Neuro-Symbolic Verification on Instruction Following of LLMs

• URL: http://arxiv.org/abs/2601.17789v1
• Date: Sun, 25 Jan 2026 11:03:15 GMT
• Title: Neuro-Symbolic Verification on Instruction Following of LLMs
• Authors: Yiming Su, Kunzhao Xu, Yanjie Gao, Fan Yang, Cheng Li, Mao Yang, Tianyin Xu,
• Abstract summary: NSVIF is a neuro-symbolic framework for verifying whether an LLM's output follows the instructions used to prompt the LLM.<n>NSVIF formulates instruction-following verification as a constraint-satisfaction problem by modeling user instructions as constraints.<n>We develop VIFBENCH, a new benchmark for instruction-following verifiers with fine-grained data labels.
• Score: 12.64007593490092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A fundamental problem of applying Large Language Models (LLMs) to important applications is that LLMs do not always follow instructions, and violations are often hard to observe or check. In LLM-based agentic workflows, such violations can propagate and amplify along reasoning chains, causing task failures and system incidents. This paper presents NSVIF, a neuro-symbolic framework for verifying whether an LLM's output follows the instructions used to prompt the LLM. NSVIF is a universal, general-purpose verifier; it makes no assumption about the instruction or the LLM. NSVIF formulates instruction-following verification as a constraint-satisfaction problem by modeling user instructions as constraints. NSVIF models both logical and semantic constraints; constraint solving is done by a unified solver that orchestrates logical reasoning and semantic analysis. To evaluate NSVIF, we develop VIFBENCH, a new benchmark for instruction-following verifiers with fine-grained data labels. Experiments show that NSVIF significantly outperforms LLM-based approaches and provides interpretable feedback. We also show that feedback from NSVIF helps improve LLMs' instruction-following capability without post-training.

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