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

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.

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 -- 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.