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 V-RAGBench——一个由 ⟨query, evidence chunk, answer⟩ 三元组构成的基准，可对检索与生成进行忠实且解耦的评估；同时提出 CARVE，一种在多种配置下并行运行检索器、并通过分块自适应重排序为每个分块挑选最优配置的简易方法。V-RAGBench is introduced, a benchmark of $\langle$ query, evidence chunk, answer$\rangle$ triplets that enables faithful, decoupled evaluation of retrieval and generation, and CARVE, a simple method that runs parallel retrievers across configurations and employs chunk-adaptive reranking to identify the winning configuration for each chunk.

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 π-Bench，一个用于评估主动式协助能力的基准，包含跨 5 个领域特定用户画像的 100 个多轮任务，用于评估 Agent 在长交互中预见并满足用户需求的能力，联合衡量长周期轨迹中的主动性与任务完成度，更贴近真实使用场景。$-Bench is introduced, a benchmark for proactive assistance comprising 100 multi-turn tasks across 5 domain-specific user personas that evaluates agents'ability to anticipate and address user needs over extended interactions, jointly measuring proactivity and task completion in long-horizon trajectories that better reflect real-world use.

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.

Agents' Last Exam（ALE）是一个面向长时序、具有经济价值且结果可验证的真实任务的 AI Agent 评测基准，旨在弥合基准测试表现与 GDP 相关影响之间的差距，而非仅仅作为排行榜。Agents'Last Exam (ALE) is introduced, a benchmark designed to evaluate AI agents on long horizon, economically valuable, real world tasks with verifiable outcomes, intended not merely as another leaderboard, but as an instrument for closing the gap between benchmark success and GDP relevant impact.

提出一个面向 RAG 的 AI 系统基准测试（RAGPerf）框架，用于刻画 RAG pipeline 的系统行为，并证明其引入的性能开销可忽略不计。The design and implementation of a RAG-based AI system benchmarking (RAGPerf) framework for characterizing the system behaviors of RAG pipelines is presented and it is shown that RAGPerf incurs negligible performance overhead.

在两台生产超级计算机上对三种 SOTA 向量数据库进行大规模评估，扩展至 64 个计算节点上的 256 个分布式 worker，结果显示：工作负载特性可能限制延迟下降，增加核心反而会降低查询吞吐，且从 16 扩展至 256 个 worker 仅带来 5.46 倍的性能提升。A large-scale evaluation of three state-of-the-art vector databases on two production supercomputers, scaling to 256 distributed workers across 64 compute nodes, reveals that workload characteristics can limit latency reduction, additional cores can reduce query throughput, and scaling from 16 to 256 workers only yields a 5.46x improvement.

本文在 KV cache 约束下对 LLM 推理进行理论建模，提出一种新型批处理与调度算法，在有效管理 KV cache 内存的同时最小化推理延迟，并通过在合成数据集上与后视最优的对比展示其强劲的实证性能。This work model LLM inference with KV cache constraints theoretically and proposes a novel batching and scheduling algorithm that minimizes inference latency while effectively managing the KV cache's memory, and demonstrates the algorithm's strong empirical performance by comparing it to the hindsight optimal in a synthetic dataset.

本文构建一种针对 GitHub issue 的持久会话评估方法，锚定在单一 base commit，对线性顺序探索与非线性、领域范围的并行 agentic 探索进行比较。This work constructs an approach for persistent-session evaluation of GitHub issues anchored at a single base commit, and compares linear sequential exploration against non-linear, domain-scoped parallel agentic exploration.

结果表明，在所评估的协调者–工作者设定下，多 Agent 系统中的隐私风险主要由架构层面的协调通道决定，而非仅取决于最终输出行为：风险来源于对标准输出级防御不可见的内部通道。Results suggest, within the evaluated coordinator-worker setting, that privacy risk in multi-agent systems is strongly shaped by architectural coordination channels rather than final-output behavior alone: it arises from internal channels that remain invisible to standard output-level defenses.

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.

介绍 MCP-Persona，这是首个专为评估 Agent 在真实场景、个性化 MCP 工具上的表现而设计的基准，并揭示了当前 Agent 在个性化工具使用上的显著不足，从而凸显该基准在发现并解决这些局限上的关键作用。MCP-Persona is introduced, the first benchmark specifically designed for evaluating agent performance on real-world, personalized MCP tools and demonstrates their significant struggles with personalized tool use, thereby highlighting the benchmark's crucial role in identifying and addressing these limitations.

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.

基于实证对 "Enhanced" 与 "Agentic" RAG 范式进行评估，为真实场景中选取最有效的 RAG 设计（兼顾性能与成本）提供指导。An empirically driven evaluation of the "Enhanced" and "Agentic" RAG paradigms is conducted, offering guidance on selecting the most effective RAG design for real-world applications, considering both performance and costs.

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 AgenticRAGTracer，这是首个主要由大语言模型自动构建、专为支持逐步验证而设计的 Agentic RAG 基准。AgenticRAGTracer is introduced, the first Agentic RAG benchmark that is primarily constructed automatically by large language models and designed to support step-by-step validation, and is primarily constructed automatically by large language models and designed to support step-by-step validation.

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.