Scaling Formal-Proof Training Data and Adversarial Robustness in Neuro-Symbolic Verifiers

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does increasing the size of formal-proof training datasets from 10k to 100k pairs impact the accuracy drop of neuro-symbolic verifiers on the miniF2F benchmark under adversarial perturbations. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does increasing the size of formal-proof training datasets from 10k to 100k pairs impact the accuracy drop of neuro-symbolic verifiers on the miniF2F benchmark under adversarial perturbations?

Autonomous literature synthesis. Automated review score: 8.3/10. Full text and citation available at Assignee Research.