🧠 Once an agent writes down a lesson that turns out to be wrong, it just keeps believing it forever. Here's a paper that tackles that quiet but serious problem. Long-running LLM agents write down "reflections" from past experience and read them back later to decide what to do next. But existing harnesses only let information flow forward: once a lesson is written, nothing ever revisits it, even after the world has moved on. A database schema changes and the agent keeps assuming the old columns. A single failure gets over-generalized into a rule the agent follows forever. These stale memories quietly keep misleading the agent. That's the problem ReHarness, proposed in "Recurrent Harness for Agentic Continual Learning," sets out to fix. On top of the usual forward write and read, it adds a "backward revision" step: using feedback from the current experience to rewrite the reflections that were just retrieved, while still keeping every edit grounded in the trajectory that produced it. The results back it up. ReHarness beats every baseline across all three benchmarks - CL-Bench, MemoryBench, and MemoryArena - with the best overall normalized gain of 0.240, while using less than a third of the tokens that raw-trajectory baselines need. URL: #LLMAgents# #ContinualLearning#