Original post · Original
New MIT and Meta paper finds that agent memory works better when old notes get corrected using what happened the next time they were used.
Agents that learn on the job save notes from past tasks and reuse them. Once written, those notes rarely change, so an overly broad or outdated lesson keeps misleading the agent.
After each task, ReHarness sends the retrieved notes and the result to a revision agent. It narrows or corrects each note, using only facts from that note's original task.
Across 3 benchmarks and 3 frontier models, it averaged 0.240 in normalized gain, the share of possible improvement over a memoryless agent. The best raw-trajectory baseline scored 0.168, and replaying full raw history used 3.4× as many tokens on CL-Bench with Claude.
If your agent learns from experience, let each outcome edit the notes that shaped it, not only add new notes.