arXiv AI
TradeWar: A Benchmark for Execution-Grounded Trade Negotiation with Adaptive LLM Agents
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 16
TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate
arXiv:2605. 13909v2 Announce Type: replace-cross Abstract: Negotiation is a central mechanism of economic exchange, shaping markets, procurement, labor agreements, and resource allocation.
arXiv AI
Aug 10
TradeVerse: A Longitudinal Benchmark of Political Negotiation in International Trade
arXiv:2608. 06549v1 Announce Type: cross Abstract: LLMs are increasingly being applied to tasks involving institutional and political texts, but existing benchmarks evaluate them on isolated documents or single tasks.
arXiv Machine Learning
Jul 8
Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations
arXiv:2607. 05863v1 Announce Type: new Abstract: Negotiation is a fundamental strategic interaction in management science, characterized by agents attempting to reach agreements while protecting private information, such as reservation costs and hidden valuations.
arXiv Computation and Language
2d ago
Can Large Language Models Extend Their Agentic Capabilities to Goal Pursuit in Open-Ended Multi-Turn Interaction?
The paper investigates whether large language models can extend their agentic capabilities to goal pursuit in open-ended multi-turn dialogue. It introduces Tool-Mediated Interaction (TMI), a training-free protocol that frames dialogue actions as tool calls and responses as tool results, preserving a continuous reasoning–action–observation loop. Across social interaction, emotional support, persuasion, and price negotiation tasks, TMI improves goal attainment, preference, donation amounts, and negotiation margins while reducing reasoning tokens and cross-turn phrase reuse.
arXiv AI
Sep 3
Competitive Market Behavior of LLMs
The study investigates how large language models (LLMs) perform in a double auction market, a common economic mechanism. By replacing human participants with LLM agents, the authors find that markets with LLMs converge more slowly or not at all, leading to less efficient resource allocations. Analysis of trading decisions reveals significant variation across model families and roles, and a lexical study of Chain-of-Thought traces links trade execution to a shift from strategic thinking to urgency.
arXiv AI
Oct 26
Knowing Whom to Trust is not Enough: The Trust--Agreement Gap in Repeated Multi-Party LLM Negotiation
The paper examines how large language model (LLM) agents can use trust estimates in repeated multi‑party, multi‑issue negotiations where one participant covertly tries to block agreement. By introducing a framework where agents maintain pairwise trust scores across successive games, the study compares trust‑aware and trust‑agnostic agents. Results show a trust–agreement gap: while trust‑aware agents quickly spot the adversary and trust can raise agreement rates by up to 23 percentage points, this benefit only materializes when agents are explicitly guided to incorporate trust into their decision‑making.