ACAI System Uses Multi-Stage Retrieval and Verification to Enhance LLM Accuracy

ACAI is an AI framework designed to address core limitations of large language models, particularly their inability to access information beyond their training cutoff. The system employs a multi-stage retrieval engine that generates optimized search queries, collects documents from diverse sources, ranks them by relevance, and filters out duplicates and outdated content. A context optimization layer then compresses retrieved material before passing only the most valuable information to the underlying language model. Rather than relying on a single model, ACAI uses a dynamic router to direct tasks to specialized models based on category, cost, and accuracy needs. A multi-agent collaboration layer and a logical verification engine further refine outputs by checking for inconsistencies, unsupported claims, and missing reasoning steps before a final response is delivered.