DIVERGE: Diversity-Enhanced RAG for Open-Ended Information Seeking

• URL: http://arxiv.org/abs/2602.00238v1
• Date: Fri, 30 Jan 2026 19:03:11 GMT
• Title: DIVERGE: Diversity-Enhanced RAG for Open-Ended Information Seeking
• Authors: Tianyi Hu, Niket Tandon, Akhil Arora,
• Abstract summary: We propose a plug-and-play agentic RAG framework with novel reflection-guided generation and memory-augmented iterative refinement.<n>We demonstrate that DIVERGE achieves the best diversity-quality trade-off compared to competitive baselines.
• Score: 10.970797088560323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing retrieval-augmented generation (RAG) systems are primarily designed under the assumption that each query has a single correct answer. This overlooks common information-seeking scenarios with multiple plausible answers, where diversity is essential to avoid collapsing to a single dominant response, thereby constraining creativity and compromising fair and inclusive information access. Our analysis reveals a commonly overlooked limitation of standard RAG systems: they underutilize retrieved context diversity, such that increasing retrieval diversity alone does not yield diverse generations. To address this limitation, we propose DIVERGE, a plug-and-play agentic RAG framework with novel reflection-guided generation and memory-augmented iterative refinement, which promotes diverse viewpoints while preserving answer quality. We introduce novel metrics tailored to evaluating the diversity-quality trade-off in open-ended questions, and show that they correlate well with human judgments. We demonstrate that DIVERGE achieves the best diversity-quality trade-off compared to competitive baselines and previous state-of-the-art methods on the real-world Infinity-Chat dataset, substantially improving diversity while maintaining quality. More broadly, our results reveal a systematic limitation of current LLM-based systems for open-ended information-seeking and show that explicitly modeling diversity can mitigate it. Our code is available at: https://github.com/au-clan/Diverge

Related papers

• 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)
• Jointly Reinforcing Diversity and Quality in Language Model Generations [64.72289248044514]
  Post-training of Large Language Models (LMs) often prioritizes accuracy and helpfulness at the expense of diversity.<n>We address this challenge with Diversity-Aware Reinforcement Learning (DARLING), a framework that jointly optimize for response quality and semantic diversity.
  arXiv  Detail & Related papers  (2025-09-02T17:38:47Z)
• 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)
• 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)
• Diversity Enhances an LLM's Performance in RAG and Long-context Task [13.165004097488655]
  A common approach involves selecting content with the highest similarity to the query.<n>This often leads to redundancy and the exclusion of diverse yet relevant information.<n>Our findings reveal that incorporating diversity substantially increases the recall of selecting relevant sentences or chunks.
  arXiv  Detail & Related papers  (2025-02-13T07:11:01Z)
• CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity [23.48167670445722]
  Retrieval-Augmented Generation (RAG) aims to generate more accurate and reliable answers with the help of the retrieved context from external knowledge sources. evaluating these systems remains a crucial research area due to the following issues. We propose a Comprehensive Full-chain Evaluation (CoFE-RAG) framework to facilitate thorough evaluation across the entire RAG pipeline.
  arXiv  Detail & Related papers  (2024-10-16T05:20:32Z)
• Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study [45.69867169347836]
  Retrieval-augmented generation (RAG) is an effective approach to mitigating the hallucination of large language models (LLMs) through the integration of external knowledge.<n>In this paper, we standardize a benchmark dataset that combines structured and unstructured knowledge across diverse domains.<n>We also develop a plug-and-play RAG framework, textbfPruningRAG, whose main characteristic is the use of multi-granularity pruning strategies.
  arXiv  Detail & Related papers  (2024-09-03T03:31:37Z)
• Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback [41.88662700261036]
  RAG systems face limitations when semantic relevance alone does not guarantee improved generation quality. We propose Pistis-RAG, a new RAG framework designed with a content-centric approach to better align LLMs with human preferences.
  arXiv  Detail & Related papers  (2024-06-21T08:52:11Z)
• CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models [49.16989035566899]
  Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios.
  arXiv  Detail & Related papers  (2024-01-30T14:25:32Z)
• Diversify Question Generation with Retrieval-Augmented Style Transfer [68.00794669873196]
  We propose RAST, a framework for Retrieval-Augmented Style Transfer. The objective is to utilize the style of diverse templates for question generation. We develop a novel Reinforcement Learning (RL) based approach that maximizes a weighted combination of diversity reward and consistency reward.
  arXiv  Detail & Related papers  (2023-10-23T02:27:31Z)

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.