AI RESEARCH
Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching
arXiv CS.AI
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ArXi:2605.29055v1 Announce Type: new Hallucination remains a major reliability barrier for production LLM systems, particularly in multi-agent pipelines where uned 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.