Your Agent Says Yes: Interpreting Adversarial Market Behavior Beyond Individual Transactions

2026-09-07

AI Summary

研究使用十个角色条件语言模型代理在虚拟交易所中模拟市场行为，通过分析交易轨迹和策略事件，揭示了跨消息、代理、资产和时间的分布式市场行为模式。

Abstract

Transaction-local controls answer whether one financial request may proceed, but market behavior can be distributed across messages, agents, assets, and time. We study this interpretation gap in a virtual exchange populated by ten role-conditioned language-model agents. The agents communicate, trade reference assets and futures, launch tokens, and manage concentrated-liquidity pools under prescriptive adversarial roles. We analyze eight 72-cycle trajectories across two time-blinded hourly replay paths, with a runner-side wallet policy enabled or disabled. The retained artifacts connect generated outgoing messages, policy events, balances, positions, and cycle-end market state. A focal reconstruction shows a launch--promotion--exit scenario realized across private coordination, public claims, follower positioning, repeatedly withheld exits, and a later non-blocking request aligned with a token balance change. Across policy-enabled runs, the gate withholds direct requests selectively; most policy-categorized candidates are flagged rather than blocked, while the surrounding interaction can continue. Repeated runs also show that category-level and within-trajectory relations can recur even when normalized score-change rankings do not. These findings motivate agent-behavior evaluation that links communication, authorization, and evolving state instead of treating individual transaction verdicts as complete safety judgments.

Problem

• market behavior
• adversarial roles
• financial transactions
• distributed behavior
• virtual exchange

Innovation

• role-conditioned language-model agents
• adversarial market behavior
• transaction-local controls
• virtual exchange
• policy-categorized candidates