STRUCTSURVEY: Structured Agentic Retrieval for Automated Survey Paper Generation

• URL: http://arxiv.org/abs/2607.01243v1
• Date: Tue, 12 May 2026 22:20:42 GMT
• Title: STRUCTSURVEY: Structured Agentic Retrieval for Automated Survey Paper Generation
• Abstract summary: STRUCTSURVEY is a hierarchical multi-agent framework that shifts structural reasoning from generation to retrieval.<n>It improves ROUGE-1 recall by +2.9 and ROUGE-2 recall by +1.0 on average, without reducing precision.<n>It also improves LLM-as-a-Judge ratings for logical structure, depth, and synthesis, showing that explicit structural retrieval yields surveys closer to human-written organization and reasoning.
• Score: 0.5566372336864309
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The rapid growth of scientific publications makes it increasingly difficult to track and synthesize research progress. While Large Language Models (LLMs) can support automated survey generation, existing methods retrieve unstructured data and require models to infer conceptual, methodological, and taxonomic relations from raw text at generation time. We introduce STRUCTSURVEY, a hierarchical multi-agent framework that shifts structural reasoning from generation to retrieval by dynamically constructing graph-based representations of entities, relations, and topical taxonomies. We evaluate STRUCTSURVEY on a new reference-grounded benchmark of ACL survey papers for reproducible long-form scientific summarization. Compared with embedding-only retrieval baselines, STRUCTSURVEY improves ROUGE-1 recall by +2.9 and ROUGE-2 recall by +1.0 on average, without reducing precision. It also improves LLM-as-a-Judge ratings for logical structure, depth, and synthesis, showing that explicit structural retrieval yields surveys closer to human-written organization and reasoning.

Related papers

• Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage [89.58253972744531]
  Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks like report generation.<n>We investigate whether upstream retrieval metrics can serve as reliable early indicators of the final generated response's information coverage.<n>Our findings demonstrate strong correlations between coverage-based retrieval metrics and nugget coverage in generated responses at both topic and system levels.
  arXiv  Detail & Related papers  (2026-03-09T18:20:20Z)
• From Flat to Structural: Enhancing Automated Short Answer Grading with GraphRAG [8.449978933501965]
  We introduce a Graph Retrieval-Augmented Generation (GraphRAG) framework that organizes reference materials into a structured knowledge graph.<n>Our methodology employs a dual-phase pipeline: utilizing Microsoft GraphRAG for high-fidelity graph construction and the HippoRAG neurosymbolic algorithm.
  arXiv  Detail & Related papers  (2026-02-28T04:44:06Z)
• RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning [69.87510139069218]
  Retrieval-Augmented Generation (RAG) integrates non-parametric knowledge into Large Language Models (LLMs)<n>Recent progress has advanced text-based RAG to multi-turn reasoning through Reinforcement Learning (RL)<n>We introduce model, an RL-based framework that enables LLMs to perform multi-turn and adaptive graph-text hybrid RAG.
  arXiv  Detail & Related papers  (2025-12-10T10:05:31Z)
• MARAG-R1: Beyond Single Retriever via Reinforcement-Learned Multi-Tool Agentic Retrieval [50.30107119622642]
  Large Language Models (LLMs) excel at reasoning and generation but are inherently limited by static pretraining data.<n>Retrieval-Augmented Generation (RAG) addresses this issue by grounding LLMs in external knowledge.<n>MarAG-R1 is a reinforcement-learned multi-tool RAG framework that enables LLMs to dynamically coordinate multiple retrieval mechanisms.
  arXiv  Detail & Related papers  (2025-10-31T15:51:39Z)
• AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys [10.50820843303237]
  This paper introduces autosurvey2, a multi-stage pipeline that automates survey generation through retrieval-augmented synthesis and structured evaluation.<n>The system integrates parallel section generation, iterative refinement, and real-time retrieval of recent publications to ensure both topical completeness and factual accuracy.<n> Experimental results demonstrate that autosurvey2 consistently outperforms existing retrieval-based and automated baselines.
  arXiv  Detail & Related papers  (2025-10-29T22:57:03Z)
• Structure-R1: Dynamically Leveraging Structural Knowledge in LLM Reasoning through Reinforcement Learning [29.722512436773638]
  We propose textscStructure-R1, a framework that transforms retrieved content into structured representations optimized for reasoning.<n>We show that textscStructure-R1 consistently achieves competitive performance with a 7B-scale backbone model.<n>Our theoretical analysis demonstrates how structured representations enhance reasoning by improving information density and contextual clarity.
  arXiv  Detail & Related papers  (2025-10-16T23:19:28Z)
• Unifying Tree Search Algorithm and Reward Design for LLM Reasoning: A Survey [92.71325249013535]
  Deliberative tree search is a cornerstone of Large Language Model (LLM) research.<n>This paper introduces a unified framework that deconstructs search algorithms into three core components.
  arXiv  Detail & Related papers  (2025-10-11T03:29:18Z)
• SurveyG: A Multi-Agent LLM Framework with Hierarchical Citation Graph for Automated Survey Generation [4.512335376984058]
  Large language models (LLMs) are increasingly adopted for automating survey paper generation.<n>We propose textbfSurveyG, an LLM-based agent framework that integrates textithierarchical citation graph<n>The graph is organized into three layers: textbfFoundation, textbfDevelopment, and textbfFrontier, to capture the evolution of research from seminal works to incremental advances and emerging directions.
  arXiv  Detail & Related papers  (2025-10-09T03:14:20Z)
• Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers [0.0]
  Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to enhance large language models.<n>RAG introduces new challenges in retrieval quality, grounding fidelity, pipeline efficiency, and robustness against noisy or adversarial inputs.<n>This survey aims to consolidate current knowledge in RAG research and serve as a foundation for the next generation of retrieval-augmented language modeling systems.
  arXiv  Detail & Related papers  (2025-05-28T22:57:04Z)
• Chain-of-Retrieval Augmented Generation [91.02950964802454]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer.<n>Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)

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.