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Evaluating Exact Output and Checkpoint-State Prediction in Real Programs
Xiaohong Chen, David Bucur, Chenglong Ma, Yi Zhang, Lingming Zhang, Sriram Vishwanath, Grigore Rosu
TL;DR
Model-only program execution prediction remains less extensively benchmarked than code generation. This benchmark pairs shorter- and longer-trace output tasks with loop checkpoint-state prediction, finding a reasoning-mode advantage but lower accuracy on longer and state tasks.
Problem
Direct execution reasoning is less extensively benchmarked than code generation, despite being a distinct capability from program synthesis.
Source: §1 · p. 1Method
The benchmark evaluates final output and projected checkpoint state on paired execution scales across 400 cases from 371 Python and C++ programs.
Source: §1 · p. 2Results
Reasoning-enabled settings outperform their off counterparts by 33.1 to 55.2 percentage points overall, while the strongest setting scores 93.0% on short final output, 77.0% on long final output, and 65.5% and 63.5% on checkpoint state.
Source: §1 · p. 2, §4.2 · p. 6Takeaways & Limitations
The benchmark exposes prediction errors that short-output scores alone leave unseen, without establishing a particular internal failure mechanism.
Source: §7 · p. 8The reported status categories distinguish compliance and correctness but do not classify wrong answers by error type.
Source: §6 · p. 8
Abstract
from arXiv · showhide
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.
1 Introduction
The introduction frames model-only program execution prediction as a complementary capability to coding-agent performance and presents paired output and checkpoint tasks. The results show a substantial reasoning-mode advantage alongside persistent difficulty on longer-input and state tasks.
1 Introduction
- Model-only execution prediction complements coding-agent scores by testing whether a model can determine program behavior from source and input before runtime feedback enters the workflow.Coding agents combine models with context, code execution, feedback, and retries [5].Source: §1 · p. 1
- The benchmark contains 400 cases from 371 Python and C++ programs, with paired execution scales and shared state projections per case.Execution checks establish ordered trace lengths, reachable deterministic checkpoints, and preservation of normal output under instrumentation.Source: §1 · p. 2
- 33.1 to 55.2 percentage points separate reasoning-enabled settings from their off counterparts overall, yet the strongest setting falls from 93.0% on short final output to 77.0% on long final output and below 66% on both state tasks.Across 2,397 paired Python comparisons with identical source, longer inputs change 528 correct answers to wrong ones and 147 wrong answers to correct ones.Source: §1 · p. 2
- Adjusted analyses provide no evidence of a positive incremental association between cumulative state load and error, while cyclomatic complexity has no consistent positive association under within-arm tests.The separate invariant-guidance pilot establishes neither an accuracy nor an efficiency benefit.Source: §1 · pp. 2–3
2 Related Work
Prior work studies short-function execution prediction, learned execution traces, coding harnesses, and invariants. This benchmark complements those directions with paired execution scales and state projections on larger programs.
Execution prediction
Learning from execution traces
- Execution-learning work spans synthetic-program interpretation, complete trace generation, execution-grounded rationales, and training on interpreter or environment trajectories [3] [12] [13] [14] [15] [16].Scratchpads and chain-of-thought prompting allocate tokens to intermediate computation [17] [18].Source: §2 · p. 3
Coding harnesses
Invariants
- The invariant pilot asks whether a source-checked, answer-free invariant helps exact output prediction, connecting execution diagnosis to semantic summaries during reasoning.Source: §2 · p. 3
3 Benchmark Construction
The benchmark fixes programs, inputs, and state projections before execution checks, then evaluates four related output and checkpoint tasks across selected source pools.
3.1 Task unit and four arms
- Each case pairs a deterministic program and two legal inputs with either final standard output or projected live-variable state at a declared checkpoint.Ground-truth files come from execution and are not shown to models; the two state checkpoints share ordered fields, with at least one core field changing.Source: §3.1 · p. 3, Table 1 · p. 4
3.2 Source pools and selection
- Four source pools cover LiveCodeBench, CodeContests [9], authored textbook algorithms, and one NetworkX routine with selected graph inputs.Construction excludes cases with nondeterministic outputs or failed language, input, cohort, or checkpoint requirements.Source: §3.2 · p. 4
- Admission checks require a strict short-trace < long-trace ordering, reachable checkpoints, matching state fields and types, deterministic oracles, and preserved clean-program output under instrumentation.After seven CodeContests checkpoint identities were corrected, 69 affected state predictions were recollected.Source: §3.2 · p. 4
3.3 Corpus statistics
- The largest cohort spans cyclomatic complexity values from 3 to 212, while NetworkX contributes 30 inputs for one fixed routine.The corpus supports examining variation both across programs and across executions of a shared implementation.Source: §3.3 · p. 4
4 Evaluation
The evaluation uses a fixed model-and-grading protocol and finds large reasoning-mode differences alongside lower accuracy on longer-input and checkpoint tasks.
4.1 Models, sessions, and grading
- Each task runs in a fresh session with tools, retrieval, and code execution disabled, and correctness is reported as completed-response C/R and planned-response C/N accuracy.C/N counts missing responses as wrong; 49 of 11,200 planned requests returned no response.Source: §4.1 · p. 5, Table 2 · p. 5
4.2 Overall results
- Across all studies, gpt-5.6-sol scores 74.8% with high reasoning versus 25.2% off on completed responses; deepseek-v4-pro-0813 scores 63.8% versus 8.6%.The provider-specific reasoning settings differ in meaning and computational budget.Source: §4.2 · p. 5
- For the strongest setting, short final-output accuracy is 93.0%, compared with 77.0% on long final output and 65.5% and 63.5% on checkpoint state.All four high-reasoning settings follow the short-output, long-output, then state-prediction ordering; these task-condition comparisons bundle changes beyond trace length.Source: Table 3 · p. 5, §4.2 · p. 6
- In the fixed-implementation NetworkX study, the best setting answers 6 of 120 tasks and none of its 60 state tasks, while the other six settings answer none of 120.The study has little resolution for ranking settings or explaining errors, and one routine does not represent library code generally.Source: §4.2 · p. 6
5 Quantitative Analysis
This section compares matched shorter- and longer-input outputs and examines whether source or execution metrics track errors. Longer-input changes are asymmetric, while adjusted analyses provide no evidence of a positive incremental state-load association.
5 Quantitative Analysis
- The analysis compares cyclomatic complexity, native trace length, peak state size, and cumulative state load across program executions.Cumulative state load sums reachable-state size across observations, combining step count and per-step state. Python and C++ measurements are analyzed separately; original tests use normalized Mann–Whitney statistics and one-sided permutation tests, while adjusted uncertainty is clustered by source program.Source: §5 · p. 6
5.1 Paired shorter- and longer-input results
- 528 correct-to-wrong changes versus 147 wrong-to-correct changes occur across 2,397 matched Python pairs; the clustered short-minus-long accuracy difference is 15.89 percentage points.The 95% percentile interval is [12.12, 20.10] across 314 source clusters; this describes admitted-source variation, not a causal trace-length effect. Branch paths, values, and output properties also change.Source: §5.1 · p. 6
5.2 Metric associations after adjustment
- 0.859 is the error odds ratio per doubling of cumulative state load in adjusted Python models, with a 95% interval of [0.733, 1.006].The three dynamic predictors are strongly correlated, so conditional coefficients require care.Source: §5.2 · p. 7
5.3 Cyclomatic complexity within task conditions
- 12 of 94 mixed-outcome series meet the unadjusted nominal rule, but none of 29 estimable long-trace-final series does.Cyclomatic complexity shows no consistent positive association with error within tested conditions; negative or nonmonotonic associations and other static properties remain open.Source: §5.3 · p. 7
6 Limitations and Future Work
The analyses have limits in causal interpretation, corpus coverage, response stability, and error diagnosis. The authors identify further controls and broader evaluation needs.
6 Limitations and Future Work
- The study did not measure how often paired inputs preserve branch behavior or isolate iteration count as the sole changing factor.Future construction should vary repeated work, state width, checkpoint position, and answer format independently.Source: §6 · p. 7
- The source-clustered reanalysis accounts for measured task covariates but cannot remove unmeasured input or semantic confounding.It is exploratory and was added after review; dynamic measurements also depend on adapters and instrumentation.Source: §6 · p. 7
- The corpus concentrates on contest programs and selected algorithms, excluding some recursive, comprehension-based, short-loop, and large-state programs.Admission favors explicit loops with deterministic, reachable checkpoints and changing state projections.Source: §6 · p. 7
- One selected response per grid cell leaves repeated-query stability unmeasured, and source resampling does not estimate repeated API-call variation.Reasoning-mode compute budgets are unequal and incompletely observed, and endpoints may drift.Source: §6 · p. 7
- Grading distinguishes envelope compliance and correctness but does not classify wrong answers as execution, arithmetic, copying, or formatting mistakes.That error taxonomy requires separately audited annotations.Source: §6 · p. 8
- The invariant-guidance pilot establishes no reliable accuracy or efficiency benefit.Its exact prompts and native sessions are not reproduced, limiting the available evidence for a confirmatory study.Source: §6 · p. 8
7 Conclusion
The benchmark reveals gaps across output and checkpoint-state prediction, while its paired and adjusted analyses do not establish a particular failure mechanism. The paper also specifies what its appendices document and what aggregate reporting cannot support.
7 Conclusion
- The strongest setting falls from 93.0% on shorter-input final output to 77.0% on longer-input final output and below 66% on both state tasks.The benchmark covers 400 cases; source-clustered analyses account for related predictions, but changed inputs and checkpoint targets remain bundled interventions.Source: §7 · p. 8
A Protocol and Analysis Scope
- Appendices B–D and F–G provide prompt and grading rules, accuracy results, analysis procedures, pilot evidence, and checkpoint transformations.The main text specifies task arms, source-pool selection, execution checks, model settings, and scoring denominators.Source: A Protocol and Analysis Scope · p. 9
- The paper reports 11,151 completed predictions and 49 no-response records, but does not reproduce case-level tasks, responses, or raw execution traces.Aggregate tables alone therefore do not support independent regrading or recomputation of execution measurements.Source: A Protocol and Analysis Scope · p. 9
B Grading Detail
This section defines how predictions are prompted, graded, and counted, including the treatment of nonresponses and response-envelope validity. The accuracy matrix reports study- and setting-level results under completed-response and all-planned denominators.
B Grading Detail
- The rendered prompt places literal source and input in tagged blocks and requests exact predicted text in a JSON object with one string field, output.A state answer is serialized JSON inside that string, and there are no per-task prompt overrides.Source: B Grading Detail · pp. 9–10
- Completed wrong answers count as wrong regardless of envelope validity, while only transport failures and requests with no response are excluded.Of 49 excluded requests, 47 are gpt-5.6-sol-off and two are qwen3.8-27b-high.Source: B Grading Detail · p. 10
- 1,231 of 11,151 completed responses (11.0%) violate the outer response envelope; 276 remain correct under conservative extraction and 955 do not match the oracle.The envelope is the outer single-string output object, not the validity or semantic accuracy of a JSON state inside that string.Source: B Grading Detail · p. 10
C Complete Accuracy Matrix
- Table 5 reports overall accuracy by study and setting, with completed-response results above and a lower matrix counting nonresponses as wrong.Planned denominators per setting are 1,132, 140, 120, 88, and 120, respectively.Source: Table 5 · p. 11
D Source-Clustered Reanalysis
The reanalysis uses source-clustered uncertainty to revisit paired accuracy differences and adjusted associations between execution measurements and error. It retains the short-versus-long gap but finds no evidence that cumulative state load adds a positive association conditional on other predictors.
D Source-Clustered Reanalysis
- The adjusted models use completed responses, study, arm, and setting indicators, then add centered log2 trace length, peak state size, and cumulative state load in stages.NetworkX and C++ cohorts are excluded from these fits.Source: D Source-Clustered Reanalysis · p. 11
- The adjusted cumulative-state-load estimate is negative, with an interval including one on the odds-ratio scale, providing no support for a positive incremental role.The trace and peak-state coefficients increase when load is added; the analysis does not establish causal effects or out-of-source predictive improvement.Source: D Source-Clustered Reanalysis · p. 11
- Paired short-minus-long percentile intervals use 10,000 bootstrap replicates sampling whole clean-source clusters, condition on the admitted source set and selected model settings, and quantify neither training-data contamination nor generalization to a representative program population.Source: D Source-Clustered Reanalysis · p. 11, Table 7 · p. 12
- The exploratory logistic models report odds ratios per metric doubling, with 95% intervals and p-values using source-cluster uncertainty.All three fits converge, with full-rank design matrices and positive-definite covariance matrices.Source: D Source-Clustered Reanalysis · p. 11, Table 6 · p. 12
E Original Exploratory Metric Analyses
The original analyses find pooled associations between dynamic execution measurements and prediction error, especially in Python, but these are exploratory and depend on task grouping and measurement convention. The section also frames state-tracking as a possible explanation, not a mechanism established by output accuracy.
E Original Exploratory Metric Analyses
- The original pooled analyses are retained as descriptive comparisons because their row-level tests and intervals do not account for repeated arms from the same case.The dynamic headline rows pool all four arms for each model–study setting; corrected tests are restricted to 45 estimable Python long-final tests.Source: E Original Exploratory Metric Analyses · pp. 12–13
- Cumulative state load meets the nominal pooled association rule in 14 of 15 estimable Python series, versus 4 of 14 C++ series.Trace length meets the rule in 12 Python and 9 C++ series; peak state size meets it in 14 Python and 0 C++ series.Source: E Original Exploratory Metric Analyses · p. 12
- On lcb_python, pooled odds ratios per doubling of cumulative state load range from 1.05 to 1.14, with all seven slopes nominally significant.On classic, six of seven finite fits are nominally significant, with supported odds ratios from 1.28 to 2.06; the slopes are cohort- and adapter-specific.Source: E Original Exploratory Metric Analyses · p. 12, Figure 2 · p. 13
- These pooled associations are exploratory because each case contributes four related rows and arm identity is unmodeled.Of 45 estimable Python long-final tests, none survives Benjamini–Hochberg correction at 0.05; the supported conclusion is descriptive and depends on task grouping and measurement convention.Source: E Original Exploratory Metric Analyses · p. 12
- One possible explanation is that models maintain or reconstruct concrete state through updates, but output accuracy alone cannot resolve this internal-computation hypothesis.Trace length, peak state size, and cumulative state load are correlated, and the analysis does not establish that the composite adds predictive value beyond its components.Source: E Original Exploratory Metric Analyses · p. 12
F Exploratory Invariant-Guidance Pilot
The invariant-guidance pilot tests whether audited, answer-free relations help exact prediction on difficult long-trace cases, while the real-case examples show checkpoint placement within and after a loop. The pilot's small, format-sensitive gains establish neither reliable accuracy improvement nor efficiency benefit.
F Exploratory Invariant-Guidance Pilot
- The invariants state a recurrence, run-length property, or decomposition rule, were checked but not formally proved, and contain neither the final output nor an evaluated endpoint.Rationale inspection finds some answers state the recurrence correctly but lose precision in arithmetic, state tracking, input reading, or transcription.Source: F Exploratory Invariant-Guidance Pilot · p. 15
- The pilot shows a two-answer gain under the exact-output contract and no aggregate gain under the visible-rationale contract.It covers 12 long-trace programs, with three fresh sessions per case under each prompt and two response contracts.Source: F Exploratory Invariant-Guidance Pilot · p. 15, Table 9 · p. 15
- Median hidden-reasoning tokens fall in both comparisons, but summed token use changes by −16.7% in exact-output and +1.9% in visible-rationale.The pilot's small, format-sensitive gains establish neither reliable accuracy improvement nor efficiency benefit.Source: F Exploratory Invariant-Guidance Pilot · p. 15
G Real-Case State Transformations
- For abc310_e, the inside checkpoint emits state after 501 complete iterations, while the post-loop checkpoint follows all 1,002 iterations.The inside arm terminates at its deterministic checkpoint; the post arm emits immediately after the loop and before later code can alter state.Source: G Real-Case State Transformations · p. 15
- The two checkpoints serialize the same fields, with oracle values changing from {"ans":63250,"cnt1":500,"dp1":500} inside the loop to {"ans":251502,"cnt1":2,"dp1":2} afterward.The benchmark uses deterministic canonical JSON serialization; the displayed emit calls abbreviate the committed serialization code.Source: G Real-Case State Transformations · p. 15