mlsanityData to 14 Oct 2026

Receiver Decides Agent-to-Agent Negotiations

arXiv 2610.18424 ↗First posted 13 Oct 2026cs.CLcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 17 of its 39 references that are in the map. First posted on arXiv 13 Oct 2026.

Builds on

17 of 39 references.

Closest ancestors · 1

Same direction, newest first

  1. 2026TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate13 May

Other references · 16

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  2. 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!cited by 1,154
  3. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  4. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 1,032
  5. 2016Sequence-Level Knowledge Distillationcited by 801
  6. 2024How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMscited by 573
  7. 2024Safety Alignment Should Be Made More Than Just a Few Tokens Deepcited by 472
  8. 2017Deal or No Deal? End-to-End Learning for Negotiation Dialoguescited by 209
  9. 2019Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Goodcited by 209
  10. 2023Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedbackcited by 207
  11. 2018Decoupling Strategy and Generation in Negotiation Dialoguescited by 162
  12. 2023Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiationcited by 96
4 more
  1. 2024Evaluating Language Model Agency through Negotiationscited by 48
  2. 2024GLEE: A Unified Framework and Benchmark for Language-based Economic Environmentscited by 20
  3. 2021RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversationscited by 19
  4. 2026AREG: Adversarial Resource Extraction Game for Evaluating Persuasion and Resistance in Large Language Modelscited by 1

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  1. 2026Counterparty Modeling is Not Strategy: The Limits of LLM Negotiators
  2. 2024Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method
  3. 2026Do Small Language Models Learn to Negotiate? A Controlled Scaling Study of RL-Trained Sellers
  4. 2026Propose, Don't Judge: An Anytime-Valid Referee for LLM Agents That Mine Investment Factors
  5. 2026Used Car Salesbots? Honesty and Credulity of LLMs as Bargaining Agents under Partial Information
  6. 2026Distilling Bayesian Belief States into Language Models for Auditable Negotiation
  7. 2026Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling
  8. 2026Choose Your Agent: Tradeoffs in Adopting AI Advisors, Coaches, and Delegates in Multi-Party Negotiation