5arXiv cs.CL (Computation and Language)·
Linear probes on chain-of-thought hidden states can predict reasoning non-convergence early
A new arXiv preprint studies the bimodal convergence pattern in chain-of-thought models like DeepSeek-R1-Distill-Qwen-7B, where generations either complete within a token budget (90.3% accuracy on AIME) or exhaust it without concluding (6.6% accuracy), with a 62% overall convergence rate. The authors train linear probes on hidden-state activations at early token positions (50-300) and find that layer-20 activations at token 150 achieve AUC 0.608, reliably above chance and outperforming behavioral baselines from token entropy and repetition statistics. The results suggest convergence fate is partially encoded in intermediate representations early in generation, pointing toward early-exit inference and adaptive compute allocation strategies. Statistical evidence is modest (permutation test p=0.063), limiting strong conclusions.
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