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    SkillAdam proposes tracking past fixes and making smaller edits to AI agent instructions

    A post describing a Tencent paper says SkillAdam scored 28.3% average accuracy versus SkillOpt’s 21.7% on long shopping and travel planning tasks.

    RP
    1 Source, 2h ago, first seen 2h ago

    TLDR

    A post describing a Tencent paper says SkillAdam logs past fixes as it rewrites instruction files that teach AI agents how to do tasks. It makes smaller changes when results are mixed, aiming to avoid undoing fixes that worked. The post reports 28.3% average accuracy on long shopping and travel planning tasks, versus 21.7% for SkillOpt, while using about a third as many tokens.

    Combined views

    1.8K

    1 Source, first seen 2h ago

    Combined views

    1.8K

    1 Source, first seen 2h ago

    19 likes
    19 likes
    7 comments
    3 saves
    4 reposts

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    Featured Source
    7 comments
    3 saves
    4 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    Today's Rank

    #19

    Today's Rank

    #19

    1 Source

    @rohanpaul_aiNew Tencent paper letting AI auto-improve your agent's instructions works better and costs far less if it remembers past fixes and avoids big, risky rewrites. Agent skills are instruction files that teach an agent how to do a job. Tools that auto-rewrite them often go in circles, burning tokens as new edits undo fixes that already worked. SkillAdam teaches the rewriting AI 2 habits. It keeps a log of what's been fixed, and it makes smaller changes when results are mixed. On long shopping and travel planning tasks, it scored 28.3% average accuracy versus 21.7% for SkillOpt, the best earlier method. It also used about a third as many tokens. If you auto-tune your agent's instructions, give the process a memory of past fixes and a brake on big edits. – arxiv. org/abs/2609.08944 Title: "SkillAdam: Stable and Efficient Skill Evolution for Agents"2h