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To address traditional Chinese Grammatical Error Correction (CGEC) limitations—weak semantic understanding, inflexible error classification, unstable revision—this paper proposes the Ant Colony-Inspired Multi-Agent CGEC Method (ACMA-CGEC), supported by Large Language Models (LLMs). ACMA-CGEC adopts an ant colony-inspired three-tier architecture (global control, information memory, task execution): Queen Ant Agent (global control core) formulates a Disassembling-Analyzing-Processing task chain and enables max 3-iteration backtracking for adaptive scheduling; Global Process Memory Pool (GPMP, sole memory carrier) unifies structured data storage to ensure multi-agent data consistency. In task execution: Disassembling Ant Agent provides standardized linguistic units via semantic-aware segmentation and part-of-speech tagging; Analyzing Ant Agent realizes decoupled syntactic-semantic detection for precise error recognition; Processing Ant Agent uses dual-model collaboration for optimal revision (adhering to minimal modification and original meaning preservation). This end-to-end CGEC method innovates in collaborative architecture, decoupled detection, and adaptive revision, demonstrating advantages in detection accuracy and revision flexibility, and offering a modular, adaptive paradigm for CGEC research.

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A Novel Ant Colony-Inspired Multi-agent Collaborative Method for Chinese Grammatical Error Correction

  • 会议论文

  • 发表日期：

    2026年

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  • Chengrui Qi,
  • Xiaoqiang Wang,
  • Nier Wu

摘要

To address traditional Chinese Grammatical Error Correction (CGEC) limitations—weak semantic understanding, inflexible error classification, unstable revision—this paper proposes the Ant Colony-Inspired Multi-Agent CGEC Method (ACMA-CGEC), supported by Large Language Models (LLMs). ACMA-CGEC adopts an ant colony-inspired three-tier architecture (global control, information memory, task execution): Queen Ant Agent (global control core) formulates a Disassembling-Analyzing-Processing task chain and enables max 3-iteration backtracking for adaptive scheduling; Global Process Memory Pool (GPMP, sole memory carrier) unifies structured data storage to ensure multi-agent data consistency. In task execution: Disassembling Ant Agent provides standardized linguistic units via semantic-aware segmentation and part-of-speech tagging; Analyzing Ant Agent realizes decoupled syntactic-semantic detection for precise error recognition; Processing Ant Agent uses dual-model collaboration for optimal revision (adhering to minimal modification and original meaning preservation). This end-to-end CGEC method innovates in collaborative architecture, decoupled detection, and adaptive revision, demonstrating advantages in detection accuracy and revision flexibility, and offering a modular, adaptive paradigm for CGEC research.