Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models
arXiv:2606.31630v1 Announce Type: new Abstract: Language models increasingly write probabilistic programs (in NumPyro, Stan, or Pyro), but a program that compiles, runs, and passes every unit test can still be \emph{statistically} wrong -- a Gaussian likelihood for heavy-tailed data, a Poisson for over-dispersed counts, an invalid prior support, or a pathological parameterization. The right verifier is therefore not a test suite but the Bayesian workflow itself: posterior predictive checks, simulation-based calibration, sampler diagnostics ($\hat R$, divergences, ESS), and held-out predictive
The increasing sophistication of language models in generating code, including probabilistic programs, necessitates robust verification methods to ensure statistical correctness beyond mere compilation.
As AI systems automate more complex tasks, including scientific modeling and data analysis, ensuring the statistical validity of their generated code is critical for reliable decision-making and trust.
The focus for verifying AI-generated probabilistic programs shifts from simple compilation and unit testing to advanced statistical calibration and Bayesian workflow diagnostics, setting a higher bar for AI reliability.
- · Developers of statistical verification tools
- · Organizations relying on AI for probabilistic modeling
- · AI safety and ethics researchers
- · Organizations deploying unverified AI-generated models
- · Developers neglecting statistical validation
- · AI applications in sensitive sectors prone to statistical errors
Increased demand for tools and expertise in statistical program verification for AI-generated code.
Higher standards for deploying AI systems that generate probabilistic models, requiring integrated 'calibration as a service' or similar solutions.
Improved reliability and trustworthiness of AI in scientific research and complex decision-making, potentially accelerating AI adoption in critical fields.
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