Latent Preference Modeling for Cross-Session Personalized Tool Calling
Summary
Introduces MPT benchmark and PRefine method for cross-session personalized tool calling that captures user choice reasoning with minimal token overhead.
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Paper page - Latent Preference Modeling for Cross-Session Personalized Tool Calling
Source: https://huggingface.co/papers/2604.17886
Abstract
Personalized tool calling in LLM-based agents is improved through memory-augmented methods that capture user choice reasoning rather than just choices, using minimal token overhead.
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 fortool-augmented agents, asAPI executiontypically requires complete arguments, highlighting the need forpersonalized tool calling. To study this problem, we introduce MPT, a benchmark comprising 265multi-session dialoguesthat cover three challenges:Preference Recall,Preference Induction, andPreference Transfer. We also proposePRefine, a test-time memory-augmented method that representsuser preferencesas evolving hypotheses. Through agenerate--verify--refine loop, it extracts reusable constraints from history and improves tool-calling accuracy while using only 1.24% of the tokens required by full-history prompting. These results indicate that robust personalization in agentic systems depends on memory that captures the reasons behind user choices, not just the choices themselves.
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