Users often omit essential details in their requests to LLM-based agents, resulting in under-specified inputs for tool use.
This poses a fundamental challenge for tool-augmented agents, as API execution typically requires complete arguments, highlighting the need for personalized tool calling.
To Study This Problem
We present Multi-Session Personalized Tool Calling (MPT), a benchmark comprising 4,695 instances over 459 multi-session interaction histories that cover three challenges: Preference Recall, Induction, and Transfer.
PRefine Methodology
We further propose PRefine, a test-time memory method that maintains the user's latent preference as a textual hypothesis revised through a generate--verify--refine loop.
Across five LLMs, existing memory systems often fail to improve on full-history prompting; PRefine outperforms all baselines and alone surpasses it on Preference Transfer.
These results indicate that memory for personalized agents must abstract behavior into preferences, rather than simply archive it.