# Falsification-Gated Self-Evolution

Long-running LLM agents often write each episode's lesson straight into memory, yet local success is not a reliable sign of reusable, safe knowledge. FGSE turns every candidate lesson into a structured hypothesis with a precondition, behavior change, expected effect and verifier, applies it only in a temporary state, and tests it on target-transfer, falsification and archived-regression probes before committing, narrowing or rejecting it. On verifier-rich benchmarks including WebArena, AppWorld and τ-bench, FGSE with a DeepSeek-V3.1 backbone reaches 39.8 WebArena success rate and 78.8 AppWorld average completion, while cutting falsification failures, regression damage and harmful committed updates to 5.0%, 1.3% and 3.6%.

Long-running LLM agents often write each episode's lesson straight into memory, yet local success is not a reliable sign of reusable, safe knowledge. FGSE turns every candidate lesson into a structured hypothesis with a precondition, behavior change, expected effect and verifier, applies it only in a temporary state, and tests it on target-transfer, falsification and archived-regression probes before committing, narrowing or rejecting it. On verifier-rich benchmarks including WebArena, AppWorld and τ-bench, FGSE with a DeepSeek-V3.1 backbone reaches 39.8 WebArena success rate and 78.8 AppWorld average completion, while cutting falsification failures, regression damage and harmful committed updates to 5.0%, 1.3% and 3.6%.