ReHarness

A joint MIT and Meta paper introduces ReHarness, which revises an agent's previously retrieved notes using the outcome of each task instead of only appending new ones, preventing stale or over-broad lessons from misleading the agent. Across three benchmarks and three frontier models it averaged 0.240 normalized gain versus 0.168 for the best raw-trajectory baseline, while using roughly one-third the tokens of replaying full raw history on CL-Bench with Claude.