{"slug": "when-llm-agents-negotiate-private-information-and-dynamic-bargaining-in-supply", "title": "When LLM Agents Negotiate: Private Information and Dynamic Bargaining in Supply Chains", "summary": "A new arXiv study (2608.07538v1) benchmarked nine LLMs from OpenAI, Google, and Alibaba in 9,840 LLM-to-LLM supply chain negotiations, finding that agents agree in 98.9% of cases and capture 95.4% of first-best surplus undiscounted, but average 2.98 rounds versus the benchmark's 1.25, eroding 21-34% of surplus. The study also found that baseline models accept individually irrational contracts in 19.2% of cases versus 0.0-0.6% for mid-tier and flagship models, and that provider identity and prompt design significantly influence surplus division.", "body_md": "arXiv:2608.07538v1 Announce Type: new\nAbstract: As LLM agents move from decision support to autonomous procurement, firms need to know whether delegated negotiators create value, divide it predictably, and avoid money-losing contracts. We study this in a canonical supply chain bargaining problem: a buyer with private demand information negotiates a quantity-payment contract with an uninformed seller. We benchmark nine LLMs from OpenAI, Google, and Alibaba against a validated Perfect Bayesian Equilibrium across 9,840 LLM-to-LLM negotiations. First, capability governs value creation. Agents agree in 98.9% of negotiations and capture 95.4% of first-best surplus undiscounted, but average 2.98 rounds against the benchmark's 1.25, and this delay erodes 21-34% of surplus. Capability also governs reliability: baseline models accept individually irrational contracts in 19.2% of cases, versus 0.0-0.6% at mid-tier and flagship, making automated profit verification the binding guardrail below that threshold. Second, surplus capture is relational. Provider identity predicts who captures surplus better than capability rank: self-play buyer shares average 40% for OpenAI, 50% for Google, and 70% for Alibaba's Qwen, an ordering that survives restricted communication and no discounting. Reversing which provider sells moves the division by 7-18 percentage points, and the capable Qwen flagship is the weakest cross-family seller: vendor choice is a first-order distributional decision. Third, the prompt is a strategic lever. Delegation separates the principal's economic patience from the agent's prompted strategic patience, a free deployment choice that is the single strongest driver of surplus division (90% of explained variance). Together these establish an equilibrium-referenced audit of AI agents along three dimensions: discounted efficiency, distributional profile, and operational reliability.", "url": "https://wpnews.pro/news/when-llm-agents-negotiate-private-information-and-dynamic-bargaining-in-supply", "canonical_source": "https://arxiv.org/abs/2608.07538", "published_at": "2026-08-11 04:00:00+00:00", "updated_at": "2026-08-11 04:16:20.197296+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research"], "entities": ["OpenAI", "Google", "Alibaba", "Qwen", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/when-llm-agents-negotiate-private-information-and-dynamic-bargaining-in-supply", "markdown": "https://wpnews.pro/news/when-llm-agents-negotiate-private-information-and-dynamic-bargaining-in-supply.md", "text": "https://wpnews.pro/news/when-llm-agents-negotiate-private-information-and-dynamic-bargaining-in-supply.txt", "jsonld": "https://wpnews.pro/news/when-llm-agents-negotiate-private-information-and-dynamic-bargaining-in-supply.jsonld"}}