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

Researchers present a multi-agent LLM pipeline architecture that reduces hallucinations by 31-36% through nested learning, semantic caching, and progressive review stages. The system simultaneously improves factual reliability, cuts energy consumption by 47%, and enhances auditability without requiring model retraining.

This research addresses a critical vulnerability in production LLM systems: hallucination propagation across multi-stage pipelines. The paper demonstrates that architectural design—rather than model retraining—can substantially mitigate false claims in AI outputs. The three-stage pipeline uses asymmetric temperature settings, with a high-stochasticity generator followed by progressive correctors, creating a practical validation framework that measures both hallucination reduction and operational cost.

The work builds on established concerns about LLM reliability in production environments, where unsupported claims can compound across decision-making chains. Multi-agent systems have emerged as a promising direction for improving outputs through iterative refinement, and this research quantifies that approach's effectiveness. The semantic caching innovation—achieving 47.3% hit rate and reducing invocations by 47%—addresses a secondary industry pain point: the computational cost of running multiple LLM stages sequentially.

For practitioners deploying LLMs at scale, these findings have immediate relevance. The ability to improve factual grounding while simultaneously reducing energy footprint and CO2 emissions creates a compelling economic and sustainability case. The ExtremeObservability configuration achieving the best results suggests that auditability and reliability reinforce rather than contradict each other, challenging common trade-off assumptions.

The reliance on a 310-prompt benchmark limits generalizability claims, and real-world hallucination patterns may differ from the constructed test cases. Future validation across diverse domains and production datasets will determine whether these improvements sustain at genuine enterprise scale. The lack of model retraining dependency makes this approach broadly applicable across different LLM families.

• →Multi-agent review pipelines reduce hallucination scores by 31-36% without requiring model retraining or fine-tuning
• →Semantic caching achieves 47% hit rate, reducing LLM calls by 47% and lowering operational costs and carbon footprint
• →Observability-heavy configurations improve both factual reliability and auditability simultaneously, resolving apparent trade-offs
• →Asymmetric temperature settings across pipeline stages (1.0 generator vs. lower correctors) enable effective hallucination detection and correction
• →Architecture-based mitigation approaches offer immediate deployment value for production LLM systems facing reliability constraints