Fine-tuned LLMs automate proving of entropy inequalities

Researchers have developed a method to automate the proving of Shannon-type entropy inequalities using fine-tuned language models and guided tree search. Their small-scale models, with parameters ranging from 0.6B to 1.7B, achieved an 85% success rate on a test set of 60 inequalities involving 10 to 15 variables. This approach significantly outperformed zero-shot prompting with larger models like GPT-5.5 and Psitip. An ablation study indicated that a 4096-token training context length and a non-skewed data distribution were optimal for performance. AI

IMPACT Demonstrates a novel application of fine-tuned LLMs for complex mathematical proofs, potentially accelerating research in information theory.

RANK_REASON The cluster contains an academic paper detailing a new method for proving entropy inequalities using language models. [lever_c_demoted from research: ic=1 ai=1.0]

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• GPT-5.5
• Psitip
• Shannon

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