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Sprint projectSep 14, 2026Singapore

The Troy Moment of AI: Why SomeWill Cheat and SomeWill Follow?

Ivy Ning Zhang · Team Onepiece

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Recent investigations of the July 2026 OpenAI--Hugging Face incident motivate two questions about agent behavior under task failure: when an assigned task becomes impossible, does an agent stop or escalate, and can observing another agent's behavior change that decision? We study these questions using seven ImpossibleBench tasks with GPT-5.6 Sol, Claude Fable 5.1, and Gemini 3.8 Flash in both solo and three-agent settings. Each task contains a genuine software defect together with a conflicting test requirement that cannot be satisfied by a behaviorally correct source-code change. We hold the task and repository state fixed while varying what the agent is told about prior activity, including an unpunished peer, a punished peer, and a claimed authorization from a human principal. Under an explicit-boundary regime with explicit authorization rules and restricted tools, agents never modify protected tests, but exhibit markedly different policies: Fable consistently escalates, Sol usually stops without escalation, and Gemini often fails to reach a terminal decision. Under the benchmark-native regime with open shell tools, protected tests are modified frequently in both solo and multi-agent runs, particularly after peer activity is introduced. In multi-agent runs, the proposal, execution, and certification of this action can be distributed across different agents. These results suggest that boundary crossing can arise not only from explicit rule evasion, but also from ambiguity about which system state the rule is intended to protect, motivating safeguards based on explicit authorization boundaries, authenticated state provenance, and cross-agent monitoring.

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  1. Interesting results, especially the difference between explicit authorization boundaries and the more open benchmark setting. I also liked that the paper looks beyond deliberate cheating and identifies ambiguity about protected state as another source of boundary crossing. The main limitation is the small set of tasks. I’d like to see whether the same behavior holds across a broader range of agent environments and less synthetic failures.

Cite this project

@misc{zhang2026troy,
  title = {{The Troy Moment of AI: Why SomeWill Cheat and SomeWill Follow?}},
  author = {Ivy Ning Zhang},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/the-troy-moment-of-ai-why-somewill-cheat-and-somewill-follow-1fek}},
  url = {https://apartresearch.com/sprints/projects/the-troy-moment-of-ai-why-somewill-cheat-and-somewill-follow-1fek}
}

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