Closed-loop knowledge dynamics saturate under internal feedback; external information can move knowledge states beyond current attractors, using a three-level operational framework with transition kernels.
Read the original at arxiv.org→arXiv:2607.14185v1 Announce Type: new Abstract: Feedback-driven loops support iterative improvement in large language models, reinforcement learning, and autonomous discovery, yet their gains often diminish under...
Original headline: "Closed-Loop Knowledge Dynamics: An Operational Framework for Saturation and Escape"
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- Jul 17, 04:00 UTC arXiv cs.LG lead source Closed-Loop Knowledge Dynamics: An Operational Framework for Saturation and Escape