Challenge: Existing agents struggle due to bounded rationality in human data, low adaptability to counterpart behavior, and limited strategic reasoning.
Approach: They propose a framework for turn-level offer optimization based on two core principles: opponent modeling and Tit-for-Tat reciprocity.
Outcome: The proposed framework outperforms baselines across diverse partner agents and validates through human evaluation.

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A Dual-Mind Framework for Strategic and Expressive Negotiation Agent (2025.acl-long)

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Challenge: Existing approaches to negotiation dialogue focus on only one aspect, ignoring the synergistic effect of their combined synergies.
Approach: They propose a dual-mind negotiation agent framework that integrates an intuitive and a deliberative module for slow, expression optimization.
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INA: An Integrative Approach for Enhancing Negotiation Strategies with Reward-Based Dialogue Agent (2023.findings-emnlp)

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Challenge: a novel negotiation agent is designed for the online marketplace . a dialogue agent can negotiate on price and other factors .
Approach: They propose a novel negotiation agent that is integrative in nature and can negotiate on price and other factors.
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PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues (2026.acl-long)

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Challenge: Emotion plays a pivotal role in shaping negotiation outcomes, influencing trust, cooperation, and long-term relationships.
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ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs (2026.acl-long)

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Challenge: Existing methods lack the capability for continuous learning and self-evolution from interactions, limiting the diversity and adaptability of attack strategies.
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Decoupling Strategy and Generation in Negotiation Dialogues (D18-1)

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Challenge: Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy.
Approach: They propose a modular approach that decouples strategy and generation by coarse dialogue acts . they test their approach on a recently proposed DEALORNODEAL game .
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EmoMAS: Emotion-Aware Multi-Agent System for High-Stakes Edge-Deployable Negotiation with Bayesian Orchestration (2026.acl-long)

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Challenge: Large language models (LLMs) are increasingly used for automated negotiation, but their cloud-centric paradigm exposes sensitive negotiations to privacy and security risks.
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Improving Dialog Systems for Negotiation with Personality Modeling (2021.acl-long)

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Challenge: In this paper, we introduce a framework for generating strategic dialog inspired by the idea of incorporating a theory of mind (ToM) into machines.
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PrefIx: Understand and Adapt to User Preference in Human-Agent Interaction (2026.findings-acl)

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Challenge: Current benchmarks evaluate task accuracy but overlook how agents interact . Preference-aware agents show 7.6% average UX improvement and 18.5% gain in preference alignment.
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Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems (2025.emnlp-main)

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Challenge: Existing platforms lack a mechanism for user actions to dynamically reshape the environment.
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Beyond Task-Oriented and Chitchat Dialogues: Proactive and Transition-Aware Conversational Agents (2025.emnlp-main)

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Challenge: Current efforts to bridge the two modes of interaction are reactive, focusing on responding to user inputs rather than coordinating dialogue flows.
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