4 papers
APeB: Benchmarking Personalization Ability of Large Language Model Agents
Garry Yang, Zizhe Chen, Xinru Chen +9
LLM-powered agents struggle with personalization when users issue raw, underspecified queries. In this setting, agents must infer latent intent, extract preferences from noisy inte…
MemRerank: Preference Memory for Personalized Product Reranking
Zhiyuan Peng, Xuyang Wu, Huaixiao Tou +2
LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffe…
EAPO: Enhancing Policy Optimization with On-Demand Expert Assistance
Siyao Song, Cong Ma, Zhihao Cheng +5
Large language models (LLMs) have recently advanced in reasoning when optimized with reinforcement learning (RL) under verifiable rewards. Existing methods primarily rely on outcom…
ShoppingComp: Are LLMs Really Ready for Your Shopping Cart?
Huaixiao Tou, Ying Zeng, Yuemeng Li +6
We present ShoppingComp, a challenging real-world benchmark for comprehensively evaluating LLM-powered shopping agents on three core capabilities: precise product retrieval, expert…