EvoTac: A Self-Evolving LLM Agent for Eliciting Reusable Tacit Negotiation Heuristics from Terminal Outcomes

We propose EvoTac, an LLM-based framework for real-world negotiation that converts sparse terminal outcomes into reusable tacit experience without fine-tuning the base model. It continuously adapts to changing opponents and scenarios through a simple predict–reflect–update loop, using decoupled layered memory to represent the agent’s constraints, observed opponent behavior patterns, and persistent hypotheses about opponent stance and type. Experiments on a real-world online marketing negotiation task (predicting final commission rates) show that EvoTac outperforms traditional models and multiple LLM baselines in prediction accuracy and first-round offer hit rate.