GRASP: Gated Regression-Aware Skill Proposer for Self-Improving LLM Agents
LLM agents acting in structured environments fail in operational rather than conversational ways, and reliability depends on procedural knowledge of the environment. Prior self-improvement methods accumulate natural-language guidance without checking that each new item preserves previously correct behavior, so a note that fixes one trajectory can silently regress another. We introduce GRASP (Gated Regression-Aware Skill Proposer), which treats agent improvement as a sequence of edits to a bounded skill library, admitting each candidate only if it produces a net improvement on a balanced held-out probe under a hard regression budget. We evaluate GRASP across five base models on two FHIR-ba...
| Authors | Johannes Moll, Jean-Philippe Corbeil, Jiazhen Pan et al. |
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| Primary category | cs.AI |
| Published | 28 May 2026 |
| Last updated at source | 21 August 2026 |
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