PrefBench: Evaluating Zero-Shot LLM Agents in Hidden-Preference Personalized Pricing Negotiations
The paper presents PrefBench, a benchmark for evaluating zero-shot LLM agents in personalized pricing negotiations with hidden buyer preferences. It highlights the challenges faced by sellers in achieving profitable outcomes despite high deal rates. The findings indicate that while LLMs can comply with structured protocols, their profit performance remains subpar compared to simpler heuristics.
- ▪PrefBench is a simulator-based benchmark designed for hidden-preference personalized pricing negotiations.
- ▪The study evaluates zero-shot LLM sellers against heuristic references over 7,500 episodes.
- ▪Despite achieving deal rates above 0.99, the LLMs' average profit is only slightly above random baseline levels.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.22855 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Computer Science and Game Theory arXiv:2605.22855 (cs) [Submitted on 19 May 2026] Title:PrefBench: Evaluating Zero-Shot LLM Agents in Hidden-Preference Personalized Pricing Negotiations Authors:Yingjie Lei View a PDF of the paper titled PrefBench: Evaluating Zero-Shot LLM Agents in Hidden-Preference Personalized Pricing Negotiations, by Yingjie Lei View PDF Abstract:Personalized pricing negotiations are a challenging testbed for LLM agents because successful interaction does not guarantee profitable decision making. A seller may produce valid actions and close many deals while still pricing poorly when buyer willingness to pay and bargaining traits remain hidden.
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