arXiv cs.AI· Sepehr Ilami, Babak Heydari·· 1 天前AI 评分20
通过信息网络引导多智能体 LLM 系统的涌现行为
Steering Emergent Behavior in Multi-Agent LLM Systems via Information Networks
AI 导读
一项被作者撤回的预印本研究让 10 个相同 LLM 智能体在 1,080 个回合、6 类博弈中重复决策，把"谁观察谁"的信息网络作为独立设计对象。结果显示信息价值取决于对齐程度：同伴信息在个体行为与集体目标一致时有帮助，但在搭便车或拥堵外部性下会放大有害行为，公共品博弈中完全不共享信息反而最优。固定预算下，"杠铃式"模块化连接最差，与人类演化博弈论的网络互惠直觉相反。
正文 · 原文
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Abstract:Large language model (LLM) agents increasingly operate in groups, each pursuing an individual objective while jointly determining system performance. If an operator controls which agent observes which other agent, but not their payoffs or messages, how should observations be structured to raise collective welfare? We make the information network (who observes whom) a design object, decoupled from the fixed interaction network over which agents act. Ten identical LLM agents play repeated group-decision games; by default each observes only an anonymous summary of the group's previous-round outcome, and a manager with a fixed budget adds directed observation links that reveal peers' past actions. Across 1,080 episodes and six games spanning coordination, anti-coordination, social dilemmas, and discovery, we ask how much information to share and, at a fixed budget, who should observe whom. Disclosure creates behavioral coupling: observation acts as a contagion channel whose welfare effect depends on the alignment between individually attractive behavior and the collective objective. First, the value of information is alignment-contingent: more peer information tends to help or be neutral when individual behavior is aligned with the collective goal, but under free-riding or congestion externalities it amplifies collectively harmful behavior; in a public-goods game the group does best with no information at all. We read this sign separation as a cross-game regularity within one controlled LLM population, not a model-invariant law. Second, at a fixed budget, who observes whom matters primarily when coupling is harmful: a modular "barbell" that lets sub-populations entrench harmful behavior is reliably the worst wiring, reversing the network-reciprocity intuition from human evolutionary game theory. These results argue against a default of full transparency for agent collectives.
| Comments: | It is preliminary and is not suitable for public release yet |
| Subjects: | Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Social and Information Networks (cs.SI) |
| ACM classes: | I.2.11 |
| Cite as: | arXiv:2611.11714 [cs.MA] |
| (or arXiv:2611.11714v2 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2611.11714 arXiv-issued DOI via DataCite |
Submission history
From: Sepehr Ilami [view email]
[v1]
Mon, 9 Nov 2026 00:18:26 UTC (277 KB)
[v2]
Tue, 10 Nov 2026 22:49:42 UTC (1 KB) (withdrawn)
来源：arXiv cs.AI · arxiv.org