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 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 suggest that apparent reasoning ability can mask substantial weaknesses in faithful long-horizon procedural execution.
@article{arxiv.2605.00817,
title = {When LLMs Stop Following Steps: A Diagnostic Study of Procedural Execution in Language Models},
author = {Sailesh Panda and Pritam Kadasi and Abhishek Upperwal and Mayank Singh},
journal= {arXiv preprint arXiv:2605.00817},
year = {2026}
}