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    AI skill retrieval can look helpful even when it hurts, a post warns

    The post describes a paper’s proposed fix, RAE: whenever an agent retrieves a skill, rerun that exact task without skill access and compare the results.

    RP
    1 Source, 23d ago, first seen 23d ago

    TLDR

    A post summarizing a paper warns that comparing tasks where an AI agent retrieved skills with tasks where it didn’t can be misleading. Those may be different kinds of tasks: if the agent retrieves skills on easier problems, retrieval can look helpful even when it does nothing or makes answers worse. The post describes the paper’s fix, RAE, as rerunning the exact task without skill access whenever retrieval happens, allowing a same-task comparison.

    Combined views

    5.2K

    1 Source, first seen 23d ago

    Combined views

    5.2K

    1 Source, first seen 23d ago

    34 likes
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    34 likes
    10 comments
    23 saves
    5 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    10 comments
    23 saves
    5 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    1 Source

    @rohanpaul_aiA 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.

    1 Source

    @rohanpaul_aiA 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.