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LLM Math Errors Linked to Late-Network Sign Flips

By PulseAugur Editorial · [1 sources] ·

A new study investigates why large language models make sign errors in multi-step mathematics, finding that these errors are committed late in the network, typically within the final quarter of MLP layers. The research analyzed six open models, including Qwen and Llama 3.3 70B Instruct, and discovered varying mechanisms for sign error commitment, such as fixed "minus-stamp" layers or sign amplifiers. While the localization of these circuits was consistent across models, their steerability varied significantly, with some models being highly overturnable and others showing high redundancy, suggesting localization does not guarantee control. AI

IMPACT Identifies specific failure modes in LLMs for mathematical reasoning, potentially guiding future model development for improved accuracy.

RANK_REASON Academic paper detailing a mechanistic study of LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM Math Errors Linked to Late-Network Sign Flips

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Academic paper detailing a mechanistic study of LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shradha Agarwal, Tariq J., Deepak Rajbhar ·

    Localization Does Not Imply Steerability: A Mechanistic Study of Sign Errors in LLM Mathematics

    arXiv:2611.07478v1 Announce Type: cross Abstract: When large language models do multi-step mathematics, a characteristic failure recurs: the model writes one wrong sign token mid-computation -- a cofactor sign, the minus of a recursive integration-by-parts step -- then executes t…

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