Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing

TL;DR

This paper introduces SAVeR, a framework for LLM agents that verifies and repairs internal reasoning to ensure logical consistency and faithfulness, improving long-term decision accuracy.

Contribution

SAVeR is the first method to enforce internal belief verification and repair in LLM agents, enhancing reasoning faithfulness without sacrificing task performance.

Findings

Improves reasoning faithfulness across six benchmark datasets.

Maintains competitive performance on end tasks.

Effectively localizes and repairs reasoning violations.

Abstract

In large language model (LLM) agents, reasoning trajectories are treated as reliable internal beliefs for guiding actions and updating memory. However, coherent reasoning can still violate logical or evidential constraints, allowing unsupported beliefs repeatedly stored and propagated across decision steps, leading to systematic behavioral drift in long-horizon agentic systems. Most existing strategies rely on the consensus mechanism, conflating agreement with faithfulness. In this paper, inspired by the vulnerability of unfaithful intermediate reasoning trajectories, we propose \textbf{S}elf-\textbf{A}udited \textbf{Ve}rified \textbf{R}easoning (\textsc{SAVeR}), a novel framework that enforces verification over internal belief states within the agent before action commitment, achieving faithful reasoning. Concretely, we structurally generate persona-based diverse candidate beliefs for selection under a faithfulness-relevant structure space. To achieve reasoning faithfulness, we perform adversarial auditing to localize violations and repair through constraint-guided minimal interventions under verifiable acceptance criteria. Extensive experiments on six benchmark datasets demonstrate that our approach consistently improves reasoning faithfulness while preserving competitive end-task performance.