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This paper demonstrates that post-training quantization can silently alter how large language models reason, even when task accuracy is preserved, through a taxonomy-based analysis of 30,000 chain-of-thought outputs across multiple models and benchmarks.
This paper characterizes two distinct processes by which language models fail in reasoning—committed failure and persistent uncertainty—using token-level uncertainty signals, and demonstrates implications for self-consistency and failure detection strategies.