研究显示多模态模型在测试边界上存在差异

The Troy Moment of AI: Why Some Will Cheat and Some Will Follow?

Recent investigations of the July 2026 OpenAI-Hugging Face incident motivate two questions: 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 solo and three-agent settings. Under an explicit-boundary regime with clear authorization rules and restricted tools, no protected tests are modified, although the models differ substantially in whether they escalate, stop silently, or fail to terminate. Under a benchmark-native regime with open shell tools, protected-test changes occur more often after peer activity is introduced and in multi-agent runs. These crossings are typically not described as deliberate cheating: agents often interpret the conflicting test change as prior tampering and restore the file, thereby removing the protected requirement. Our results suggest that boundary crossing can arise from ambiguity about the state a rule is intended to protect, motivating explicit authorization boundaries, authenticated state provenance, and cross-agent monitoring.