提出双视角关系学习方法，提升AI代理基准测试效率
Beyond Outcomes: Dual-View Relational Learning for Efficient Agent Benchmarking
研究团队提出了一个新方法，能更高效地评估AI代理的性能，压缩率很高，还能提供诊断反馈。
研究团队分析了大规模代理轨迹，识别出与最终性能相关的六个互补过程信号。他们提出DualViewEval方法，通过联合利用结果和过程关系来学习一个精确大小的最小任务集，并预测完整基准测试的分数。在五个代理基准测试和五个代表性基线模型上，该方法取得了最佳结果。在APEX-Agents和BFCL上，仅用20个任务就实现了24倍至40倍的压缩，将平均绝对误差（MAE）降低了14.5%至28.2%，并在SWE-bench Verified上将Kendall's τ提高了7.2%。
Beyond Outcomes: Dual-View Relational Learning for Efficient Agent Benchmarking
Agent benchmarks are substantially more costly to evaluate than conventional LLM benchmarks. Benchmark compression is therefore a natural solution, yet existing methods primarily model redundancy in task--model final-score distributions, which is important in agentic evaluation. To address this limitation, we analyze large-scale trajectories and identify six complementary process signals that are systematically associated with final agent performance. To disentangle agent performance redundancy from a complete perspective, we propose DualViewEval, an agent benchmark compression method that jointly exploits outcome and process relations to learn an exact-size miniset and predict the full-benchmark scores. Across five agent benchmarks and five representative baselines, DualViewEval achieves the best results in all datasets. With only 20 tasks, it achieves $24\times$--$40\times$ compression on APEX-Agents and BFCL, reducing mean absolute error (MAE) by $14.5\%$--$28.2\%$ over the strongest competitors while improving Kendall's $τ$ by up to $7.2\%$ relative to EssenceBench on SWE-bench Verified. The selected minisets further reveal capability differences among different agents, providing compact and diagnostic feedback for efficient agentic model development.