论文

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.

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.

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.

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.