# ASTRA: A Negotiation Agent with Adaptive and Strategic Reasoning via Tool-integrated Action for Dynamic Offer Optimization

arXiv: https://arxiv.org/abs/2503.07129 · HTML: https://mlsanity.com/paper/2503.07129/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 40 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 20 of 20)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://mlsanity.com/paper/1706.05125/) (2017)
- [Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback](https://mlsanity.com/paper/2305.10142/) (2023)
- [Decoupling Strategy and Generation in Negotiation Dialogues](https://mlsanity.com/paper/1808.09637/) (2018)
- [Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration](https://mlsanity.com/paper/2305.13626/) (2023)
- [How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis](https://mlsanity.com/paper/2402.05863/) (2024)
- [Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation](https://mlsanity.com/paper/2309.17234/) (2023)
- [Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena](https://mlsanity.com/paper/2310.05746/) (2023)
- [CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems](https://mlsanity.com/paper/2103.15721/) (2021)
- [Game-theoretic LLM: Agent Workflow for Negotiation Games](https://mlsanity.com/paper/2411.05990/) (2024)
- [Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method](https://mlsanity.com/paper/2402.15813/) (2024)
- [Improving Dialog Systems for Negotiation with Personality Modeling](https://mlsanity.com/paper/2010.09954/) (2020)
- [Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues](https://mlsanity.com/paper/2402.13550/) (2024)
- [Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness](https://mlsanity.com/paper/2004.09731/) (2020)
- [Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning](https://mlsanity.com/paper/2310.04474/) (2023)
- [Negotiating with LLMS: Prompt Hacks, Skill Gaps, and Reasoning Deficits](https://mlsanity.com/paper/2312.03720/) (2023)
- [Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions](https://mlsanity.com/paper/2310.14404/) (2023)
- [Opponent Modeling in Negotiation Dialogues by Related Data Adaptation](https://mlsanity.com/paper/2205.00344/) (2022)
- [ACE: A LLM-based Negotiation Coaching System](https://mlsanity.com/paper/2410.01555/) (2024)

## Built on by (most cited first, 4 of 4)

- [Multi-User Large Language Model Agents](https://mlsanity.com/paper/2604.08567/) (2026)
- [Why Do LLMs Struggle in Strategic Play? Broken Links Between Observations, Beliefs, and Actions](https://mlsanity.com/paper/2605.00226/) (2026)
- [MERIT Feedback Elicits Better Bargaining in LLM Negotiators](https://mlsanity.com/paper/2602.10467/) (2026)
- [DPBench: Structural Determinants of Multi-Agent LLM Coordination Under Simultaneous Resource Contention](https://mlsanity.com/paper/2602.13255/) (2026)

## Abstract (arXiv)

Negotiation requires dynamically balancing self-interest and cooperation within the flow of conversation to maximize one's own utility. Yet, existing agents struggle due to bounded rationality in human data, low adaptability to counterpart behavior, and limited strategic reasoning. To address this, we introduce principle-driven negotiation agents, powered by ASTRA, a novel framework for turn-level offer optimization grounded in two core principles: opponent modeling and Tit-for-Tat reciprocity. ASTRA operates in three stages: (1) interpreting counterpart behavior, (2) optimizing counteroffers via a tool-integrated action with a linear programming (LP) solver, and (3) selecting offers based on strategy assessment and the partner's acceptance probability. Through simulations and human evaluations, our agent effectively adapts to an opponent's shifting stance and achieves favorable outcomes through enhanced adaptability and strategic reasoning. Beyond enhancing negotiation performance, it also serves as a powerful coaching tool, offering interpretable strategic feedback and optimal offer recommendations beyond human bounded rationality, with its potential further validated through human evaluation.