ClawTrace: Cost-Aware Tracing for LLM Agent Skill Distillation
Skill-distillation pipelines learn reusable rules from LLM agent trajectories, but they lack a key signal: how much each step costs. Without per-step cost, a pipeline cannot distinguish adding a missing step to fix a bug from removing an expensive step that never affected the outcome. We use the cost-attribution gap to ask whether the rule types inside a distilled skill transfer the same way to new tasks. ClawTrace records cost-attributed agent traces and compiles each session into a TraceCard; CostCraft reads TraceCards and writes three kinds of skill patches: preserve, prune, and repair. We find a pattern aggregate metrics hide. On 30 held-out SpreadsheetBench tasks across two seeds, re...
| Authors | Boqin Yuan, Yue Su, Renchu Song et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 April 2026 |
| Last updated at source | 25 May 2026 |
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