Not All Test-Time Compute Is Evidence: Model-Specific Bias in LLM Reasoning
Test-time scaling methods implicitly assume that additional computation from the same model provides independent evidence. We show that this assumption fails within a single reasoning model: errors are correlated across repeated samples, across reasoning depth, and in the model's own self-evaluation, so more compute from the same model is not more evidence. We introduce DIA…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.