Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching

In three linesarXiv paper proposing multi-agent architecture with semantic memory and caching to mitigate LLM hallucinations. Three-stage pipeline (FrontEndAgent, SecondLevelReviewer, ThirdLevelReviewer) evaluated on 310 prompts. Results: THS reduction of -31.3% to -35.9%, 47.3% cache hit rate, 47% reduction in LLM calls. No retraining required.

Summary generated by Claude — human-verified