NightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track
A research paper details NightFeats, a multi-agent retrieval-augmented generation (RAG) system that won Best Dynamic Evaluation in the text-to-text track at the MMU-RAGent competition for NeurIPS 2025. The system employs a three-phase pipeline for knowledge synthesis: retrieval, curation, and composition, utilizing temporal-semantic reranking and contradiction reconciliation. Evaluations indicated NightFeats outperformed proprietary systems like Claude-SonnetV2 and Nova-Pro, suggesting that architectural transparency and verifiable evidence grounding are more aligned with human preferences than systems focused solely on automatic metrics. AI
IMPACT Demonstrates that transparent, evidence-grounded RAG systems can outperform proprietary models in human evaluations.