When Shallow Wins: Silent Failures and the Depth-Accuracy Paradox in Latent Reasoning

• URL: http://arxiv.org/abs/2603.03475v1
• Date: Tue, 03 Mar 2026 19:43:36 GMT
• Title: When Shallow Wins: Silent Failures and the Depth-Accuracy Paradox in Latent Reasoning
• Authors: Subramanyam Sahoo, Aman Chadha, Vinija Jain, Divya Chaudhary,
• Abstract summary: We demonstrate that state-of-the-art models (Qwen2.5-Math-7B) achieve 61% accuracy through a mixture of reliable and unreliable predictions.<n>We show that 18.4% of correct predictions employ stable, faithful reasoning while 81.6% emerge through computationally inconsistent pathways.
• Score: 16.505918019260964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mathematical reasoning models are widely deployed in education, automated tutoring, and decision support systems despite exhibiting fundamental computational instabilities. We demonstrate that state-of-the-art models (Qwen2.5-Math-7B) achieve 61% accuracy through a mixture of reliable and unreliable reasoning pathways: 18.4% of correct predictions employ stable, faithful reasoning while 81.6% emerge through computationally inconsistent pathways. Additionally, 8.8% of all predictions are silent failures -- confident yet incorrect outputs. Through comprehensive analysis using novel faithfulness metrics, we reveal: (1) reasoning quality shows weak negative correlation with correctness (r=-0.21, p=0.002), reflecting a binary classification threshold artifact rather than a monotonic inverse relationship; (2) scaling from 1.5B to 7B parameters (4.7x increase) provides zero accuracy benefit on our evaluated subset (6% of GSM8K), requiring validation on the complete benchmark; and (3) latent reasoning employs diverse computational strategies, with ~20% sharing CoT-like patterns. These findings highlight that benchmark accuracy can mask computational unreliability, demanding evaluation reforms measuring stability beyond single-sample metrics.

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