练样例学习知识密集型任务，并研究了文档索引内容的影响，表明该索引可便捷地更新。This work presents Atlas, a carefully designed and pre-trained retrieval augmented language model able to learn knowledge intensive tasks with very few training examples, and studies the impact of the content of the document index, showing that it can easily be updated.

本文在低 prefill 延迟约束下优化 Pareto 前沿以最大化精度，提出 CoinRAG（Contextualized Information Nugget KV Cache Reuse for Long-Context RAG），通过 chunk 级上下文无缝拼接其切片化的 KV 表示。This paper optimizes the Pareto frontier under low prefill latency constraints while maximizing accuracy by proposing CoinRAG (Contextualized Information Nugget KV Cache Reuse for Long-Context RAG), which seamlessly assembles their sliced KV representations with a chunk-level context.

本文提出 Exact Adaptive Hybrid Retrieval（EAHR），将完整列表加权 RRF 定义的有序 Top-K 固定为检索目标，并将通道深度视为请求特定的执行状态，在全部 150 组 query-snapshot 组合中复现了完整列表的有序 Top-20。This work proposes Exact Adaptive Hybrid Retrieval (EAHR), which fixes the ordered Top-$K$ defined by complete-list weighted RRF as the retrieval target and treats channel depth as request-specific execution state and reproduced the complete-list ordered Top-20 in all 150 query-snapshot combinations.

本文通过深度多模态嵌入视觉与自然语言数据，提出了一种用于图文双向检索的模型，并引入结构化的最大间隔目标，使该模型能够显式地跨模态关联片段。This work introduces a model for bidirectional retrieval of images and sentences through a deep, multi-modal embedding of visual and natural language data and introduces a structured max-margin objective that allows this model to explicitly associate fragments across modalities.

本文利用源自 CNN 的同一基础信息重新审视初始搜索与重排序两个检索阶段，显著改进了现有基于 CNN 的识别流水线。This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN, and significantly improves existing CNN-based recognition pipeline.

训练一个紧凑分类器，对被省略的句子按其是否为理解保留文本所必需进行排序，并在推理时无需支撑标注地回插排名靠前的候选，同时优化相关性与指代完整性。A compact classifier is trained to rank omitted sentences by whether they are needed to interpret retained text and reinsert the top-ranked candidates without support annotations at inference, training a compact classifier to optimize both relevance and referential completeness.

本文提出 Factorized Hypothesis Search (FHS)，在多个命名语义维度上维护多个部分解释，以支持结构化查询渲染、多假设检索以及维度级候选验证。This work proposes Factorized Hypothesis Search (FHS), which maintains multiple partial interpretations over named semantic dimensions that support structured query rendering, multi-hypothesis retrieval, and dimension-level candidate verification.

本文提出 KGCaRe，一种将神经检索与基于 LLM 生成 KG 的符号推理相结合的混合方法，在 Vanilla LLM、Code Prompt、Text Prompt、Think-on-Graph、Vanilla RAG 和 HybridContextQA 等 baseline 上 consistently 取得更优表现。KGCaRe is proposed, a hybrid approach that combines neural retrieval with symbolic reasoning over LLM-generated KGs that consistently outperforms existing baselines, including Vanilla LLM, Code Prompt, Text Prompt, Think-on-Graph, Vanilla RAG, and HybridContextQA.

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.

本文提出一种自我知识 RAG 框架，引导 LLM 从专利匹配查询中自主提取关键技术实体并构建层次化本体结构，从而实现查询扩展与精确检索。A self-knowledge RAG framework is proposed that guides LLMs to autonomously extract key technical entities and construct hierarchical ontological structures from patent matching queries, thereby enabling query expansion and precise retrieval.

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.

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 RAGSieve，为每个检测范围构建匹配的参考集合，其构建需要投毒标签、可信语料或训练过程。This work presents RAGSieve, which constructs a reference matched to each detection scope, which requires poison labels, a trusted corpus, or training to be constructed.

对八个法律 RAG 系统在 GDPR 和某国国内民法两个法律语料上的幻觉行为进行细粒度分析，发现含有错误假设的 false-premise 问题在人工编写问题上产生高幻觉率。A fine-grained analysis of hallucination behavior in eight legal RAG systems across two legal corpora, the GDPR and a national civil law, finds that false-premise questions, containing incorrect assumptions that must be rejected, produce high hallucination rates on the manually-drafted questions.

将文档结构形式化为多模态超图（Multimodal Hypergraph），以超边作为统一语义容器来封装跨文本、图像和表格的多路关联，超越点对点建模，并引入 Anchor-driven Incremental Refinement 机制。This paper formalizes the document structure as a Multimodal Hypergraph, utilizing hyperedges as unified semantic containers to encapsulate multi-way associations across text, images, and tables, thereby transcending point-to-point modeling and introducing an Anchor-driven Incremental Refinement mechanism.

提出 DSPrompt，一种 Dynamic Soft Prompt 防御框架，无需修改检索 pipeline，直接重塑 retriever 的 embedding 语义，并以极低的计算成本 consistently 优于现有防御基线。DSPrompt is proposed, a Dynamic Soft Prompt defense framework that directly reshapes the retriever's embedding semantics, without modifying the retrieval pipeline, and is consistently outperforming existing defense baselines at a fraction of their computational cost.

提出 Intent-Guided Decoding (IGD)，一个根据用户意图在检索上下文和参数化记忆之间进行仲裁的框架，显著提升了 RAG 中的事实恢复能力。Intent-Guided Decoding (IGD) is proposed, a framework that arbitrates between retrieved context and parametric memory according to user intent and substantially improves factual recovery in RAG.