论文提出 RAE 方法评估 LLM 智能体技能检索的真实效果

Rohan Paul · @rohanpaul_ai · X·2026-09-08 10:24·39分钟前
AI 导读

一项来自 Soongsil University 的论文指出,技能增强智能体的常规评估存在选择偏差,聚合检索增益可能掩盖检索任务本身的负收益,论文将其形式化为 Skill Following(SF)问题并提出 Retrieval-Invoked Actual-Use Effect(RAE)加以度量。

Rohan Paul@rohanpaul_ai
53AI 编辑部评分,满分 100

论文提出 RAE 方法评估 LLM 智能体技能检索的真实效果

2026-09-08 10:24· 39分钟前
AI 导读

一项来自 Soongsil University 的论文指出,技能增强智能体的常规评估存在选择偏差,聚合检索增益可能掩盖检索任务本身的负收益,论文将其形式化为 Skill Following(SF)问题并提出 Retrieval-Invoked Actual-Use Effect(RAE)加以度量。

A skill-enabled agent can look better overall while performing worse on the exact tasks where it retrieved skills,

This paper finds a nasty evaluation failure: positive retrieval gains can hide negative performance on the very tasks where retrieval happened.

Most evaluations make a flawed comparison: they check whether tasks where the agent chose to retrieve a skill scored better than tasks where it chose not to.

But those may be completely different kinds of tasks. If the agent tends to retrieve skills on easier problems, retrieval will look helpful even if the skill itself did nothing, or even made the answer worse.

This paper’s fix, RAE, is simple: when retrieval happens, rerun that exact task without skill access and compare the result.