PyINE: A Framework for Scalable Elicitation and Oversight via Code Execution
Reasoning models can remain capable of solving a task while still defaulting to cheaper but misleading shortcuts. This creates a central oversight problem: when a model gives an answer with plausible but incomplete reasoning, can an overseer determine whether that output should be trusted? To study this problem, we introduce PyINE, a framework for scalable elicitation and oversight using instrumented Python programs as a verifiable execution substrate. In PyINE, programs define task environments, execution traces provide authoritative labels for outcomes and intermediate facts, and task variants can be generated mechanically rather than through static human annotation. We instantiate the ...
| Authors | Pierre-Luc St-Charles, Alessandro Palmas, Damiano Fornasiere et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 October 2026 |
| Last updated at source | 10 October 2026 |
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