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The paper proposes a Shannon Scaling Law that models LLM training as information transmission over a noisy channel, explaining non-monotonic performance phenomena like catastrophic overtraining and quantization-induced degradation, and demonstrating superior predictive accuracy over traditional scaling laws.
This paper identifies 'library drift' as a silent failure mode in self-evolving LLM skill libraries, where unbounded skill accumulation causes retrieval degradation and performance stagnation. It provides trace-level diagnostics and a verified governance recipe that lifts pass@1 from 0.258 to 0.584 on MBPP+ hard-100.