JTPRO: A Joint Tool-Prompt Reflective Optimization Framework for Language Agents
Large language model (LLM) agents augmented with external tools often struggle as number of tools grow large and become domain-specific. In such settings, ambiguous tool descriptions and under-specified agent instructions frequently lead to tool mis-selection and incorrect slot/value instantiation. We hypothesize that this is due to two root causes: generic, one-size-fits-all prompts that ignore tool-specific nuances, and underspecified tool schemas that lack clear guidance on when and how to use each tool and how to format its parameters. We introduce Joint Tool-Prompt Reflective Optimization (JTPRO), a framework for improving tool-calling reliability in trace-supervised settings by iter...
| Authors | Sandip Ghoshal, Anshul Mittal, Jyotika Singh et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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