NightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track
Quick Answer
This paper shows that NightFeats, a multi-agent RAG system, won Best Dynamic Evaluation at NeurIPS 2025, outperforming Claude-SonnetV2 and Nova-Pro.
Quick Take
It emphasizes a structured approach with retrieval, curation, and composition phases, achieving better alignment with human preferences over traditional metrics.
Key Points
- NightFeats decomposes knowledge synthesis into retrieval, curation, and composition phases.
- Introduces core primitives like temporal-semantic reranking and citation-preserving composition.
- Outperformed proprietary models on -as-a-Judge and Human Likert evaluations.
- Architectural transparency enhances alignment with human preferences over automatic metrics.
- Awarded Best Dynamic Evaluation in the text-to-text track at NeurIPS 2025.
Paper Resources
Source Excerpt
arXiv:2606. 11199v1 Announce Type: new Abstract: We present NightFeats, a structured (RAG) system submitted to the MMU-RAGent competition at NeurIPS 2025, where it was awarded Best Dynamic Evaluation in the text-to-text track.
Rather than targeting benchmark maximization, this work proposes a principled pipeline that decomposes knowledge synthesis into three coordinated phases: retrieval, curation, and composition, each governed by explicit intermediate representations and handoff contracts. …
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