论文

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.

本文将 S2G-RAG 的结构化充分性-缺口判断适配到冻结的 Search-R1 流程中，并在来自 900 个不相交 HotpotQA 问题的 3009 个状态上训练了一个 Qwen3.5-2B judge，以减少检索次数同时广泛保持答案准确性。This work adapts S2G-RAG's structured sufficiency-and-gap judgment to a frozen Search-R1 pipeline and trains a Qwen3.5-2B judge on 3,009 states from 900 disjoint HotpotQA questions to reduce retrieval while broadly preserving answer accuracy.

本文提出 Syfer，一种用于多语言多跳问答的 synthesizer-folding 框架，默认推迟翻译而非直接应用翻译，在保持具有竞争力准确性的同时，在性能与计算成本之间取得良好平衡。The method Syfer is introduced, a synthesizer-folding framework for multilingual multi-hop question answering that defers translation rather than applying it by default and attains competitive accuracy while striking a favourable balance between performance and computational cost.

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.

在多个 benchmark 数据集上的实证评估表明，RAEF 在准确率和推理开销方面均优于 RAF，并且与零样本及微调基础模型的全面对比显示，RAEF 在避免微计算负担的同时取得了与微调相当或更优的性能。Empirical evaluation across multiple benchmark datasets demonstrates that RAEF outperforms RAF in both accuracy and inference overhead, and comprehensive comparisons with zero-shot and fine-tuned foundation models show that RAEF achieves competitive or superior performance to fine-tuning while avoiding its computational burden.

提出 GRIP（Grounded Reasoning via Information-Restricted Premises），引入容量不对称：decoder 对 query 保持全维度访问，而检索到的证据则通过一个严苛的随机瓶颈，迫使证据通道仅编码 query 中无法获得的残余信息。GRIP (Grounded Reasoning via Information-Restricted Premises), which imposes capacity asymmetry: the decoder keeps full-dimensional access to the query, while retrieved evidence passes through a severe stochastic bottleneck, which forces the evidence channel to encode only the residual information unavailable from the query.

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.