PyINE: A Framework for Scalable Elicitation and Oversight via Code Execution
PyINE tests whether overseers can catch shortcut-driven reasoning failures when execution traces provide the ground truth.
The paper introduces instrumented Python programs as task environments, with traces used to verify outcomes and intermediate facts. Its first release includes nearly one million deterministic traces and more than 500,000 matched LLM-generated code variants. The authors train a model that gets better at predicting execution outcomes but still fails when misleading cues conflict with what the program actually does. Their overseer tests find that dataset-level scores can mask poor coverage of rare, important failures, while stronger checks are costlier and harder to threshold reliably. ArXiv · AI/CL/LG's note
The paper introduces instrumented Python programs as task environments, with traces used to verify outcomes and intermediate facts. Its first release includes nearly one million deterministic traces and more than 500,000 matched LLM-generated code variants. The authors train a model that gets better at predicting execution outcomes but still fails when misleading cues conflict with what the program actually does. Their overseer tests find that dataset-level scores can mask poor coverage of rare, important failures, while stronger checks are costlier and harder to threshold reliably. ArXiv · AI/CL/LG's note
score 5