Summary
Financial markets often check each transaction separately to decide if it’s allowed, but real market actions can be spread out across many messages, people, and times. This paper looks at a virtual market where computer agents talk, trade, and manage assets in ways that sometimes try to trick the system. The researchers studied how these agents coordinate by sharing messages and making moves that aren’t easy to catch when just looking at one transaction alone. Their work shows that to understand market risks better, we need to connect communication, authorization decisions, and changing market info instead of focusing only on individual transactions.
financial transaction controlsagent communicationvirtual exchangelanguage-model agentsadversarial rolesmarket behaviorauthorization policytoken launchliquidity poolsmarket state reconstruction
Authors
Zelin Li, Yiyun Su, Matt White, Zhipeng Wang, Xiao-Yang Liu, Tianyu Shi
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.