mlsanityData to 14 Oct 2026

CIIR@LiveRAG 2025: Optimizing Multi-Agent Retrieval Augmented Generation through Self-Training

arXiv 2506.10844 ↗First posted 12 Jun 2025cs.CL cs.IRcited by 2

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

Abstract (arXiv)

This paper presents mRAG, a multi-agent retrieval-augmented generation (RAG) framework composed of specialized agents for subtasks such as planning, searching, reasoning, and coordination. Our system uses a self-training paradigm with reward-guided trajectory sampling to optimize inter-agent collaboration and enhance response generation. Evaluated on DataMorgana-derived datasets during the SIGIR 2025 LiveRAG competition, mRAG outperforms conventional RAG baselines. We further analyze competition outcomes and showcase the framework's strengths with case studies, demonstrating its efficacy for complex, real-world RAG tasks.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 11 of its 22 references that are in the map. First posted on arXiv 12 Jun 2025.

Builds on

11 of 22 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG15 Jan
  2. 2024Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization13 Oct

Other references · 9

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  3. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  4. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
  5. 2023Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Modelscited by 349
  6. 2025Generating Diverse Q&A Benchmarks for RAG Evaluation with DataMorganacited by 13
  7. 2025Scaling Sparse and Dense Retrieval in Decoder-Only LLMscited by 12
  8. 2024Retrieval-Enhanced Machine Learning: Synthesis and Opportunitiescited by 11
  9. 2023Towards Efficient Multi-Agent Learning Systemscited by 4

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Uncertainty Quantification for Retrieval-Augmented Reasoningcited by 6
  2. 2025SIGIR 2025 -- LiveRAG Challenge Reportcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Experience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts
  2. 2025C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation
  3. 2025Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning
  4. 2025Interact-RAG: Reason and Interact with the Corpus, Beyond Black-Box Retrieval
  5. 2025SIRAG: Towards Stable and Interpretable RAG with A Process-Supervised Multi-Agent Framework
  6. 2025Efficient Agent: Optimizing Planning Capability for Multimodal Retrieval Augmented Generation
  7. 2025DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision
  8. 2025MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning