Receiver Decides Agent-to-Agent Negotiations
cs.CL
Submitted: 2026-10-13
Updated: 2026-10-13
Comments: 18 pages, 12 figures
Code: https://github.com/openai/evals
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: When one language-model agent asks another for money, which of the two decides how much changes hands? We ran a round-robin tournament of 1,049 negotiations among fifteen agents.
Abstract
When one language-model agent asks another for money, which of the two decides how much changes hands? We ran a round-robin tournament of 1,049 negotiations among fifteen agents. In each game a requester tries to raise money from a holder that has been told to keep a 100 endowment. Knowing which agent holds the money explains 47% of the variance in the amount transferred; knowing which agent asks explains 2%. Dropping the most extreme agents narrows the split to 22% against 4%. Changing the requester's appeal from one named person to a group changed what requesters wrote, but the amounts transferred under the two framings are statistically equivalent. The holders that lose least are the ones that turn down the opening request, and their turn-1 refusal rate ranks them by total losses (Spearman correlation = -0.73). Training moves this behaviour in both directions. Fine-tuning Qwen3-8B on a persuasion corpus with almost no refusals raised what it gave away as a holder from 12% to 84% of the endowment. Distilling two never-losing holders into a 4B student cut its losses from 95% to 1%. Evaluations of agents that hold resources should test them in the holder role, including after persuasion fine-tuning.
Sources
- Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
- AREG: Adversarial Resource Extraction Game for Evaluating Persuasion and Resistance in Large Language Models
- GLEE: A Unified Framework and Benchmark for Language-based Economic Environments
- TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate
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