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TradeWar: A Benchmark for Execution-Grounded Trade Negotiation with Adaptive LLM Agents

Paper recorded by Signals 4 on 2026-10-30 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-30 on arXiv · recorded by Signals 4 on 2026-11-02

Category: cs.AI · 人工智能 · first seen 2026-11-02

Abstract

Multi-turn negotiation remains a challenging problem for large language model (LLM) agents. While LLMs can conduct natural-language dialogue, existing benchmarks predominantly assess only agreement rates, overlooking whether negotiated commitments actually create value once executed. To address this gap, we introduce TradeWar, a multi-country simulation benchmark that translates negotiation dialog

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#54 most recent of 920 cs.AI papers we have recorded · ↑ newer: Know When the Equation Is Wrong: Structural Uncertainty for LLM-Guided · ↓ older: AutoAnalysis: An automated, self-updating meta-analysis of human-AI co
Cite this page: TradeWar: A Benchmark for Execution-Grounded Trade Negotiation with Adaptive LLM Agents: the #54 most recent of 920 cs.AI papers we have recorded (as of 2026-10-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/tradewar-a-benchmark-for-execution-grounded-trade-negotiation-with-adaptive-llm-.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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