Brilliant paper on agentic continual learning from Meta and MIT.

(bookmark it)

Agent memory harnesses usually only write forward. A lesson goes into the bank and is never checked against whether it helped.

This work proposes ReHarness, which rewrites the reflections an agent retrieved for a task after it sees how that task turned out.

Add and delete operations keep a reflection bank tidy, but they ignore downstream outcomes. ReHarness uses each finished trajectory as feedback on the specific reflections that guided it, so overgeneralized or outdated lessons get corrected.

Across three continual-learning benchmarks with 16 tasks and three backbones, it beats raw-context, memory, reflection, and prompt-optimization baselines, and the gains stay significant after multiple-comparison correction.

https://arxiv.org/abs/2610.12668

Chat with Paper: https://academy.dair.ai/papers/recurrent-harness-for-agentic-continual-learning-2610.12668