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Continual learning for LLM agents hits a wall: scaling models doesn't reliably improve skill generation, and self-feedback can lead to recursive drift.
Multi-LLM revision pipelines often succeed not because of error correction, but because the second model simply re-solves the problem, especially in constrained tasks like multiple-choice question answering.
