3 papers
cs.LG2026
Beyond Solver Verdicts: Generative Reward Models for Autoformalization
Vikash Singh, Debargha Ganguly, Aman Goel +5
Neurosymbolic systems rely on mathematical solvers to guarantee reasoning correctness, yet solvers are fundamentally blind to whether a formal translation maintains strict referenc…
cs.CL2026
Stratified Consistency Distillation for Natural Language Formalization
Zhichao Hou, Ferhat Erata, Joe Lilien +1
Neurosymbolic reasoning has shown promising success in addressing complex reasoning tasks by combining large language models (LLMs) and symbolic solvers. While this approach shows…
cs.CL2025
A Neurosymbolic Approach to Natural Language Formalization and Verification
Chenyang An, Sam Bayless, Stefano Buliani +27
Large Language Models perform well at natural language interpretation and reasoning, but their lack of formal correctness guarantees limits their adoption in regulated industries l…