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Silent Failures in Quantized LLM Reasoning: A Taxonomy-Based Analysis of Hollow Convergence and Failure Mode Shifts

arXiv cs.CL · 2026-07-14 Cached

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.

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