A direct repair for looping AI
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Tree-Ising targets repetitive reasoning through weight edits.
Tree-Ising fixes reasoning loops by editing model weights. Its authors estimate $0.90 in GPU costs per fixed-recipe run.
An optimizer chooses changes without calling another language model.
Using Qwen3.5-0.8B, after 100 MATH-500 problems informed early screening, the remaining 400 yielded 53.8% versus 50.8% for an agent-written repair, averaging four answers per problem.
This small-model preprint is no universal repair service. Costs exclude development, and the recipe reuses previously identified layers.
Original source published: Tree-Ising research team (arXiv), Nov 10, 2026, 23:32:04 Beijing time / 10:32:04 AM EST
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