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Research2026-05-25 · source-backed
This paper models LLM training as information transmission over a noisy channel via the Shannon-Hartley theorem. Existing power-law scaling laws can't explain catastrophic overtraining or quantization-induced degradation. This framework predicts when scaling breaks down. Directly useful for teams making training compute allocation decisions.
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Simon Willison released LLM / Shared entities / What happened next / Tension
Linked by a graph relationship (Simon Willison released LLM); both cover Directly, LLM; picks up the Directly thread on 2026-06-18.
Simon Willison released LLM / Shared entity: LLM / What happened next / Tension
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; picks up the LLM thread on 2026-08-16.
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; picks up the LLM thread on 2026-07-27.
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; picks up the LLM thread on 2026-06-19.
Simon Willison released LLM / Shared entity: LLM / What happened next
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; picks up the LLM thread on 2026-08-21.
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; picks up the LLM thread on 2026-08-17.
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; picks up the LLM thread on 2026-08-12.
Simon Willison released LLM / Same source domain / Shared topic
Linked by a graph relationship (Simon Willison released LLM); reported by the same outlet (arxiv.org); overlapping topics (channel, model).