#negotiation-ai News & Analysis

5 articles tagged with #negotiation-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBullisharXiv – CS AI · Apr 147/10

🧠

Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewards

Researchers demonstrate that Reinforcement Learning from Verifiable Rewards (RLVR) can train Large Language Models to negotiate effectively in incomplete-information games like price bargaining. A 30B parameter model trained with this method outperforms frontier models 10x its size and develops sophisticated persuasive strategies while generalizing to unseen negotiation scenarios.

AINeutralarXiv – CS AI · Jun 236/10

🧠

Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues

Researchers systematically evaluated Large Language Models' negotiation capabilities across diverse dialogue scenarios, finding that GPT-4 demonstrates superior performance in most tasks while struggling with subjective assessments and strategically optimal responses. This evaluation framework advances understanding of LLM limitations in complex multi-turn interactions requiring theory-of-mind reasoning and strategic communication.

🧠 GPT-4

AINeutralarXiv – CS AI · May 276/10

🧠

EmoDistill: Offline Emotion Skill Distillation for Language Model Agents in Adversarial Negotiation

Researchers introduce EmoDistill, an offline framework that teaches language model agents to strategically use emotion in adversarial negotiations. The system decomposes emotional strategy into emotion selection and expression, with experiments showing that emotionally-framed language significantly shifts negotiation outcomes, suggesting emotion functions as a tactical tool rather than stylistic decoration.

AIBullisharXiv – CS AI · May 276/10

🧠

EvoEmo: Towards Evolved Emotional Policies for Adversarial LLM Agents in Multi-Turn Price Negotiation

Researchers present EvoEmo, an evolutionary reinforcement learning framework that enables LLM agents to develop dynamic emotional strategies in multi-turn price negotiations. The system outperforms baseline approaches by achieving higher success rates and efficiency while improving buyer outcomes, demonstrating that adaptive emotional expression enhances AI negotiation capabilities.

AIBullisharXiv – CS AI · Apr 106/10

🧠

EmoMAS: Emotion-Aware Multi-Agent System for High-Stakes Edge-Deployable Negotiation with Bayesian Orchestration

Researchers introduce EmoMAS, a Bayesian multi-agent framework that enables small language models to perform sophisticated negotiation by treating emotional intelligence as a strategic variable. The system coordinates game-theoretic, reinforcement learning, and psychological agents to optimize negotiation outcomes while maintaining privacy through edge deployment, demonstrating performance comparable to larger models across high-stakes domains.