论文

研究发现，recall@k 并非已部署 KB-VQA 的正确评价指标，且弥合差距需要 reader 侧介入；本文首次对多模态 KB-VQA 中 reader 侧位置依赖性进行了受控探查，设计了一种 gold-position 协议——在问题提示中仅改变 gold passage 所在的槽位。The findings indicate that recall@k is the wrong metric for deployed KB-VQA and that closing the gap requires reader-side intervention; the first controlled probe of reader-side position dependence in multimodal KB-VQA is designed, a gold-position protocol in which only the gold passage's prompt slot varies within question.

AsymCache 是一个面向 LLM 推理的计算-延迟感知 KV cache 管理系统，将 cache 驻留决策与 GPU attention kernel 性能显式对齐，包含三个关键组件：用于高效处理非连续 KV 上下文的多段注意力（MSA）、联合优化命中率与位置感知重计算代价的 cache 淘汰策略，以及面向高硬件利用率的自适应分片调度器。AsymCache is proposed, a computation-latency-aware KV cache management system for LLM inference that explicitly aligns cache residency decisions with GPU attention kernel performance, including three key components: Multi-Segment Attention (MSA) for efficient non-contiguous KV context processing, a cache eviction policy that jointly optimizes hit rate and position-aware recomputation cost, and an adaptive chunking scheduler for high hardware utilization.

这篇立场论文认为，LLM 推理 serving 已超越通用启发式方法，如今需要数学优化与算法基础，并呼吁社区将 LLM serving 的算法设计视为一个新的研究前沿。This position paper argues that LLM inference serving has outgrown generic heuristics and now demands mathematical optimization and algorithmic foundations, and calls on the community to recognize algorithmic design for LLM serving as a research frontier.

揭示使用工具的 LLM Agent 的一种部署失效模式——来源缺口，以及一类跨上下文多步越狱攻击，可在早期交互中保留看似无害的中间产物，并在很久以后（可能在不同 Agent 实例或工作流阶段）诱发有害行为。A deployment failure mode for tool-using LLM agents, the provenance gap, and a family of cross-context multi-step jailbreaks that preserve benign-looking intermediate artifacts from an early interaction and elicit harmful behavior much later, potentially in a different agent instance or workflow stage.

通过形式化分析与架构分解，展示 SSGM 如何缓解拓扑引发的知识泄漏（敏感上下文被固化到长期存储），以及有助于防止语义漂移（知识在迭代摘要中退化）。Through formal analysis and architectural decomposition, it is shown how SSGM can mitigate topology-induced knowledge leakage where sensitive contexts are solidified into long-term storage, and help prevent semantic drift where knowledge degrades through iterative summarization.

文章认为基于实体的分解能形成对原始信息更精炼的表示，并有助于降低索引与生成过程中的噪声；在端到端 QA 评测中，VectorRAG 表现优于标准 GraphRAG，且接近当前 SOTA 图方法的效果。It is argued that entity-based decomposition yields a more distilled representation of original information, and additionally serves to reduce noise in the indexing, and generation process, and on end to end QA evaluation VectorRAG performs better than standard GraphRAG and almost as good as current SOTA graph-based solutions.

研究表明，机器学习在公共部门的成功，将更少依赖模型准确率的突破，而更多依赖机构能否构建出透明、可复现、可问责且受公民信任的数据基础设施。It is shown that the success of machine learning in the public sector will depend less on breakthroughs in model accuracy and more on the ability of institutions to engineer transparent, reproducible, and accountable data infrastructures that citizens can trust.

本文提出一种分层隔离架构，结合策略感知的 ingestion、retrieval-time gating 与共享推理，并通过服务端 agentic 编排加以执行，在为多租户隔离提供天然强制点的同时，允许客户端框架保留对 agent 组合与延迟敏感操作的控制权。A layered isolation architecture combining policy-aware ingestion, retrieval-time gating, and shared inference, enforced through server-side agentic orchestration is introduced, creating natural enforcement points for multitenant isolation while allowing client-side frameworks to retain control over agent composition and latency-sensitive operations.

本文探讨 Paxos 与 Raft 何者更优地解决分布式共识问题，通过以 Raft 的术语与实用抽象描述一个简化的 Paxos 算法，精确揭示两者的差异。This paper considers the question of which algorithm, Paxos or Raft, is the better solution to distributed consensus to determine exactly how they differ by describing a simplified Paxos algorithm using Raft's terminology and pragmatic abstractions.

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.

本文认为，可解释 AI 研究总体上有助于推动 AI/ML 在医疗领域的落地，并特别有助于增强透明性与信任。It is argued that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitates transparency and trust.