# Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks

arXiv: https://arxiv.org/abs/2501.13946 · HTML: https://mlsanity.com/paper/2501.13946/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 19 Jan 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

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

- [Automated Design of Agentic Systems](https://mlsanity.com/paper/2408.08435/) (2024)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [Measuring Attribution in Natural Language Generation Models](https://mlsanity.com/paper/2112.12870/) (2021)
- [Agent AI: Surveying the Horizons of Multimodal Interaction](https://mlsanity.com/paper/2401.03568/) (2024)
- [Interpreting and Mitigating Hallucination in MLLMs through Multi-agent Debate](https://mlsanity.com/paper/2407.20505/) (2024)
- [Good Parenting is all you need -- Multi-agentic LLM Hallucination Mitigation](https://mlsanity.com/paper/2410.14262/) (2024)
- [AI Multi-Agent Interoperability Extension for Managing Multiparty Conversations](https://mlsanity.com/paper/2411.05828/) (2024)
- [Conversational AI Multi-Agent Interoperability, Universal Open APIs for Agentic Natural Language Multimodal Communications](https://mlsanity.com/paper/2407.19438/) (2024)

## Built on by (most cited first, 12 of 12)

- [LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions](https://mlsanity.com/paper/2509.18970/) (2025)
- [Prompt Injection Detection and Mitigation via AI Multi-Agent NLP Frameworks](https://mlsanity.com/paper/2503.11517/) (2025)
- [Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies](https://mlsanity.com/paper/2510.22095/) (2025)
- [Prompt Injection Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching](https://mlsanity.com/paper/2601.13186/) (2026)
- [Performance Comparison of IBN orchestration using LLM and SLMs](https://mlsanity.com/paper/2603.06647/) (2026)
- [MADP: A Multi-Agent Pipeline for Sustainable Document Processing with Human-in-the-Loop](https://mlsanity.com/paper/2605.17159/) (2026)
- [Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching](https://mlsanity.com/paper/2605.29055/) (2026)
- [The Case for Repeatable, Open, and Expert-Grounded Hallucination Benchmarks in Large Language Models](https://mlsanity.com/paper/2505.17345/) (2025)
- [Ghost Framing Theory: Exploring the role of generative AI in new venture rhetorical legitimation](https://mlsanity.com/paper/2603.11384/) (2026)
- [The Inverse-Wisdom Law: Architectural Tribalism and the Consensus Paradox in Agentic Swarms](https://mlsanity.com/paper/2604.27274/) (2026)
- [A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models](https://mlsanity.com/paper/2606.26879/) (2026)
- [Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog](https://mlsanity.com/paper/2607.21412/) (2026)

## Abstract (arXiv)

Hallucinations remain a significant challenge in current Generative AI models, undermining trust in AI systems and their reliability. This study investigates how orchestrating multiple specialized Artificial Intelligent Agents can help mitigate such hallucinations, with a focus on systems leveraging Natural Language Processing (NLP) to facilitate seamless agent interactions. To achieve this, we design a pipeline that introduces over three hundred prompts, purposefully crafted to induce hallucinations, into a front-end agent. The outputs are then systematically reviewed and refined by second- and third-level agents, each employing distinct large language models and tailored strategies to detect unverified claims, incorporate explicit disclaimers, and clarify speculative content. Additionally, we introduce a set of novel Key Performance Indicators (KPIs) specifically designed to evaluate hallucination score levels. A dedicated fourth-level AI agent is employed to evaluate these KPIs, providing detailed assessments and ensuring accurate quantification of shifts in hallucination-related behaviors. A core component of this investigation is the use of the OVON (Open Voice Network) framework, which relies on universal NLP-based interfaces to transfer contextual information among agents. Through structured JSON messages, each agent communicates its assessment of the hallucination likelihood and the reasons underlying questionable content, thereby enabling the subsequent stage to refine the text without losing context. The results demonstrate that employing multiple specialized agents capable of interoperating with each other through NLP-based agentic frameworks can yield promising outcomes in hallucination mitigation, ultimately bolstering trust within the AI community.