PrefBench: Evaluating Zero-Shot LLM Agents in Hidden-Preference Personalized Pricing Negotiations
arXiv:2605.22855v1 Announce Type: cross 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. This paper presents PrefBench, a simulator-based benchmark for hidden-preference personalized pricing negotiations. Each episode pairs a simulated buyer with a fixed vehicle-customization bundle; the seller observes public persona descriptors, bun
The proliferation of advanced LLM agents necessitates robust evaluation frameworks to understand their practical limitations in complex, real-world simulations like personalized negotiations.
Sophisticated LLM agents will increasingly handle negotiation and sales functions, making their performance in hidden-preference scenarios critical for commercial and economic outcomes.
The introduction of PrefBench provides a standardized benchmark to rigorously test and improve LLM agents' capabilities in personalized pricing, moving beyond simple task completion to actual profitable decision-making.
- · AI Agent Developers
- · E-commerce Platforms
- · Sales and Marketing Automation
- · Consumers seeking optimized deals
- · Inefficient Sales Systems
- · Businesses relying on traditional pricing models
LLM agents will be developed to be more shrewd negotiators, capable of inferring hidden preferences and adapting strategies.
The competitive landscape for personalized pricing will intensify, as businesses adopt more intelligent automated negotiation systems.
This could lead to a shift in consumer behavior, as individuals interact more frequently with sophisticated AI-driven sales and negotiation interfaces.
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Read at arXiv cs.LG