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arXiv AI By Miao-Miao Yi, Zhen-Yu Cai, Y -M. Du, C. P. Sun

Information-Theoretic Reliability--Safety Bounds and Finite-Alignment Dynamics in AI Knowledge Distillation

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The paper establishes an information‑theoretic bound on the trade‑off between reliability and safety in AI, showing that this trade‑off is governed by a single parameter that measures a hidden layer’s ability to distinguish benign from hazardous inputs. Using a Boltzmann‑machine model, the authors derive exact relations between reliability and safety that align with experimental data. In the context of knowledge distillation, they demonstrate that teacher responses renormalize the student’s trade‑off parameters and, via a renormalization‑group approach, analyze multigenerational distillation to predict critical transitions between capability loss and stable transmission.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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