Auto57%Harbor: pendingCoding

CRUXEval-style

Evaluating Exact Output and Checkpoint-State Prediction in Real Programs

A benchmark of 400 cases from 371 Python/C++ programs measuring model ability to predict final output and intermediate checkpoint states without code execution.

How to run

ltb · cruxeval-style
$ npm i -g lettokenburn-cli
$ ltb bench info cruxeval-style

External harness. No Harbor adapter has been generated for this benchmark yet. `ltb bench info` prints the known links.

Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs

Scores

7 sourced entries
HarnessSourceConfidenceLink
GPT-5.6 Sol74.8N/AIndependentmediumOct 8, 2026arxiv.org
DeepSeek V4 Pro 081363.8N/AIndependentmediumOct 8, 2026arxiv.org
GLM-5.355.3N/AIndependentmediumOct 8, 2026arxiv.org
Qwen3.8 27B39.4N/AIndependentmediumOct 8, 2026arxiv.org
GPT-5.6 Sol Off24.5N/AIndependentmediumOct 8, 2026arxiv.org
DeepSeek V4 Pro 0813 Off8.6N/AIndependentmediumOct 8, 2026arxiv.org
Qwen3.8 27B Off6.3N/AIndependentmediumOct 8, 2026arxiv.org

Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.

Reproduced by the community

All runs
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About

The benchmark comprises 400 cases derived from 371 distinct Python and C++ programs, including contest solutions and algorithms. It features paired short/long trace inputs and checkpoint state predictions.

Scoring evaluates exact match on gradable responses across seven model settings. The strongest setting achieved 93.0% on short outputs but dropped to 65.5% on state tasks, highlighting difficulties with longer executions.