论文

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.

我们提出了一种面向 TianoCore 开源 UEFI 固件开发生态中缺陷分诊的新方法。该集成方法名为 TianoForge，部署了人工智能（特别是机器学习）领域的前沿技术，以实现自动化的缺陷分诊，包括无效缺陷报告检测、重复缺陷报告检测、缺陷报告优先级排序以及缺陷报告分配。我们使用多种 Generative Pretrained Transformer (GPT) Large Language Models (LLMs)，结合与不结合 Retrieval Augmented Generation (RAG)，来完成这些任务。鉴于缺陷分诊在We propose a novel approach to bug triage in the TianoCore open-source UEFI firmware development ecosystem. This integrated approach, called TianoForge, deploys the state of the art in artificial intelligence, specifically machine learning, to enable automated bug triage. This includes invalid bug report detection, duplicate bug report detection, bug report prioritization, and bug report assignment. We use various Generative Pretrained Transformer (GPT) Large Language Models (LLMs) with and without Retrieval Augmented Generation (RAG) to automate these tasks. Given the crucial role of bug tria

随着 Retrieval-Augmented Generation (RAG) 向多样化组合生成方向发展，它受到两个关键瓶颈的阻碍：对证据利用的度量有缺陷，以及上下文预算分配欠优。我们依次解决这两个问题。为解决度量问题，我们揭示了一种普遍的"诊断幻觉"：标准相关性代理在难负例上表现糟糕。我们用一种高效的因果留一探针来取代它们，该探针能够准确隔离生成式依赖关系，并形式化地校准 LLM 注意力的结构性稀释。为解决分配问题，我们部署该因果探针As Retrieval-Augmented Generation (RAG) shifts toward diverse portfolio generation, it is stymied by two critical bottlenecks: flawed measurement of evidence utilization, and suboptimal context budget allocation. We resolve both sequentially. To resolve measurement, we expose a pervasive ``diagnostic illusion'': standard relevance proxies fail catastrophically on hard negatives. We replace them with an efficient causal leave-one-out probe that accurately isolates generative reliance and formally calibrates the structural dilution of LLM attention. To resolve allocation, we deploy this causal p

本文命名了"检索状态锁定"这一失败模式，通过分离单一置信度分数所混淆的三个对象——答案表面、检索到的证据以及检索状态本身——来诊断该问题，并直接衡量"一致性盲区"。This work names the failure retrieval-state lock-in and diagnose it by separating the three objects a single confidence score conflates: the answer surface, the retrieved evidence, and the retrieval state itself, and measures the agreement blind spot directly.

FRAMe 展示了先进 LLM 如何被部署用于以人为本的任务规划，将自然语言指令转化为安全、高效且灵活的飞行路线。FRAMe signifies how advanced LLMs can be deployed for human-centric mission planning, translating natural language instructions into safe, efficient, and flexible flight routes.

提出 AgentKGV，一种用于知识图谱事实核查的智能体 LLM-RAG 框架，集成动态路由与迭代查询改写，以应对文档级检索中的表层形式不匹配问题。AgentKGV, the Agentic LLM-RAG framework for KG fact Verification, is proposed, that integrates dynamic routing and iterative query rewriting, which handles surface-form mismatch in document-level retrieval.

Cross-encoder 在 RAG 流水线中具有较高的重排序准确率，但推理成本随序列长度呈二次增长，难以实时部署。本文通过两阶段流水线解决该问题：使用 Unsloth 框架与 LoRA 适配器，在自定义的查询-文档相关性数据集上对 LLaMA 3 (8B) 进行监督微调，随后进行 4-bit 量化以提升推理效率。该模型可替换双路检索 RAG 流水线中结合 BM25 与稠密向量检索的 cross-encoder，并在特定领域问答……Cross-encoders achieve high reranking accuracy in Retrieval-Augmented Generation (RAG) pipelines but impose quadratic inference costs that limit real-time deployment. We address this by fine-tuning LLaMA 3 (8B) as a drop-in reranker using a two-stage pipeline: supervised fine-tuning on a custom query-document relevance dataset via the Unsloth framework with LoRA adapters, followed by 4-bit quantization for efficient inference. The resulting model replaces the cross-encoder in a dual-retriever RAG pipeline combining BM25 and dense vector search. Evaluated on a domain-specific question-answering

本文提出 Chunk Coverage (CC)，一种独立于 oracle 的 RAG 系统检索组件测试充分性准则，结果表明 CC 在无需测试 oracle 的情况下捕获了与有效测试相关的检索多样性。Chunk Coverage (CC), an oracle-independent test adequacy criterion for testing the retrieval component of RAG systems, is introduced and results show that CC captures retrieval diversity relevant to effective testing without requiring test oracles.

已部署的平台与其面向运维的评估共同构成了一条可信赖、统计上可靠的 AI 辅助工作流，适用于设施运维，并可推广到其他大型科学仪器。Together, the deployed platform and its operations-grounded evaluation present a promising workflow for trustworthy, statistically grounded AI assistance in facility operations, transferable to other large scientific instruments.

结果表明，RAG 增强的 LLM 能显著提升生成文本的事实一致性、领域专属性与规范精度，同时降低产生无支持内容的风险；本地部署的 RAG 增强 LLM 不应仅被视为文本生成工具，而应作为认知计算基础设施中的语义处理模块，在法律和信息高度动态的环境中支撑合规与组织决策。The results demonstrate that augmenting LLMs with RAG significantly improves the factual consistency, domain specificity and normative precision of generated texts while reducing the risk of unsupported content generation and indicate that locally deployed LLMs enhanced with RAG should be regarded not merely as text generation tools but as semantic processing modules within cognitive computing infrastructures supporting regulatory compliance and organizational decision-making in environments characterized by high legal and informational volatility.

提出统一框架，融合时频图结构学习与协变量感知的表示融合，证实其在建模选择性变量交互、利用协变量提升预测精度方面的有效性。This work proposes a unified framework integrating time–frequency graph structure learning with covariate-aware representation fusion, confirming its effectiveness in modeling selective variable interactions and leveraging covariates for improved forecasting accuracy.

结果表明DEFRAG缩小了SLM与LLM之间的准确度差距，同时相比集中式服务成本降低最高达98.4%，峰值吞吐提升最高达97.8%，展现其在边缘实现民主化LLM服务的潜力。Results show that DEFRAG narrows the SLM-LLM accuracy gap, while reducing cost by up to 98.4% and increasing peak throughput by up to 97.8% over centralized services, demonstrating the potential of DEFRAG for democratized LLM services at the edge.

TEngineDB-V通过将全局段解耦索引物化为关系表，使向量搜索成为Tencent OLAP引擎的一等分析原语，消除scatter-gather执行、降低放大效应，并支持原生存储优化。TEngineDB-V makes vector search a first-class analytical primitive in Tencent's OLAP engine through a global segment-decoupled index materialized as relational tables, eliminating scatter-gather execution, reducing amplification, and enabling native storage optimizations.

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.

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.