SurveyAgent-HKA: A multi-agent framework for scientific survey generation with LLMs and human knowledge augmentation

• URL: http://arxiv.org/abs/2609.05938v1
• Date: Sat, 05 Sep 2026 07:03:47 GMT
• Title: SurveyAgent-HKA: A multi-agent framework for scientific survey generation with LLMs and human knowledge augmentation
• Abstract summary: We introduce SurveyAgent-HKA, a multi-agent framework that improves end-to-end scientific survey generation.<n>The framework decomposes survey generation into well-defined sub-tasks handled by LLM-powered agent.<n>Our framework is efficient in both time and cost, making it a practical solution for broader AI-assisted scientific writing applications.
• Score: 17.544221843099812
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic scientific survey generation has become an important task in scientific document processing. The common approach of retrieving literature from a single source (e.g., arXiv) and generating surveys through a one-pass large language model (LLM) call often leads to limited reference coverage and, more importantly, fails to replicate the expert-driven revision process that is crucial for writing high-quality surveys. In this paper, we introduce SurveyAgent-HKA, a multi-agent framework that improves end-to-end scientific survey generation by incorporating knowledge derived from published surveys and peer-review comments. The framework decomposes survey generation into well-defined sub-tasks handled by LLM-powered agent. It first retrieves relevant papers from multiple sources and identifies key topics through clustering to construct an initial outline, which is then refined using outlines from related human-written surveys. Based on the refined outline, topic-focused papers are retrieved and re-ranked to select for drafting a well-grounded survey. Then, we identify common issues raised by experts in peer-review comments from published surveys to guide the revisions and finalize the survey. Experiments on two domains show that our approach outperforms mainstream baselines in citation quality, structural consistency, and content quality. Furthermore, our framework is efficient in both time and cost, making it a practical solution for broader AI-assisted scientific writing applications.

Related papers

• Deep Academic Survey: Stateful Agentic Closed-Loop Paradigm for Academic Survey Automation [60.151050016855834]
  DAS is a stateful agentic framework for generating publication-oriented academic surveys.<n>Its key idea is to separate reusable paper analysis from topic-specific manuscript construction.<n>DAS achieves the highest average in all four dimensions, with an overall score of 4.34 compared with 4.03 for the strongest competitor.
  arXiv  Detail & Related papers  (2026-08-18T17:29:00Z)
• TVIR: Building Deep Research Agents Towards Text--Visual Interleaved Report Generation [63.89818340842003]
  TVIR (Text--Visual Interleaved Report Generation) is a benchmark of 100 expert-curated multimodal deep research tasks.<n>TVIR-Agent is a hierarchical multi-agent framework that serves as a strong baseline for constructing outlines and retrieving images.<n>We develop a dual-path evaluation framework that combines Textual Assessment and Visual Assessment.
  arXiv  Detail & Related papers  (2026-06-01T14:35:14Z)
• AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery [55.70879973230979]
  AutoResearchBench is a benchmark for autonomous scientific literature discovery.<n>Compared to previous benchmarks on agentic web browsing, AutoResearchBench is research-oriented.<n>Even the most powerful LLMs, despite having largely conquered general agentic web-browsing benchmarks such as BrowseComp, achieve only 9.39% accuracy on Deep Research and 9.31% IoU on Wide Research.
  arXiv  Detail & Related papers  (2026-04-28T06:05:17Z)
• PaperScope: A Multi-Modal Multi-Document Benchmark for Agentic Deep Research Across Massive Scientific Papers [52.97586643334384]
  We introduce PaperScope, a benchmark for agentic deep research.<n>It is built on a knowledge graph of over 2,000 AI papers spanning three years.<n>It integrates semantically related key information nodes and employs optimized random-walk article selector.
  arXiv  Detail & Related papers  (2026-04-13T11:07:08Z)
• 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)
• Learning to Summarize by Learning to Quiz: Adversarial Agentic Collaboration for Long Document Summarization [86.98098988779809]
  We propose SummQ, a novel adversarial multi-agent framework for long document summarization.<n>Our approach employs summary generators and reviewers that work collaboratively to create and evaluate comprehensive summaries.<n>We evaluate SummQ on three widely used long document summarization benchmarks.
  arXiv  Detail & Related papers  (2025-09-25T08:36:19Z)
• SurveyGen: Quality-Aware Scientific Survey Generation with Large Language Models [14.855783196702191]
  We present SurveyGen, a large-scale dataset comprising over 4,200 human-written surveys across diverse scientific domains.<n>We build QUAL-SG, a novel quality-aware framework for survey generation.
  arXiv  Detail & Related papers  (2025-08-25T04:22:23Z)
• SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generation [37.921524136479825]
  SurGE (Survey Generation Evaluation) is a new benchmark for scientific survey generation in computer science.<n>SurGE consists of (1) a collection of test instances, each including a topic description, an expert-written survey, and its full set of cited references, and (2) a large-scale academic corpus of over one million papers.<n>In addition, we propose an automated evaluation framework that measures the quality of generated surveys across four dimensions.
  arXiv  Detail & Related papers  (2025-08-21T15:45:10Z)
• SciSage: A Multi-Agent Framework for High-Quality Scientific Survey Generation [2.985620880452744]
  SciSage is a multi-agent framework employing a reflect-when-you-write paradigm.<n>It critically evaluates drafts at outline, section, and document levels, collaborating with specialized agents for query interpretation, content retrieval, and refinement.<n>We also release SurveyScope, a benchmark of 46 high-impact papers ( 2020-2025) across 11 computer science domains.
  arXiv  Detail & Related papers  (2025-06-15T02:23:47Z)
• SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing [13.101632066188532]
  We introduce SurveyForge, which generates the outline by analyzing the logical structure of human-written outlines.<n>To achieve a comprehensive evaluation, we construct SurveyBench, which includes 100 human-written survey papers for win-rate comparison.<n>Experiments demonstrate that SurveyForge can outperform previous works such as AutoSurvey.
  arXiv  Detail & Related papers  (2025-03-06T17:15:48Z)
• ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
  ResearchAgent is an AI-based system for ideation and operationalization of novel work.<n>ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.<n>We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)

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.