{"ID":2873280,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06341","arxiv_id":"2509.06341","title":"Evaluating Multi-Turn Bargain Skills in LLM-Based Seller Agent","abstract":"In online second-hand marketplaces, multi-turn bargaining is a crucial part of seller-buyer interactions. Large Language Models (LLMs) can act as seller agents, negotiating with buyers on behalf of sellers under given business constraints. A critical ability for such agents is to track and accurately interpret cumulative buyer intents across long negotiations, which directly impacts bargaining effectiveness. We introduce a multi-turn evaluation framework for measuring the bargaining ability of seller agents in e-commerce dialogues. The framework tests whether an agent can extract and track buyer intents. Our contributions are: (1) a large-scale e-commerce bargaining benchmark spanning 622 categories, 9,892 products, and 3,014 tasks; (2) a turn-level evaluation framework grounded in Theory of Mind (ToM) with annotated buyer intents, moving beyond outcome-only metrics; and (3) an automated pipeline that extracts reliable intent from massive dialogue data.","short_abstract":"In online second-hand marketplaces, multi-turn bargaining is a crucial part of seller-buyer interactions. Large Language Models (LLMs) can act as seller agents, negotiating with buyers on behalf of sellers under given business constraints. A critical ability for such agents is to track and accurately interpret cumulati...","url_abs":"https://arxiv.org/abs/2509.06341","url_pdf":"https://arxiv.org/pdf/2509.06341v1","authors":"[\"Issue Yishu Wang\",\"Kakam Chong\",\"Xiaofeng Wang\",\"Xu Yan\",\"DeXin Kong\",\"Chen Ju\",\"Ming Chen\",\"Shuai Xiao\",\"Shuguang Han\",\"jufeng chen\"]","published":"2025-09-08T05:12:03Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}