ReHarness proposes revising AI agents' reflections based on task outcomes

A post describes a Meta and MIT paper proposing ReHarness, which rewrites the reflections an AI agent retrieved for a task after seeing how that task turned out. The poster says it outperformed raw-context, memory, reflection and prompt-optimization baselines across three continual-learning benchmarks, 16 tasks and three backbones, with gains that remained significant after multiple-comparison correction.