Model Capability Dominates: Inference-Time Optimization Lessons from AIMO 3

Natapong Nitarach (Schwyter)

Model Capability Dominates: Inference-Time Optimization Lessons from AIMO 3: 3 upvotes on Hugging Face Daily Papers, #28 of 29 papers on 2026-04-17. Day-by-day upvote history.

Majority voting over multiple LLM attempts improves mathematical reasoning, but correlated errors limit the effective sample size. A natural fix is to assign different reasoning strategies to different voters. The approach, Diverse Prompt Mixer, is tested on the AIMO 3 competition: 3 models, 23+ experiments, 50 IMO-level problems, one H100 80 GB, 5-hour limit. Every prompt-level intervention fails. High-temperature sampling already decorrelates errors; weaker strategies reduce accuracy more than they reduce correlation. Across an 8-point capability gap at equal N=8 and every optimization tested, model capability dominates. The gap between the best majority-vote score (42/50) and pass@20 (~45.5) is selection loss, not prompt loss. A verifier-based selector could close it. Prompt engineering cannot.

Paper page on Hugging Face · arXiv

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