mlsanityData to 14 Oct 2026

Using multi-agent architecture to mitigate the risk of LLM hallucinations

arXiv 2507.01446 ↗First posted 2 Jul 2025cs.AIcited by 1

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

Improving customer service quality and response time are critical factors for maintaining customer loyalty and increasing a company's market share. While adopting emerging technologies such as Large Language Models (LLMs) is becoming a necessity to achieve these goals, the risk of hallucination remains a major challenge. In this paper, we present a multi-agent system to handle customer requests sent via SMS. This system integrates LLM based agents with fuzzy logic to mitigate hallucination risks.

Key facts. Cited by 1 paper 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 25 references that are in the map. First posted on arXiv 2 Jul 2025.

Builds on

8 of 25 references.

References · 8

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  3. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  4. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 769
  5. 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMscited by 591
  6. 2023GPTScore: Evaluate as You Desirecited by 361
  7. 2023Towards Mitigating Hallucination in Large Language Models via Self-Reflectioncited by 81
  8. 2024"Ask Me Anything": How Comcast Uses LLMs to Assist Agents in Real Timecited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Beyond Final Answers: Auditing Trajectory-Level Hallucinations in Multi-Agent Industrial Workflowscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions
  2. 2024Good Parenting is all you need -- Multi-agentic LLM Hallucination Mitigation
  3. 2024Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models
  4. 2026Hallucination Cascade: Analyzing Error Propagation in Multi-Agent LLM Systems
  5. 2025Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks
  6. 2026Collective Hallucination in Multi-Agent LLMs:Modeling and Defense
  7. 2025Mitigating Hallucination in Large Language Models (LLMs): An Application-Oriented Survey on RAG, Reasoning, and Agentic Systems
  8. 2024Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models