An LLM-Empowered Graph-Structured Collaborative Inference Framework for Logical Reading Comprehension

Abstract

In machine reading comprehension (MRC) tasks that involve complex logical reasoning, graph structures are often regarded as a more suitable representation paradigm than plain text sequences, owing to their explicit structured semantic environment and strong relational inference capabilities. Nevertheless, most existing graph-based MRC models commonly suffer from incomplete node representations and sparse connectivity during graph construction. Moreover, these models typically devote most of their capacity to refining node features while comparatively overlooking the rich relational information encoded in edges. Such limitations significantly curb the potential of graph structures to support deep relational understanding and complex reasoning tasks. In light of this, this study proposes a Graph-Structured Collaborative Inference (GSCI) framework for the task of logical reading comprehension, which consists of three key modules: LLM-empowered logic-oriented graph construction, dual-stream collaborative graph modeling, and structure-aware answer prediction. Firstly, GSCI leverages an LLM to perform fine-grained semantic parsing, extracting abundant structured knowledge that constructs logic-oriented graph foundation for downstream inference. Secondly, a node–edge cooperative modeling mechanism is designed to jointly capture semantic information from nodes and relational signals from edges. Thirdly, the learned structured representations are integrated with textual representations to support candidate answer prediction. Finally, comprehensive experiments on two challenging logical reasoning benchmarks (ReClor and LogiQA) demonstrate that GSCI consistently outperforms existing baselines, providing strong empirical evidence of its effectiveness in logical reading comprehension tasks.