FormalJudge: A Neuro-Symbolic Paradigm for Agentic Oversight

• URL: http://arxiv.org/abs/2602.11136v2
• Date: Thu, 12 Feb 2026 10:45:21 GMT
• Title: FormalJudge: A Neuro-Symbolic Paradigm for Agentic Oversight
• Authors: Jiayi Zhou, Yang Sheng, Hantao Lou, Yaodong Yang, Jie Fu,
• Abstract summary: This paper proposes a neuro-symbolic framework that employs a bidirectional Formal-of-Thought architecture.<n>We validate across three benchmarks spanning behavioral safety, multi-domain constraint adherence, and agentic upward deception detection.
• Score: 21.731032636844237
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As LLM-based agents increasingly operate in high-stakes domains with real-world consequences, ensuring their behavioral safety becomes paramount. The dominant oversight paradigm, LLM-as-a-Judge, faces a fundamental dilemma: how can probabilistic systems reliably supervise other probabilistic systems without inheriting their failure modes? We argue that formal verification offers a principled escape from this dilemma, yet its adoption has been hindered by a critical bottleneck: the translation from natural language requirements to formal specifications. This paper bridges this gap by proposing , a neuro-symbolic framework that employs a bidirectional Formal-of-Thought architecture: LLMs serve as specification compilers that top-down decompose high-level human intent into atomic, verifiable constraints, then bottom-up prove compliance using Dafny specifications and Z3 Satisfiability modulo theories solving, which produces mathematical guarantees rather than probabilistic scores. We validate across three benchmarks spanning behavioral safety, multi-domain constraint adherence, and agentic upward deception detection. Experiments on 7 agent models demonstrate that achieves an average improvement of 16.6% over LLM-as-a-Judge baselines, enables weak-to-strong generalization where a 7B judge achieves over 90% accuracy detecting deception from 72B agents, and provides near-linear safety improvement through iterative refinement.

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