Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor
arXiv:2507.15903v2 Announce Type: replace-cross Abstract: Empowered by large language models (LLMs), intelligent agents have become a popular paradigm for interacting with open environments to facilitate AI deployment. However, hallucinations generated by LLMs-where outputs are inconsistent with facts-pose a significant challenge, undermining the credibility of intelligent agents. Only if hallucinations can be mitigated, the intelligent agents can be used in real-world without any catastrophic risk. Therefore, effective detection and mitigation of hallucinations are crucial to ensure the depen
The rapid advancement and deployment of LLM-empowered agents necessitate urgent solutions for hallucination, which currently limits their real-world applicability.
Mitigating AI hallucination is critical for the trustworthy and safe deployment of AI agents across various industries, impacting their economic and societal integration.
Improved hallucination mitigation will enable more reliable and autonomous AI agents, expanding their utility and accelerating the automation of complex tasks.
- · AI agent developers
- · Enterprises adopting AI agents
- · AI safety researchers
- · Software as a Service (SaaS)
- · Companies with unreliable AI products
- · Sectors requiring high-stakes decision making without robust AI
- · Human intermediaries in automated workflows
Increased trust and adoption of AI-driven intelligent agents in sensitive applications.
Accelerated displacement of white-collar work previously deemed too complex for current AI reliability.
Potential for new regulatory frameworks specifically addressing agentic AI safety and accountability.
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Read at arXiv cs.AI