Generative Reward Models Fix Deceptive Autoformalization in Neurosymbolic Reasoning
CWRU · hf · 2026-09-12
A new paper from CWRU, Beyond Solver Verdicts: Generative Reward Models for Autoformalization, identifies a vulnerability in neurosymbolic reasoning: autoformalization can produce incorrect formal translations that still match solver verdicts, deceiving verification. The authors propose a generative verification method that scores reference equivalence without an oracle and improves downstream accuracy.
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