When Should Agents Remember? Falsification-Gated Self-Evolution for LLM Agents
[Sztuczna inteligencja] Autorzy: Runxuan Tang, Haoyu Gao, Yuyan Ding i 4 więcej
LLM agents increasingly operate in long-horizon, verifier-rich environments, but current self-improvement mechanisms often write episode-derived lessons directly into memory, making local success an unreliable signal for reusable and safe knowledge. This paper addresses the core question of when an experience-derived behavioral update should be admitted into persistent agent state. We propose Falsification-Gated Self-Evolution (FGSE), a verifier-grounded framework that represents each candidate lesson as a structured hypothesis with an explicit precondition, behavior change, expected effect, and verifier. FGSE applies the hypothesis only in a temporary state, tests it on target-transfer, falsification, and archived-regression probes, and then commits, refines, or rejects it according to measured gain and risk. Across web, app, tool-use, and long-term memory benchmarks, FGSE achieves str…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja