Select, Read, and Write: A Multi-Agent Framework of Full-Text-based Related Work Generation (2025)

Abstract: Automatic related work generation (RWG) can save people’s time and effort when writing a draft of related work section (RWS) for further revision. However, existing methods for RWG always suffer from shallow comprehension due to taking the limited portions of references papers as input and isolated explanation for each reference due to ineffective capturing the relationships among them. To address these issues, we focus on full-text-based RWG task and propose a novel multi-agent framework. Our framework consists of three agents: a selector that decides which section of the papers is going to read next, a reader that digests the selected section and updates a shared working memory, and a writer that generates RWS based on the final curated memory. To better capture the relationships among references, we also propose two graph-aware strategies for selector, enabling to optimize the reading order with constrains of the graph structure. Extensive experiments demonstrate that our framework consistently improves performance across three base models and various input configurations. The graph-aware selectors outperform alternative selectors, achieving state-of-the-art results. The code and data are available at https://github.com/1190200817/Full_Text_RWG.

## introduction with the exponential growth of academic publications @xcite , automatic related work generation (rwg) becomes more and more attractive to research communities because it can save time and effort in preparing the first draft of the related work section (rws) @xcite . although the rwg task has a long history @xcite and the advancement of llms significantly improves * corresponding authors: ruihua song and xiting wang. the general ability of text understanding and generation, writing a good rws is not trivial. even for experienced researchers, they have to spend a bunch of time to draft the rws after intensive reading of all references. they need to deeply comprehend the similarities and differences between references, organize them in a reasonable taxonomy, and position the current work by pointing out its novelty. however, existing methods are far from being as excellent as experienced researchers in writing rws. there are at least two main challenges: 1) misinterpretations or hallucinations due to using limited portions of references (c1) and 2) isolated explanation for each reference due to ineffective exploitation of the relationships (c2). ## problem formulation before introducing our framework, we first define the problem formulation and notations used throughout this paper. the input to the task consists of two main components: the citing paper c, which represents the paper being written, and a set of reference papers @xmath0, r 2 , . . . , r n }, where r i denotes a single reference paper and n is the total number of references. for simplicity, c is also denoted as r 0 . following prior rwg tasks, r is assumed to be given and corresponds to the references cited by the ground-truth related work section. given the above inputs {r 0 } ∪ r, the goal of the task is to generate a related work section (rws) for c that incorporates all references in r while maintaining coherence with the context of c. since we focus on full-text-based rwg task, each reference paper r i contains its entire content, represented as r @xmath1,1 , s i,2 , . . . , s i,l i }, where s i,j denotes the j-th section of r i , and l i is the total number of sections in r i . similarly, the input also includes all sections of the citing paper r 0 = {s 0,1 , s 0,2 , . . . , s 0,l 0 }, except for the related work section, which is to be generated. ## conclusion in this paper, we propose a multi-agent framework along with a graph-aware selector within the framework for full-text-based related work generation (rwg) tasks. the framework consists of three agents: a selector, a reader, and a writer, which work collaboratively to read the papers in selected order and finally generate the related work section (rws). our framework enables to optimize both the reading order and memory update. our graph-aware selector can operate under the constraints of the graph to better capture the relationships among references. extensive experiments demonstrate that our framework consistently improves the performance of different base models across various input configurations and the graphaware selector based on the citation graph achieves the best performance. case study reveals that our framework generates more logically coherent and tightly connected rws.