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.

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%.

介绍 EvoArena 基准套件，将环境变化建模为跨终端、软件与社会领域的渐进式更新序列；并提出 EvoMem，一种基于 patch 的记忆范式，将记忆演化记录为结构化的更新历史，使 Agent 能通过记忆的变化推理环境的演化。EvoArena is introduced, a benchmark suite that models environment changes as sequences of progressive updates across terminal, software, and social domains, and EvoMem is proposed, a patch-based memory paradigm that records memory evolution as structured update histories, enabling agents to reason about environmental evolution through changes in their memory.

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.

本文提出了 SeedRG，一个用于缓解 knowledge leakage 并应对 benchmark aging 问题的半合成 benchmark 生成 pipeline。SeedRG is introduced, a semi-synthetic benchmark generation pipeline that mitigates knowledge leakage and addresses the issue of benchmark aging.

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.

本文提出 RAGCap-Bench，一个面向能力的 benchmark，用于对 agentic RAG workflow 中的中间任务进行细粒度评测，并构建了典型 LLM 错误的分类体系以设计针对性评测问题。This work proposes RAGCap-Bench, a capability-oriented benchmark for fine-grained evaluation of intermediate tasks in agentic RAG workflows, and constructs a taxonomy of typical LLM errors to design targeted evaluation questions.

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.

本文介绍了 CodeXGLUE，一个基准数据集，旨在推动面向程序理解与生成的机器学习研究，涵盖 14 个数据集上的 10 项任务，并提供模型评估与比较的平台。This paper introduces CodeXGLUE, a benchmark dataset to foster machine learning research for program understanding and generation that includes a collection of 10 tasks across 14 datasets and a platform for model evaluation and comparison.

于 2018 年 8 月 28 日中午 12:15 在 Pettit 微电子研究中心 102 A/B 室进行报告。Presented on August 28, 2018 at 12:15 p.m. in the Pettit Microelectronics Research Center, Room 102 A/B.