# Toward Verifiable Misinformation Detection: A Multi-Tool LLM Agent Framework

arXiv: https://arxiv.org/abs/2508.03092 · HTML: https://mlsanity.com/paper/2508.03092/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

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

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection](https://mlsanity.com/paper/2309.12247/) (2023)
- [TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection](https://mlsanity.com/paper/2402.07776/) (2024)
- [A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases](https://mlsanity.com/paper/2410.20293/) (2024)
- [Fake News Detection After LLM Laundering: Measurement and Explanation](https://mlsanity.com/paper/2501.18649/) (2025)
- [Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets](https://mlsanity.com/paper/2309.11576/) (2023)
- [CL-ISR: A Contrastive Learning and Implicit Stance Reasoning Framework for Misleading Text Detection on Social Media](https://mlsanity.com/paper/2506.05107/) (2025)

## Built on by (most cited first, 6 of 6)

- [FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents](https://mlsanity.com/paper/2606.12087/) (2026)
- [Tractable Asymmetric Verification for Large Language Models via Deterministic Replicability](https://mlsanity.com/paper/2509.11068/) (2025)
- [AI-Driven Decision-Making System for Hiring Process](https://mlsanity.com/paper/2512.20652/) (2025)
- [Multi-Agentic System Leveraging Open-Source LLMs to Mitigate Disinformation Threats](https://mlsanity.com/paper/2606.30259/) (2026)
- [Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability](https://mlsanity.com/paper/2607.10402/) (2026)
- [Semantics of Subterfuge: Benchmarking Legal Deception Detection Against General-domain State-of-the-Art](https://mlsanity.com/paper/2607.29066/) (2026)

## Abstract (arXiv)

With the proliferation of Large Language Models (LLMs), the detection of misinformation has become increasingly important and complex. This research proposes an innovative verifiable misinformation detection LLM agent that goes beyond traditional true/false binary judgments. The agent actively verifies claims through dynamic interaction with diverse web sources, assesses information source credibility, synthesizes evidence, and provides a complete verifiable reasoning process. Our designed agent architecture includes three core tools: precise web search tool, source credibility assessment tool and numerical claim verification tool. These tools enable the agent to execute multi-step verification strategies, maintain evidence logs, and form comprehensive assessment conclusions. We evaluate using standard misinformation datasets such as FakeNewsNet, comparing with traditional machine learning models and LLMs. Evaluation metrics include standard classification metrics, quality assessment of reasoning processes, and robustness testing against rewritten content. Experimental results show that our agent outperforms baseline methods in misinformation detection accuracy, reasoning transparency, and resistance to information rewriting, providing a new paradigm for trustworthy AI-assisted fact-checking.