Evaluating Exact Output and Checkpoint-State Prediction in Real Programs

Abstract:We present a benchmark for predicting final output and checkpoint state from source and input alone. It extends CRUXEval-style output prediction with paired shorter- and longer-trace inputs and checkpoints inside and after a loop. The benchmark contains 400 cases from 371 Python and C++ programs, evaluated under seven settings from four model families without tools or code execution. Of 11,200 planned predictions, 11,151 produced gradable responses. Reasoning-enabled settings outperform their off counterparts by 33.1 to 55.2 percentage points on completed responses. The strongest setting scores 93.0% on shorter-trace final output, 77.0% on longer-trace final output, and 65.5% and 63.5% on the two state tasks; these scores also hold when missing responses count as wrong. Across 2,397 matched Python comparisons with identical source, changing to the longer-trace input yields 528 correct-to-wrong changes and 147 reversals. Source-clustered analyses preserve this accuracy gap, while adjusted Python models give no evidence of a positive incremental association between cumulative state load and error. Changed inputs and checkpoint tasks alter several factors together, so the gaps do not isolate trace length or an internal state-tracking mechanism. The benchmark exposes errors hidden by short-output scores alone.

15 pages. Accepted at the NeurIPS 2026 Workshop on AI for Verifiable Coding. Includes appendices

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

arXiv:2610.11889 [cs.SE]

(or arXiv:2610.11889v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2610.11889 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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