MuISQA: Multi-Intent Retrieval-Augmented Generation for Scientific Question Answering

• URL: http://arxiv.org/abs/2511.16283v1
• Date: Thu, 20 Nov 2025 12:03:36 GMT
• Title: MuISQA: Multi-Intent Retrieval-Augmented Generation for Scientific Question Answering
• Abstract summary: Multi-Intent Scientific Question Answering (MuISQA) benchmark designed to evaluate RAG systems on heterogeneous evidence coverage across sub-questions.<n>In addition, we propose an intent-aware retrieval framework that leverages large language models (LLMs) to hypothesize potential answers, decompose them into intent-specific queries, and retrieve supporting passages for each underlying intent.<n>Experiments on both MuISQA benchmark and other general RAG datasets demonstrate that our method consistently outperforms conventional approaches, particularly in retrieval accuracy and evidence coverage.
• Score: 16.26521741515184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Complex scientific questions often entail multiple intents, such as identifying gene mutations and linking them to related diseases. These tasks require evidence from diverse sources and multi-hop reasoning, while conventional retrieval-augmented generation (RAG) systems are usually single-intent oriented, leading to incomplete evidence coverage. To assess this limitation, we introduce the Multi-Intent Scientific Question Answering (MuISQA) benchmark, which is designed to evaluate RAG systems on heterogeneous evidence coverage across sub-questions. In addition, we propose an intent-aware retrieval framework that leverages large language models (LLMs) to hypothesize potential answers, decompose them into intent-specific queries, and retrieve supporting passages for each underlying intent. The retrieved fragments are then aggregated and re-ranked via Reciprocal Rank Fusion (RRF) to balance coverage across diverse intents while reducing redundancy. Experiments on both MuISQA benchmark and other general RAG datasets demonstrate that our method consistently outperforms conventional approaches, particularly in retrieval accuracy and evidence coverage.

Related papers

• Hybrid-IR: Dual-Path Hybrid Retrieval with Iterative Reasoning for Complex Medical Question Answering [19.561452074492653]
  We present a dual-path retrieval framework with an iterative retrieval-reasoning mechanism termed "Hybrid-IR"<n>The proposed Hybrid-IR integrates graph-based retrieval for exploration of structured knowledge and dense retrieval for fine-grained semantic matching.<n> Experiments on three widely used medical QA benchmarks demonstrate the effectiveness of our Hybrid-IR.
  arXiv  Detail & Related papers  (2026-06-24T03:09:50Z)
• MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation [36.73029890123542]
  MASS-RAG is a multi-agent synthesis approach to retrieval-augmented generation.<n>It applies distinct agents for evidence summarization, evidence extraction, and reasoning over retrieved documents.<n>It combines their outputs through a dedicated synthesis stage to produce the final answer.
  arXiv  Detail & Related papers  (2026-04-20T17:00:38Z)
• From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG [65.0203623486525]
  Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering.<n>Their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields.<n>Existing methods rely on noisy token-level signals and lack the multi-round refinement required for complex reasoning.
  arXiv  Detail & Related papers  (2026-02-06T08:25:30Z)
• When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering [0.2796197251957245]
  We study whether synchronized iterative retrieval and reasoning can surpass an idealized static upper bound (Gold Context) RAG.<n>We benchmark eleven state-of-the-art LLMs under three regimes: (i) No Context, measuring reliance on parametric memory; (ii) Gold Context, where all evidence is supplied at once; and (iii) Iterative RAG, a training-free oracle controller that alternates retrieval, hypothesis refinement, and evidence-aware stopping.<n>Across models, Iterative RAG consistently outperforms Gold Context, with gains up to 25.6 percentage points, especially for non-reasoning
  arXiv  Detail & Related papers  (2026-01-27T17:35:05Z)
• ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering [54.72902502486611]
  ReAG is a Reasoning-Augmented Multimodal RAG approach that combines coarse- and fine-grained retrieval with a critic model that filters irrelevant passages.<n>ReAG significantly outperforms prior methods, improving answer accuracy and providing interpretable reasoning grounded in retrieved evidence.
  arXiv  Detail & Related papers  (2025-11-27T19:01:02Z)
• FAIR-RAG: Faithful Adaptive Iterative Refinement for Retrieval-Augmented Generation [0.0]
  We introduce FAIR-RAG, a novel agentic framework that transforms the standard RAG pipeline into a dynamic, evidence-driven reasoning process.<n>We conduct experiments on challenging multi-hop QA benchmarks, including HotpotQA, 2WikiMultiHopQA, and MusiQue.<n>Our work demonstrates that a structured, evidence-driven refinement process with explicit gap analysis is crucial for unlocking reliable and accurate reasoning in advanced RAG systems.
  arXiv  Detail & Related papers  (2025-10-25T15:59:33Z)
• Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs [69.10441885629787]
  Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge.<n>It falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts.<n>This survey synthesizes both strands under a unified reasoning-retrieval perspective.
  arXiv  Detail & Related papers  (2025-07-13T03:29:41Z)
• Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation [39.545788636148025]
  We present the first comprehensive study of position bias in multimodal RAG systems.<n>Our results reveal that multimodal interactions intensify position bias compared to unimodal settings.<n>These findings highlight the need for evidence reordering or debiasing strategies to build more reliable and equitable generation systems.
  arXiv  Detail & Related papers  (2025-05-30T06:48:02Z)
• Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval Augmented Generation [108.13261761812517]
  We introduce FRANQ (Faithfulness-based Retrieval Augmented UNcertainty Quantification), a novel method for hallucination detection in RAG outputs.<n>We present a new long-form Question Answering (QA) dataset annotated for both factuality and faithfulness.
  arXiv  Detail & Related papers  (2025-05-27T11:56:59Z)
• UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities [53.76854299076118]
  UniversalRAG is a novel RAG framework designed to retrieve and integrate knowledge from heterogeneous sources with diverse modalities and granularities.<n>We propose a modality-aware routing mechanism that dynamically identifies the most appropriate modality-specific corpus and performs targeted retrieval within it.<n>We validate UniversalRAG on 8 benchmarks spanning multiple modalities, showing its superiority over various modality-specific and unified baselines.
  arXiv  Detail & Related papers  (2025-04-29T13:18:58Z)
• CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation [68.81271028921647]
  We introduce CORAL, a benchmark designed to assess RAG systems in realistic multi-turn conversational settings. CORAL includes diverse information-seeking conversations automatically derived from Wikipedia. It supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling.
  arXiv  Detail & Related papers  (2024-10-30T15:06:32Z)
• Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage [74.70255719194819]
  We introduce a novel framework based on sub-question coverage, which measures how well a RAG system addresses different facets of a question. We use this framework to evaluate three commercial generative answer engines: You.com, Perplexity AI, and Bing Chat. We find that while all answer engines cover core sub-questions more often than background or follow-up ones, they still miss around 50% of core sub-questions.
  arXiv  Detail & Related papers  (2024-10-20T22:59:34Z)
• ELOQ: Resources for Enhancing LLM Detection of Out-of-Scope Questions [52.33835101586687]
  We study out-of-scope questions, where the retrieved document appears semantically similar to the question but lacks the necessary information to answer it.<n>We propose a guided hallucination-based approach ELOQ to automatically generate a diverse set of out-of-scope questions from post-cutoff documents.
  arXiv  Detail & Related papers  (2024-10-18T16:11:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.