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arXiv · 2605.00817

When LLMs Stop Following Steps: A Diagnostic Study of Procedural Execution in Language Models

Sailesh Panda·Pritam Kadasi·Abhishek Upperwal·Mayank Singh
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Abstract

Large language models (LLMs) often achieve strong performance on reasoning benchmarks, but final-answer accuracy alone does not show whether they faithfully execute the procedure specified in a prompt. We introduce a controlled diagnostic benchmark for arithmetic procedural execution, where models are given a step-wise arithmetic procedure and two numeric inputs, and must return the final computed value. Complexity is varied through procedure length and look-back dependencies over intermediate variables. Average first-answer accuracy drops from 63% on 5-step procedures to 20\% on 95-step procedures. Generation-level analysis shows that failures often involve missing answers, premature answers, self-correction after an initial error and under-executed traces. These findings reveal a consistent decline in execution performance as arithmetic procedural complexity increases.

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Sailesh Panda, Pritam Kadasi, Abhishek Upperwal, Mayank Singh. 2026-05-01. When LLMs Stop Following Steps: A Diagnostic Study of Procedural Execution in Language Models. https://arxiv.org/abs/2605.00817

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