This paper addresses the reliability issues of hallucinations in production large language model systems by proposing a multi-stage agentic pipeline architecture. The researchers combine a HOPE-inspired Nested Learning framework with Continuum Memory Systems and semantic similarity caching to improve factual accuracy across complex tasks. By using an Open Floor Protocol to coordinate these stages, they demonstrate significant reductions in hallucination scores while improving the operational viability of deep review pipelines through high cache hit rates that lower energy consumption and carbon footprints.
Main topics:
- Nested Learning architecture for multi-agent systems
- Semantic similarity caching for reducing LLM invocations and CO2e footprint
- Open Floor Protocol (OFP) orchestration in agentic workflows
- Evaluation metrics including Factual Claim Density and Observability Score Ratio
- Reduction of end-to-end hallucination scores without model retraining
Main topics:
- Nested Learning architecture for multi-agent systems
- Semantic similarity caching for reducing LLM invocations and CO2e footprint
- Open Floor Protocol (OFP) orchestration in agentic workflows
- Evaluation metrics including Factual Claim Density and Observability Score Ratio
- Reduction of end-to-end hallucination scores without model retraining
