mlsanityData to 7 Oct 2026

RMIT-ADM+S at the MMU-RAG NeurIPS 2025 Competition

arXiv 2602.20735 ↗First posted 24 Feb 2026cs.IR cs.AI cs.CLcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

This paper presents the award-winning RMIT-ADM+S system for the Text-to-Text track of the NeurIPS~2025 MMU-RAG Competition. We introduce Routing-to-RAG (R2RAG), a research-focused retrieval-augmented generation (RAG) architecture composed of lightweight components that dynamically adapt the retrieval strategy based on inferred query complexity and evidence sufficiency. The system uses smaller LLMs, enabling operation on a single consumer-grade GPU while supporting complex research tasks. It builds on the G-RAG system, winner of the ACM~SIGIR~2025 LiveRAG Challenge, and extends it with modules informed by qualitative review of outputs. R2RAG won the Best Dynamic Evaluation award in the Open Source category, demonstrating high effectiveness with careful design and efficient use of resources.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 12 of its 19 references that are in the map. First posted on arXiv 24 Feb 2026.

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12 of 19 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track24 Jun

Other references · 11

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2025Qwen3 Technical Reportcited by 8,195
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  4. 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,175
  5. 2023Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Studycited by 43
  6. 2021TREC Deep Learning Track: Reusable Test Collections in the Large Data Regimecited by 23
  7. 2025Rankers, Judges, and Assistants: Towards Understanding the Interplay of LLMs in Information Retrieval Evaluationcited by 15
  8. 2025LLM-Evaluation Tropes: Perspectives on the Validity of LLM-Evaluationscited by 14
  9. 2025Does quantization affect models' performance on long-context tasks?cited by 10
  10. 2025Towards Understanding Bias in Synthetic Data for Evaluationcited by 7
  11. 2025RMIT-ADM+S at the SIGIR 2025 LiveRAG Challengecited by 1

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  1. 2025RAGO: Systematic Performance Optimization for Retrieval-Augmented Generation Serving
  2. 2026RAGMark: A Comprehensive Framework for Benchmarking Retrieval-Augmented Generation Systems
  3. 2026RAGe: A Retrieval-Augmented Generation Evaluation Framework
  4. 2026Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite
  5. 2026RAGRouter-Bench: A Dataset and Benchmark for Adaptive RAG Routing
  6. 2024XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
  7. 2024Toward Optimal Search and Retrieval for RAG
  8. 2026Cost-Aware Query Routing in RAG: Empirical Analysis of Retrieval Depth Tradeoffs