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Rohan Paul· @rohanpaul_ai · X·· 1 小时前AI 评分41
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腾讯新论文提出 SkillAdam,通过记录已修复问题和避免大规模重写,让智能体指令的自动优化更高效、成本更低。在长购物和旅行规划任务中,SkillAdam 平均准确率达 28.3%,优于此前最佳方法 SkillOpt 的 21.7%,且 token 消耗约为其三分之一。

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New 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"

来源:Rohan Paul · x.com