论文

本文提出 M$^3$Exam，一个以查询为中心、基于真实用户-Agent 交互构建的多模态对话记忆基准，涵盖跨模态定位与隐式信息推断等多维度评估。M$^3$Exam is introduced, a query-centric multimodal conversational memory benchmark built on realistic user-agent interaction, with multi-dimensional evaluation spanning cross-modal grounding and implicit information inference.

本文命名并研究这两条研究脉络之间的模式：其递归单元是配备文件系统工具、代码执行与规划的完整 Agent harness，而非无工具的模型调用，并给出针对长上下文推理的受控评估。This work names and studies the pattern between these two lines of work, where the recursive unit is a full agent harness with filesystem tools, code execution, and planning rather than a model call with no tools, and provides a controlled evaluation on long-context reasoning.

本文提出 ForeSci，一个时间受控的基准，用于评估 LLM Agent 是否能从历史证据中做出前瞻性研究判断，并在四种骨干模型上评测原生 LLM、Hybrid RAG 以及三种 research-agent 适配方案。This work introduces ForeSci, a temporally controlled benchmark for evaluating whether LLM agents can make such forward-looking research judgements from historical evidence, and evaluates native LLMs, Hybrid RAG, and three research-agent adaptations across four backbones.

本文提出 MMLongEmbed，首个面向长上下文场景评估 MEM 的综合基准，并发现现有架构严重依赖浅层特征匹配，难以捕捉深层语义与结构依赖。This work introduces MMLongEmbed, the first comprehensive benchmark for evaluating MEMs in long-context scenarios, and finds that current architectures rely heavily on superficial feature matching and struggle to capture deep semantic and structural dependencies.

一个两级框架将手动 harness 工程转变为自动化 harness 工程，并更进一步——将"自动化本身的设计"也自动化。A two-level framework shifts manual harness engineering into automated harness engineering, and takes one step further --automating the design of the automation itself.

提出 Agentic Harness Engineering（AHE），一个通过三个相互匹配的 observability 支柱应对 harness 工程挑战的闭环，将每一次编辑转化为可证伪的契约，使 harness 演进能够自主进行而不退化为试错。Agentic Harness Engineering (AHE) is introduced, a closed loop that addresses harness engineering challenges through three matched observability pillars that turn every edit into a falsifiable contract, so harness evolution proceeds autonomously without collapsing into trial-and-error.

本文提出 Natural-Language Agent Harnesses，即可编辑的、描述运行级 harness 策略的文档，以及 Intelligent Harness Runtime（IHR），一个将上述文档解释为 agent 调用、交接、状态更新、验证门控与 artifact 契约的共享运行时。This paper introduces Natural-Language Agent Harnesses, editable documents that describe run-level harness policy, and Intelligent Harness Runtime (IHR), a shared runtime that interprets these documents into agent calls, handoffs, state updates, validation gates, and artifact contracts.

文中指出，安全的 LLM Agent 需要显式的信任边界、原则化的权限控制、具备溯源能力的 state 管理，以及与真实运行场景对齐的评估实践；现有 benchmark 仍未能充分覆盖长程、具状态、对部署敏感的风险。It is argued that secure LLM agents require explicit trust boundaries, principled privilege control, provenance-aware state management, and evaluation practices aligned with realistic operational settings, as well as existing benchmarks still underrepresent long-horizon, stateful, and deployment-sensitive risks.

在 DeNovoSWE 上对 Qwen3-30B-A3B 进行微调可显著提升长程 SWE 性能，在具有挑战性的 BeyondSWE-Doc2Repo benchmark 上将其得分从 5.8% 提升至 47.2%。Fine-tuning Qwen3-30B-A3B on DeNovoSWE substantially improves long-horizon SWE performance, raising its score on the challenging BeyondSWE-Doc2Repo benchmark from 5.8% to 47.2%.

本综述系统梳理了超越基础 Skill 创建的 Skill 演化与评估图景，将其归纳为四种范式：执行反馈、轨迹蒸馏、压缩与强化学习，并指出了构建可泛化、高效且可验证安全的 Skill 生态的开放方向。This survey systematically examines the landscape of skill evolution and evaluation beyond foundational skill creation into four distinct paradigms, spanning execution feedback, trajectory distillation, compression, and reinforcement learning, and identifies open directions for building skill ecosystems that are generalizable, efficient, and verifiably safe.

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.

本研究对新兴的内存键值存储进行了全面的性能与可行性评估，突出了性能、兼容性与长期可行性（包括项目成熟度、社区支持与持续开发）之间的权衡。This study presents a comprehensive performance and viability assessment of the emerging in-memory key-value stores and highlights trade-offs between performance, compatibility, and long-term viability, including project maturity, community support, and sustained development.

本文提出一个无偏的多进程 evaluation 框架，能够有效分散 client 端负载，从而在每秒数千次 query 以上的生产规模下实现对 LLM 的精确、可复现 profiling。This work proposes an unbiased, multi-process evaluation framework that effectively distributes client-side load, enabling accurate, reproducible profiling of LLMs at production scales exceeding thousands of queries per second.

本文通过大规模超参数搜索，系统地重新评估了 Vanilla LoRA 以及九个代表性 LoRA 变体，发现不同 LoRA 方法偏好的学习率区间各异，并将最优学习率区间的差异归因于最大 Hessian 特征值的变化，与经典学习理论相吻合。This work systematically re-evaluate nine representative LoRA variants alongside vanilla LoRA through extensive hyperparameter searches, finding that different LoRA methods favor distinct learning rate ranges and attributes the differing optimal learning rate ranges to variations in the largest Hessian eigenvalue, aligning with classical learning theories.

本工作提出 AlphaEval，一个基于真实生产环境的基准，包含来自七家在其核心业务中部署 AI Agent 的公司的 94 个任务，覆盖六个 O*NET (Occupational Information Network) 领域；并贡献了一套从需求到基准的构建框架，将从需求到评估的完整流程标准化。This work presents AlphaEval, a production-grounded benchmark of 94 tasks sourced from seven companies deploying AI agents in their core business, spanning six O*NET (Occupational Information Network) domains, and contributes a requirement-to-benchmark construction framework that standardizes the entire pipeline from requirement to evaluation.

对 SOTA LLM GPT-4 在医学能力考试与基准数据集上进行全面评估，并通过案例研究定性探索其行为，展示了 GPT-4 解释医学推理、为学生定制个性化讲解以及围绕病例交互式构造新反事实场景的能力。A comprehensive evaluation of GPT-4, a state-of-the-art LLM, on medical competency examinations and benchmark datasets and explores the behavior of the model qualitatively through a case study that shows the ability of G PT-4 to explain medical reasoning, personalize explanations to students, and interactively craft new counterfactual scenarios around a medical case.

结果表明，通过结合基础 LLM 改进（PaLM 2）、医学领域微调以及包括新颖集成精化方法在内的提示策略，医学问答正快速接近医生水平的表现。Results highlight rapid progress towards physician-level performance in medical question answering by leveraging a combination of base LLM improvements (PaLM 2), medical domain finetuning, and prompting strategies including a novel ensemble refinement approach.

本文针对 OOD 检测领域的近期技术发展空白，提出统一框架 generalized OOD detection（广义 OOD 检测），涵盖上述五类问题，即 AD、ND、OSR、OOD detection 与 OD。This paper addresses the gap in recent technical developments in recent technical developments in the field of OOD detection by presenting a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i.e.,AD, ND, OSR, OOD detection, and OD.