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

arXiv: https://arxiv.org/abs/2604.08401 · HTML: https://mlsanity.com/paper/2604.08401/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 8 of its 58 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 8 of 8)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens](https://mlsanity.com/paper/2508.01191/) (2025)
- [AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs](https://mlsanity.com/paper/2507.08616/) (2025)
- [A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models](https://mlsanity.com/paper/2509.11575/) (2025)
- [QA-Dragon: Query-Aware Dynamic RAG System for Knowledge-Intensive Visual Question Answering](https://mlsanity.com/paper/2508.05197/) (2025)
- [Multi-agent Undercover Gaming: Hallucination Removal via Counterfactual Test for Multimodal Reasoning](https://mlsanity.com/paper/2511.11182/) (2025)

## Built on by (most cited first, 7 of 7)

- [OpenClawBench: Benchmarking Process-side Anomalies in Real-world Agent Execution Trajectories](https://mlsanity.com/paper/2605.29253/) (2026)
- [When Stale Constraints Go Unchecked: Budgeted Verification Failures in Inherited Agent Memory](https://mlsanity.com/paper/2608.25553/) (2026)
- [Belief-Guided Inference Control for Large Language Model Services via Verifiable Observations](https://mlsanity.com/paper/2604.27536/) (2026)
- [CogEEGAgent: Toward Autonomous Cognitive EEG Analysis with Grounded Execution and Selection-Aware Verification](https://mlsanity.com/paper/2607.25045/) (2026)
- [Towards an Agent Operating System - Lessons from Classical and Cloud OS](https://mlsanity.com/paper/2607.25076/) (2026)
- [Theoria: Rewrite-Acceptability Verification over Informal Reasoning States](https://mlsanity.com/paper/2607.01223/) (2026)
- [What Do Rationales Communicate? A Message-Intervention Study in Role-Specialized QA](https://mlsanity.com/paper/2610.00018/) (2026)

## Abstract (arXiv)

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 Self-Audited Verified Reasoning (textscSAVeR), 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.