Case-Based Calibration of Adaptive Reasoning and Execution for LLM Tool Use
Tool use extends large language models beyond parametric knowledge, but reliable execution requires balancing appropriate reasoning depth with strict structural validity. We approach this problem from a case-based perspective to present CAST, a case-driven framework that treats historical execution trajectories as structured cases. Instead of reusing raw exemplar outputs, CAST extracts case-derived signals to identify complexity profiles for estimating optimal reasoning strategies, alongside failure profiles to map likely structural breakdowns. The framework translates this knowledge into a fine-grained reward design and adaptive reasoning, enabling the model to autonomously internalize c...
| Authors | Renning Pang, Tian Lan, Leyuan Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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