[2603.08388] A Hierarchical Error-Corrective Graph Framework for Autonomous Agents with LLM-Based Action Generation

Computer Science > Artificial Intelligence arXiv:2603.08388 (cs) [Submitted on 9 Mar 2026 (v1), last revised 22 Mar 2026 (this version, v3)] Title:A Hierarchical Error-Corrective Graph Framework for Autonomous Agents with LLM-Based Action Generation Authors:Cong Cao, Jingyao Zhang, Kun Tong View a PDF of the paper titled A Hierarchical Error-Corrective Graph Framework for Autonomous Agents with LLM-Based Action Generation, by Cong Cao and Jingyao Zhang and Kun Tong View PDF HTML (experimental) Abstract:We propose a Hierarchical Error-Corrective Graph FrameworkforAutonomousAgentswithLLM-BasedActionGeneration(HECG),whichincorporates three core innovations: (1) Multi-Dimensional Transferable Strategy (MDTS): by integrating task quality metrics (Q), confidence/cost metrics (C), reward metrics (R), and LLM-based semantic reasoning scores (LLM-Score), MDTS achieves multi-dimensional alignment between quantitative performance and semantic context, enabling more precise selection of high-quality candidate strate gies and effectively reducing the risk of negative transfer. (2) Error Matrix Classification (EMC): unlike simple confusion matrices or overall performance metrics, EMC provides structured attribution of task failures by categorizing errors into ten types, such as Strategy Errors (Strategy Whe) and Script Parsing Errors (Script-Parsing-Error), and decomposing them according to severity, typical actions, error descriptions, and recoverability. This allows precise analysis o...