Neuro-Symbolic Anchored Governance for Alleviating Action-Level Hallucinations in Large Language Model Agents

Large Language Model (LLM) agents frequently suffer from action-level hallucinations. Unlike traditional hallucinations that appear as factual inconsistencies in text generation, action-level hallucinations involve proposing actions that are semantically plausible but violate environment-specific feasibility or procedural constraints. Probabilistic generation alone does not ensure action legality, and violation risks can compound over long action sequences. Explicit checks identify violations, but agents must also determine when to intervene and how to recover. To address this, we propose Neuro-Symbolic Anchored Governance (NSAG), a framework that externalizes rule enforcement from the LLM to a symbolic execution interface. NSAG selects task-relevant rules and learns to calibrate intervention using symbolic violation signals. To resume execution when an action is blocked, we introduce MinFix, a search-based repair operator that seeks a minimal executable sequence satisfying the action's missing preconditions while preserving high-level intent. We develop and release Town-Benchmark with 1,000 task instances across five domains and evaluate NSAG on Town-Benchmark and ScienceWorld. NSAG outperforms state-of-the-art baselines in hallucination mitigation and task performance, with consistent gains across LLM backbones and low inference overhead. The reductions are empirical, and the calibration is fitted on the same five domains it is evaluated on. The open environment and code are available at https://anonymous.4open.science/r/NSAG/.