HECG combines multi-dimensional metrics for strategy choice, ten-type error classification with recoverability details, and causal-context graphs to improve LLM agent reliability in complex tasks.
Technical Report CVC TR-98-003/DCS TR- 1165, Yale Center for Computational Vision and Control (1998)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Hierarchical Error-Corrective Graph Framework for Autonomous Agents with LLM-Based Action Generation
HECG combines multi-dimensional metrics for strategy choice, ten-type error classification with recoverability details, and causal-context graphs to improve LLM agent reliability in complex tasks.