{"slug": "hallucination-mitigation-with-agentic-ai-nested-learning-and-ai-sustainability", "title": "Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching", "summary": "Researchers have developed a multi-agent AI pipeline that reduces hallucination rates by up to 35.9% across 310 test prompts while cutting energy consumption by 47.3% through semantic caching. The system uses a three-stage architecture with continuum memory and observability metrics to detect and correct unsupported claims without retraining models. The findings demonstrate that memory-augmented agentic designs can simultaneously improve factual reliability, operational efficiency, and auditability in production LLM systems.", "body_md": "arXiv:2605.29055v1 Announce Type: new\nAbstract: 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 -- FCD (Factual Claim Density), FGR (Factual Grounding References), FDF (Fictional Disclaimer Frequency), ECS (Explicit Contextualization Score), and OSR (Observability Score Ratio) -- aggregated into THS (Total Hallucination Score) across five weighting configurations to study mitigation-observability trade-offs. FDF, ECS, OSR, and FGR are subtracted as mitigation signals, so that a more negative THS indicates stronger mitigation. The FrontEndAgent is configured as a high-stochasticity generator (temperature = 1.0) to produce a realistic hallucination baseline, while the SecondLevelReviewer and ThirdLevelReviewer operate as progressive correctors. This asymmetric design yields end-to-end THS reductions of -31.3% to -35.9% across five weighting configurations. Semantic caching achieves 440 cache hits over 930 potential calls (47.3% hit rate), reducing LLM invocations to 490, lowering energy and CO2e footprint, and making multi-stage review pipelines operationally viable at production scale. ExtremeObservability attains the most negative final THS (-0.0709), confirming that observability-heavy configurations reinforce rather than compromise mitigation. These findings suggest that memory-augmented multi-agent designs can jointly improve factual reliability, operational efficiency, and auditability without model retraining.", "url": "https://wpnews.pro/news/hallucination-mitigation-with-agentic-ai-nested-learning-and-ai-sustainability", "canonical_source": "https://arxiv.org/abs/2605.29055", "published_at": "2026-05-29 04:00:00+00:00", "updated_at": "2026-05-29 04:21:43.746099+00:00", "lang": "en", "topics": ["large-language-models", "ai-safety", "ai-research", "ai-agents", "ai-infrastructure"], "entities": ["HOPE", "Continuum Memory Systems", "Open Floor Protocol", "FrontEndAgent", "SecondLevelReviewer", "ThirdLevelReviewer"], "alternates": {"html": "https://wpnews.pro/news/hallucination-mitigation-with-agentic-ai-nested-learning-and-ai-sustainability", "markdown": "https://wpnews.pro/news/hallucination-mitigation-with-agentic-ai-nested-learning-and-ai-sustainability.md", "text": "https://wpnews.pro/news/hallucination-mitigation-with-agentic-ai-nested-learning-and-ai-sustainability.txt", "jsonld": "https://wpnews.pro/news/hallucination-mitigation-with-agentic-ai-nested-learning-and-ai-sustainability.jsonld"}}