论文

结果表明，思维轨迹是推理任务的有效检索语料；将其转换为结构化、紧凑化或诊断式表征后，可释放出更强的增益。The results suggest that thinking traces are an effective retrieval corpus for reasoning tasks, and transforming them into structured, compact, or diagnostic representations unlocks even stronger gains.

本文提出 SCAR（Semantic Continuity-Aware Retrieval），一种自适应检索策略，通过权衡查询-邻居相关性与结构连续性惩罚来选择性扩展相邻分块，由此得到近似尺度不变的决策规则，无需重新校准即可跨 embedding 模型迁移。SCAR (Semantic Continuity-Aware Retrieval), an adaptive retrieval policy that selectively expands neighboring chunks by weighing query-neighbor relevance against a structural continuity penalty, is proposed, yielding an approximately scale-invariant decision rule that transfers across embedding models without recalibration.

本文提出 V-RAGBench——一个由 ⟨query, evidence chunk, answer⟩ 三元组构成的基准，可对检索与生成进行忠实且解耦的评估；同时提出 CARVE，一种在多种配置下并行运行检索器、并通过分块自适应重排序为每个分块挑选最优配置的简易方法。V-RAGBench is introduced, a benchmark of $\langle$ query, evidence chunk, answer$\rangle$ triplets that enables faithful, decoupled evaluation of retrieval and generation, and CARVE, a simple method that runs parallel retrievers across configurations and employs chunk-adaptive reranking to identify the winning configuration for each chunk.

本文提出 PathRouter，一种面向 agentic GraphRAG 的路径感知训练框架，沿答案正确性与证据路径重叠度联合评估每条轨迹，归纳出四类轨迹并采用差异化 GRPO 优势缩放，抑制捷径式强化同时保留证据寻求行为。This work presents PathRouter, a path-aware training framework for agentic GraphRAG that jointly evaluates each trajectory along answer correctness and evidence-path overlap, yielding four trajectory categories with differentiated GRPO advantage scaling that suppresses shortcut reinforcement while preserving evidence-seeking behavior.

本文提出 Diverge，一种即插即用的 agentic RAG 框架，通过迭代式、反思引导的多视角探索以及多样性感知检索支持来改善多样性—质量权衡，并引入用于刻画开放域问答中多样性—质量权衡的评估指标。Diverge is proposed, a plug-and-play agentic RAG framework that improves the diversity--quality trade-off through iterative, reflection-guided exploration of diverse viewpoints and diversity-aware retrieval support, and introduces evaluation metrics for characterizing the diversity-quality trade-off in open-ended question answering.

本文提出名为 InSemRAG 的 RAG 框架，通过迭代检索—校验机制及两个支撑模块——意图感知检索器（IAR）与语义保持切分（SPC）——应对上述挑战。This work proposes a RAG framework, termed InSemRAG, that addresses these challenges via an iterative retrieve-and-check mechanism with two supporting modules, an intention-aware retriever (IAR) and semantics-preserving chunking (SPC).

MRAgent，一种将联想记忆图与主动重构机制相结合的框架，将 LLM 推理直接融入记忆访问，确保记忆检索能动态适配推理上下文，同时避免无约束扩展引发的组合爆炸。MRAgent, a framework that combines an associative memory graph with an active reconstruction mechanism that integrates LLM reasoning directly into memory access, ensuring that memory retrieval is dynamically adapted to the reasoning context while avoiding combinatorial explosion caused by unconstrained expansion.

提出一个面向 RAG 的 AI 系统基准测试（RAGPerf）框架，用于刻画 RAG pipeline 的系统行为，并证明其引入的性能开销可忽略不计。The design and implementation of a RAG-based AI system benchmarking (RAGPerf) framework for characterizing the system behaviors of RAG pipelines is presented and it is shown that RAGPerf incurs negligible performance overhead.

本文首次系统分析了 LLM 如何通过强化学习（RL）学习针对不同 retriever 调整 query 表述策略，并揭示 RL 能有效教会 LLM 根据特定 retriever 特性定制 query。This work presents the first systematic analysis of how LLMs can learn to adapt their query formulation strategies for different retrievers via reinforcement learning (RL), and reveals that RL effectively teaches an LLM to tailor its queries to specific retriever characteristics.

本文旨在应对常见挑战并简化 RAG 与既有企业基础设施的集成，提出一套面向可扩展本地化部署企业 RAG 方案的综合性 AI 工程蓝图。This paper aims to address the gap in comprehensive on-premises RAG implementation frameworks by presenting a comprehensive AI engineering blueprint for scalable on-premises enterprise RAG solutions to address common challenges and streamline the integration of RAG into existing enterprise infrastructure.

本文提出 Retrieval-Augmented Speculative Decoding (RAPID)，利用 RAG 在长上下文推理中同时加速并提升生成质量，并设计了一种推理时知识迁移机制，通过 RAG 丰富目标分布。Retrieval-Augmented Speculative Decoding (RAPID) is introduced, which leverages RAG for both accelerating and enhancing generation quality in long-context inference and develops an inference-time knowledge transfer that enriches the target distribution by RAG.

总体而言，分阶段检索的影响往往超过"理想证据存在"本身；本文为专业科学场景下 RAG 系统的部署与诊断提供了实践指导，并为构建更可靠、可控的迭代式检索-推理框架奠定了基础。This is the first controlled, mechanism-level diagnostic evaluation of whether synchronized iterative retrieval and reasoning can surpass even an idealized static upper bound (Gold Context) RAG, and practical guidance for deploying and diagnosing RAG in specialized scientific settings.

引入 D（Domain-Collection-Document），一种面向领域的设计，用于在不修改底层语言模型的前提下组织 RAG 系统中的知识并控制查询处理。D (Domain-Collection-Document), a domain-oriented design to structure knowledge and control query processing in RAG systems without modifying the underlying language model, is introduced.

提出 Tail-Aware Adaptive-k（TAA-k），一个无需训练、通过局部验证策略将 EVT 落地的框架，检索质量接近 oracle，相较全局 EVT 方法获得数量级的效率提升，并在不同 embedding 模型和压缩维度下保持稳健。Tail-Aware Adaptive-k (TAA-k), a training-free framework that operationalizes EVT through a localized validation strategy, is proposed, which achieves near-oracle retrieval quality with orders-of-magnitude efficiency gains over global EVT methods, while maintaining robustness across embedding models and compression dimensions.

分析表明，鲁棒的长期记忆（Long-Term Memory）安全无法仅在 retrieval 或执行阶段后置加固，而必须在最初就以存储阶段的溯源、版本化与策略感知的 retention 为基础进行锚定。This analysis indicates that robust Long-Term Memory security cannot be retrofitted at retrieval or execution time alone, but must be anchored in storage-time provenance, versioning, and policy-aware retention from the outset.

提出 SIFT：Selective-Index For Fast Compute of RAG Prefill by Exploiting Attention Invariance，离线处理文档并提取每个文档中高分注意力的细粒度位置，以两个紧凑的位向量存储这些高分位置。SIFT: Selective-Index For Fast Compute of RAG Prefill by Exploiting Attention Invariance is proposed, which processes documents offline and extracts fine-grained locations of high attention scores for each document and stores locations of high scores in the form of two compact bit vectors.

本综述界定了 CAIS 的概念，提出基于组件角色与编排策略的多维分类体系，并分析四种基础范式：Retrieval-Augmented Generation (RAG)、LLM Agents、Multimodal LLMs (MLLMs) 与 Orchestration。This survey defines the concept of CAIS, proposes a multi-dimensional taxonomy based on component roles and orchestration strategies, and analyzes four foundational paradigms: Retrieval-Augmented Generation (RAG), LLM Agents, Multimodal LLMs (MLLMs), and Orchestration.

提出 TRUSTMARGIN，一种免训练、即插即用的仲裁层，利用模型自身的似然对两个候选进行打分，在不微调、无需外部评判或额外生成的情况下，在直接回答与 RAG 之间进行选择。TRUSTMARGIN is proposed, a training-free, plug-and-play arbitration layer that scores the two existing candidates with the model's own likelihoods and selects between Direct and RAG without fine-tuning, external judges, or additional generation.

本工作将 embedding 缩放作为正交于稀疏度缩放的强有力维度加以探索，并推出 LongCat-Flash-Lite，一个从零训练的 68.5B 参数、约 30 亿激活参数的模型，不仅超越参数等量级的 MoE 基线，还对同规模现有模型展现出卓越竞争力。This work explores embedding scaling as a potent, orthogonal dimension for scaling sparsity and introduces LongCat-Flash-Lite, a 68.5B parameter model with ~3B activated trained from scratch that not only surpasses parameter-equivalent MoE baselines but also exhibits exceptional competitiveness against existing models of comparable scale.

基于实证对 "Enhanced" 与 "Agentic" RAG 范式进行评估，为真实场景中选取最有效的 RAG 设计（兼顾性能与成本）提供指导。An empirically driven evaluation of the "Enhanced" and "Agentic" RAG paradigms is conducted, offering guidance on selecting the most effective RAG design for real-world applications, considering both performance and costs.

本文将 Agentic 检索-生成循环形式化为有限时域部分可观测马尔可夫决策过程，显式建模其控制策略与状态转移，并构建了全面的分类体系与模块化架构分解，按规划机制、检索编排、记忆范式与工具调用行为对系统进行分类。This paper formalizes agentic retrieval-generation loops as finite-horizon partially observable Markov decision processes, explicitly modeling their control policies and state transitions, and develops a comprehensive taxonomy and modular architectural decomposition that categorizes systems by their planning mechanisms, retrieval orchestration, memory paradigms, and tool-invocation behaviors.

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.

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.

该综述细致梳理了 RAG 范式的演进，涵盖 Naive RAG、Advanced RAG 与 Modular RAG，并对 RAG 框架的三大基础支柱——检索、生成与增强技术——进行了深入审视。This comprehensive review paper offers a detailed examination of the progression of RAG paradigms, encompassing the Naive RAG, the Advanced RAG, and the Modular RAG, and meticulously scrutinizes the tripartite foundation of RAG frameworks, which includes the retrieval, the generation and the augmentation techniques.

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.