Stanford 论文：更多信息会让部分 LLM 谈判智能体收益下降
Stanford 测了 10 个 LLM 谈判，发现给模型更多信息反而可能让 Claude Opus 5.5 收益从 94% 掉到 64%，选模型时得连信息量一起测。
Stanford 一篇新论文在交易、议价和双向拍卖场景测试了 10 个前沿 LLM，唯一变量是私有估值信息的披露精度。结果显示更多信息并非普遍有利：拍卖中完全披露让 Claude Opus 5.5 的可获取收益份额从 94% 降到 64%，Grok 4.7 从 97% 跌到 62%。交易场景里方向相反，精确估值让 GPT-6 Sol 的收益从 98% 降到 91%，却让 Claude Sonnet 5 从 72% 升到 86%。论文结论是构建多智能体系统时需要针对具体模型测试信息披露规则，更多细节会让部分模型要价更高。
New Stanford paper shows that more information helps some LLM agents trade better and hurts others, depending on the model.
AI agents buying and selling in an auction were told exactly how much each side valued the goods, Claude Opus 5.5’s share of possible gains fell from 94% to 64%.
Multi-agent systems need rules on what each agent sees about others, such as exact prices or only rankings. The usual assumption is that more information helps.
They ran 10 frontier LLMs in trading, bargaining, and double auctions, changing only how precisely private values were shared, and scored gains against known utilities.
In trade, exact values cut GPT-6 Sol's gains from 98% to 91% of the maximum but raised Claude Sonnet 5's from 72% to 86%. In auctions, full disclosure inflated price demands and dropped Grok 4.7 from 97% to 62%.
When agents negotiate for you, test the model and what it gets told together, because more detail makes some models greedier.
