GIST-CMTF: Goal-State Inference for Causal Minimal Tool Filtering in LLM Agents

The article introduces GIST-CMTF, a goal-state inference layer designed to enhance Causal Minimal Tool Filtering (CMTF) in tool-augmented LLM agents. GIST-CMTF predicts candidate symbolic goals and evaluates ambiguity, achieving a task success rate of 97.0% across various model backends and filtering methods, significantly reducing wrong-goal execution from 19.4% to 2.5%. This advancement emphasizes the importance of validating goal states in addition to tool relevance, which is crucial for improving the reliability of agent interactions in practical applications.