论文

本文在桌游《The Dark Eye》的虚构世界之上构建了一个完全单语德语的 RAG 问答测试平台——该领域德语资料丰富但过于小众，模型无法凭记忆作答，必须依赖检索。This work builds a fully monolingual German RAG question-answering testbed over the fictional world of the tabletop role-playing game The Dark Eye, a domain that is richly documented in German but too niche for the model to answer from memory, so that it has to rely on retrieval.

提出 AI 决策检查点——过程开发轨迹中的显式时刻，过程开发者在此识别 AI 候选子过程，评估对时间、成本、质量与灵活性的预期影响，考虑法律与组织约束，并记录关于采用、限制或拒绝特定 AI 组件的合理决策。AI-decision checkpoints are proposed, explicit moments in a process development trajectory where process developers identify AI-candidate sub-processes, assess expected effects on time, cost, quality, and flexibility, consider legal and organisational constraints, and document a reasoned decision to adopt, constrain, or reject specific AI components.

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.

当前大视频语言模型（LVLM）在处理长视频时仍面临挑战，主要原因是帧通常被独立处理，难以捕获跨事件的时序依赖。尽管检索增强方法已被引入以提供额外上下文，但多数方法在帧或片段层面操作，限制了建模事件随时间演化及其相互关系的能力。本文提出事件链检索增强生成（EC-RAG），一种无需训练的框架，将视频内容组织为 ex...Current large video-language models (LVLMs) still face challenges when dealing with long videos, mainly because frames are often processed independently, making it difficult to capture temporal dependencies across events. Although retrieval-augmented approaches have been introduced to provide additional context, most of them operate at the frame or snippet level, which limits their ability to model how events evolve over time and relate to each other. In this paper, we propose Event Chain Retrieval-Augmented Generation (EC-RAG), a training-free framework that organizes video content into an ex

结果显示，RAG 在生成完整 API 调用时能够减少幻觉并提升正确性，但在端点已提供的情况下反而会促使生成不必要的参数而降低正确性；约束解码（CD）则能可靠地防止非法 URL、HTTP 方法与参数的出现，并显著提升两种 starter code 的整体正确性。Results show that RAG reduces hallucinations and improves correctness when generating full API invocations but reduces it when the endpoint is already provided as it encourages the generation of unnecessary parameters, and CD reliably prevents illegal URLs, HTTP methods, and arguments and substantially improves overall correctness for both starter codes.

长上下文推理与检索增强生成（RAG）在截然不同的规模上处理证据选取——从单个长 prompt 到整个语料库。我们探究是否存在一种模型内部机制能够跨该范围选取证据。提出 UNREAL（UNifying REtrieval And Long-Context with a Single Model），一种模型原生的证据选取框架，跨越语料检索与长上下文推理。UNREAL 直接从冻结 LLM 的内部表征中编码 chunk 并推导检索查询，新增可训练参数少于 500K，且保持基模型...Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an entire corpus. We ask whether a single model-internal mechanism can select evidence across this range. We introduce UNifying REtrieval And Long-Context with a Single Model (UNREAL), a model-native evidence selection framework to span corpus retrieval and long-context inference. UNREAL encodes chunks and derives retrieval queries directly from the frozen LLM's internal representations. It adds fewer than 500K trainable parameters and leaves the ba

论文提出 Agentic AutoRAG，一个面向多目标 RAG 超参数优化并具备检索-生成失败归因能力的 LLM Agent 优化器，其 LLM-judge 准确率高于作者对比的全部基线，并在前 10 次试验中达到或超过统计基线 30 次试验的 judge 准确率。Agentic AutoRAG is introduced, an LLM-agent optimizer for multi-objective RAG hyperparameter optimization with retrieval-versus-generation failure attribution, and reaches higher LLM-judge accuracy than every baseline the authors compare, and within its first 10 trials it matches or beats the statistical baselines' full 30-trial judge accuracy.

本文研究仅使用身份标签对预训练关键点匹配器进行弱监督自适应，无需关键点级或几何对应真值，并利用典型监测数据集中已有的身份标注，实现图像匹配模型向野生动物领域的数据高效专业化。This work studies weakly supervised adaptation of a pretrained keypoint matcher using only identity labels, without keypoint-level or geometric correspondence ground truth, and enables data-efficient specialization of image matching models to wildlife domains using identity annotations already available in typical monitoring datasets.

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.

大语言模型正日益应用于基于长且异构信息源的任务。传统 RAG 依赖固定的相似度检索，而 agentic 变体虽能自适应查询与工具使用，但仍以检索为中心。然而在许多任务中，解答所需的证据并未显式存在于任何单一来源条目中，而必须通过对多个来源条目进行过滤、聚合或计算来推导。在本工作中，我们提出 RECAST（通过计算、访问与综合进行证据路由）……（原文截断）Large language models are increasingly applied to tasks grounded in long, heterogeneous information sources. Conventional Retrieval-Augmented Generation (RAG) relies on fixed similarity-based retrieval, while agentic variants adapt queries and tool use but remain largely retrieval-centric. However, in many tasks, the evidence required for a solution is not explicitly present in any single source item. Instead, it must be derived through filtering, aggregation, or computation across multiple source items. In this work, we introduce RECAST (Routing Evidence through Computation, Access, and Synth

DeepSeek Engram 等条件记忆架构利用输入 n-gram 查询已学习的 embedding，以有限的额外计算扩展大语言模型（LLM）容量。除模型扩展外，该架构还展现出将事实知识存储与通用计算解耦的潜力，为在固定 Transformer 主干的同时更新事实知识提供了一条可行路径。然而实现这一目标颇具挑战：同一事实的不同表达可能激活不同的 n-gram embedding，而更新共享 embedding 又会……Conditional memory architectures such as DeepSeek Engram use input n-grams to look up learned embeddings, expanding the capacity of large language models (LLMs) with limited additional computation. Beyond model scaling, this architecture has demonstrated the potential to decouple factual knowledge storage from general-purpose computation, offering a promising route to updating factual knowledge while keeping the Transformer backbone fixed. Realizing this potential is challenging because different expressions of a fact may activate different n-gram embeddings, while updating shared embeddings c

检索增强生成系统日益依赖文档结构处理：结构对齐分块、LLM 生成的块上下文、标题路径元数据，以及分层两阶段检索。各自研究在不同语料、嵌入器和指标上支持各自方法，但没有一项控制了共同混杂因素：在块前添加任何文本都会扰动其嵌入。我们提出机制隔离的消融方法，在同一协议下测试全部四种处理，跨条件匹配块大小，并加入一个语义为空的安慰剂——结构上有效但被打乱Retrieval-augmented generation systems increasingly rely on document-structure treatments: structure-aligned chunking, LLM-generated chunk contexts, heading-path metadata, and hierarchical two-stage retrieval. Separate studies support each on different corpora, embedders, and metrics, and none control for a shared confound: any text prepended to a chunk perturbs its embedding. We present a mechanism-isolating ablation testing all four treatments under one protocol, matching chunk sizes across conditions and adding a semantically null placebo---heading paths that are structurally valid but shuf

潜变量世界模型在预测未来状态和在真实世界中规划方面表现出色。然而在实践中，我们缺乏一种原则性的方法来估计其能力如何随模型规模、数据和算力扩展，这一开放问题减缓了该领域进展。本工作提出 RoboJEPA，一种基于联合嵌入预测架构 (JEPA) 的世界模型，在涵盖 12 种机器人形态的大规模数据集上训练。我们表明 RoboJEPA 的想象误差——其潜变量推演的误差——遵循关于算力的二阶幂律，使我们能够预先Latent world models have shown a remarkable ability to predict future states and to plan in the real world. In practice, however, we lack a principled way to estimate how their capabilities scale with model size, data, and compute, an open problem that slows progress in the field. In this work we present RoboJEPA, a world model based on the Joint Embedding Predictive Architecture (JEPA) and trained on a large-scale dataset spanning 12 robotic embodiments. We show that RoboJEPA's imagination error, the error of its latent rollouts, follows a second-order power law in compute, allowing us to pre

大规模信息检索系统（包括 RAG 与推荐引擎）广泛采用多层分层数据结构，以在高维向量空间中实现超高速近似最近邻搜索。然而，确保贪心导航既精确又高效的几何条件仍未被充分理解。本文研究由 d 维环面 T^d 上 n 个数据点构建的邻近图分层结构上的贪心导航效率，并确定了一个确定性覆盖条件，在该条件下……Large-scale information retrieval systems, including retrieval-augmented generation (RAG) and recommendation engines, widely use multi-layered hierarchical data structures for ultra-fast approximate nearest-neighbor search in high-dimensional vector spaces. However, the geometric conditions that ensure accurate and efficient greedy navigation remain poorly understood. In this work, we study the efficiency of greedy navigation on a hierarchy of proximity graphs constructed from \(n\) data points on the \(d\)-dimensional torus~$\mathbb{T}^d$. We identify a deterministic coverage condition under

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.

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.

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.