Tree-Ising repairs Qwen reasoning loops with sparse BF16 edits
On two layers it scores 54.6% on MATH-500 versus 51.7% for a GPT-6 Astra edit and 7.9% for the base model.
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On two layers it scores 54.6% on MATH-500 versus 51.7% for a GPT-6 Astra edit and 7.9% for the base model.
Multiverse Computing frames LLM block pruning as an Ising optimization problem.
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