Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching
arXiv:2605.29055v1 Announce Type: new Abstract: Hallucination remains a major reliability barrier for production LLM systems, particularly in multi-agent pipelines where unsupported claims can propagate unchecked across stages. This paper adapts a HOPE-inspired Nested Learning architecture with Continuum Memory Systems (CMS) and semantic similarity caching to a hybrid benchmark of 310 prompts combining 217 epistemic-uncertainty prompts and 93 fabrication-induction stress-test prompts. A three-stage agentic pipeline orchestrated via the Open Floor Protocol (OFP) is evaluated with five KPIs -- F
The proliferation of LLM systems in production environments necessitates robust solutions for hallucination mitigation, especially in multi-agent architectures that compound reliability issues.
Reliable AI systems are critical for enterprise adoption and trust, making advancements in hallucination mitigation a key driver for broader AI integration and responsible deployment.
This research outlines a promising architectural approach to significantly reduce LLM hallucinations in complex agentic pipelines, potentially accelerating the development of more trustworthy AI applications.
- · AI developers
- · Enterprises deploying LLMs
- · AI-as-a-Service providers
- · Companies relying on unreliable LLM outputs
- · Research without practical mitigation strategies
Improved reliability and broader deployment of LLM-based autonomous agents become possible.
Increased trust in AI systems could accelerate automation across various industries.
More sophisticated and interconnected AI agents might emerge, leading to new forms of white-collar task automation.
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Read at arXiv cs.AI