negotiation

Negotiation Coach is an AI agent that helps users prepare for salary talks, vendor renewals, and high-stakes deals with researched strategies, role-play simulations, and language coaching.

This paper presents a mechanism-design framework for verifying negotiation and allocation tasks in LLM agents using A2A/MCP protocols. It evaluates rational behavior across models, finding that mechanism-level incentive compatibility does not automatically transfer to LLM agents.

Discusses the 'happy path problem' in agentic commerce and references protocols like x402, ERC 8004, ERC 7710, and A2A for handling payments, identity, permissions, and negotiation.

The paper introduces SocialRL, a reinforcement learning approach to enhance social reasoning in small language models, enabling them to negotiate effectively and match or exceed the performance of larger models like GPT-5 in various interaction domains.

This paper proposes MAS-DecStream, a decentralized scheduling framework for stream processing in mobile edge-cloud infrastructures, extending the Contract Net Protocol with LLM-assisted negotiation. Experiments show reduced latency violations and improved utility over rule-based baselines.

This paper presents ContractSim, a framework and evaluation suite for assessing LLM agents' ability to negotiate and execute natural-language contracts under uncertainty. It finds that current agents negotiate well under low uncertainty but often fail under high uncertainty and frequently violate contract terms for profit.

This paper studies how LLM agents negotiate in a dynamic supply chain bargaining problem, benchmarking nine models from OpenAI, Google, and Alibaba against a Bayesian equilibrium and finding that capability, provider identity, and prompt design shape surplus creation and division.

This paper introduces Isolated Bilateral Reinforcement Learning (IB-RL), a method where two dialogue roles co-evolve through joint rollouts while optimizing their own rewards independently. It addresses the static-counterpart mismatch in RL for strategic dialogue, showing improved generalization to unseen counterparts in Vehicle TeleSales and Deal-or-No-Deal benchmarks.

The paper introduces TSR, a framework that decomposes social dialogue into strategic planning and linguistic execution, and LHRL-VGR, a reinforcement learning algorithm with variance-gated rewards. Fine-tuning a Qwen2.5-7B agent with this approach surpasses the GPT-4o baseline by 7.32% in goal completion on the SOTOPIA benchmark.

Announcement of the GLEE Competition at IAB@NeurIPS 2026, where participants build AI agents for bargaining, negotiation, and persuasion in live multi-turn games, with a $6,000 prize pool and optional paper submission.

Microsoft Research highlights new research on SocialRL for small language model negotiation, PazaBench V2 for African language speech evaluation, EvoLib for agent experience learning, improved A/B testing methods, and AI-driven precision oncology.

This paper introduces DebtBench, the first persona-enriched benchmark for debt collection negotiation, and DebtGPT, a debt collection agent that jointly optimizes financial recovery and interaction experience. Experiments show most LLMs struggle in this realistic scenario, while DebtGPT matches GPT-4o performance.

A new 4B parameter AI model trained with Social Reinforcement Learning (SocialRL) outperforms GPT-5 models in negotiation tasks, suggesting that traits making an AI pleasant assistant (agreeableness, transparency) hinder negotiation effectiveness.

A ransomware negotiator hired to represent victims was secretly working for the BlackCat attackers, sharing confidential client information to maximize ransom payments, leading to a 6-year prison sentence.

This paper formalizes behavioral privacy leakage in multi-round agentic negotiation and proposes an adaptive stochastic policy that provides differential privacy guarantees while maintaining high negotiation utility.

Ivo introduces Benchmarks, a tool that reviews and redlines agreements by leveraging a company's entire history of contract negotiations to improve future negotiations.

A developer built a 6-agent AI system for satellite collision avoidance in 4 days for a hackathon, sharing lessons learned.

Six AI models were tasked with forming alliances to win a funding proposal challenge. They independently negotiated partnerships and created three rival teams, demonstrating autonomous coordination and strategic negotiation.

This paper introduces an automated mediator for human negotiation that uses a structured pipeline of LLM modules to conduct pre-mediation. In human-subject experiments, the system achieves preparation outcomes comparable to professional human mediators while reducing error in preference inference.

An insurance claims adjuster describes how auto lenders are deploying AI bots to dispute total loss vehicle valuations using inaccurate data, wasting adjusters' time and making it difficult to reach human representatives. The post seeks advice on bypassing these AI systems to speak with a live person.