Agents
Brilliant paper on agentic continual learning from Meta and MIT. (bookmark it) Agent memory harnesses usually only write forward. A lesson g…
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 he
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 ReHarness uses each finished trajectory as feedback on the specific reflections that guided it, so overgeneralized or outdated lessons get corrected.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
Related
- Banger paper from Meta and colleagues. (bookmark it) They discuss benefits of giving your agent write access to its own context. In other wo…
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- // Contextual Agentic Memory is a Memo, not True Memory // Most agent memory today isn't memory. They are more like memos. A new paper argue…
Source: DAIR.AI (X) | 2026-10-13