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
Article Excerpt
From source RSS / original summaryarXiv: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. Inspired by Agentic (ACE), the system introduces temporal-semantic reranking, bounded contradiction reconciliation, and citation-preserving composition as core architectural primitives.
Competition results show that NightFeats surpasses proprietary baselines including Claude-SonnetV2 and Nova-Pro on -as-a-Judge and Human Likert evaluations, confirming that architectural transparency and verifiable evidence grounding are better aligned with human preferences than systems optimizing narrowly for automatic similarity metrics.
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CL
See more →TriAgent: Divergence-Aware Committees for Cost-Efficient Financial Sentiment Analysis
TriAgent introduces a cost-efficient multi-agent system for financial sentiment analysis, combining VADER, FinBERT, and Qwen2.5. It achieves an F1 score of ~0.87 with significant savings of $9.3M/year at a 10M-user scale compared to GPT-4o-mini, while also detecting hallucinations with an AUC of 0.90.