arXiv:2607.03162v1 Announce Type: new Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries. In this setting, agents must infer latent intent, extract preferences from noisy interaction histories, and select among competing alternatives. Existing benchmarks rarely test this capability, as they often rely on user-refined queries or simplified histories. We introduce personalized product search (PPS), a testbed for agentic personalization under raw queries and diverse histories. We construct Agent Personalized Benchmark (APeB) from action logs, p
The proliferation of Large Language Models (LLMs) and the increasing focus on autonomous agents necessitate better methods for evaluating their practical capabilities, particularly in complex, real-world user interactions.
This benchmark addresses a critical gap in LLM agent development by focusing on personalization from 'raw' user queries, which is essential for general-purpose agent adoption and effectiveness, moving beyond simplified test cases.
The introduction of APeB provides a standardized and more realistic testing ground for LLM agents, shifting evaluation from simplified, refined inputs to complex, noisy, and underspecified user interactions, pushing for more robust agent design.
- · AI Agent developers
- · Personalized e-commerce platforms
- · Consumer tech companies
- · AI benchmark developers
- · LLM agents unable to handle personalization
- · Companies relying on simplified agent metrics
- · Platforms with poor implicit user data integration
Improved personalization capabilities in LLM-powered applications.
Accelerated development of more sophisticated, human-like AI agents capable of understanding nuances and preferences without explicit instruction.
Enhanced user experience across various digital platforms, potentially leading to greater user reliance on AI agents for complex tasks.
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Read at arXiv cs.AI