NightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track
arXiv:2606.11199v1 Announce Type: new Abstract: We present NightFeats, a structured multi-agent retrieval-augmented generation (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 Context Engineering (ACE), the system int
The rapid advancement in large language models and multi-modal AI is driving the next wave of innovation in autonomous agent systems and their application in knowledge synthesis.
This development showcases a structured and principled approach to multi-agent RAG systems, indicating a maturing field that could lead to more reliable and context-optimized AI applications.
The focus on explicit intermediate representations and 'handoff contracts' between AI agents introduces a more robust and auditable methodology for complex AI workflows, moving beyond simpler RAG implementations.
- · AI platform developers
- · Enterprises leveraging AI for knowledge management
- · Research institutions specializing in AI agents
- · AI infrastructure providers
- · Legacy knowledge management software
- · Manual intelligence analysis firms
- · Undifferentiated RAG solution providers
Improved accuracy and reliability of AI-generated content, particularly in complex information synthesis tasks.
Accelerated development of fully autonomous AI agents capable of collapsing multi-step white-collar workflows.
Potential for AI systems to generate novel insights and research findings by autonomously synthesizing vast and disparate information sources.
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