EscalAtion: Assessing Multi-Agent Risks in Military Contexts

We investigate the economic potential of Transformative AI, focusing on "temporal coherence"—the ability to maintain goal-directed behavior over time—as a critical, yet underexplored, factor in task automation. We argue that temporal coherence represents a significant bottleneck distinct from computational complexity. Using a Large Language Model to estimate the 'effective time' (a proxy for temporal coherence) needed for humans to complete remote O*NET tasks, the study reveals a non-linear link between AI coherence and automation potential. A key finding is that an 8-hour coherence capability could potentially automate around 80-84\% of the analyzed remote tasks.

We offer initial investigations into whether the untrusted model can 'simulate' the trusted monitor: is U able to successfully guess what suspicion score T will assign in the APPS setting? We also offer a clean, modular codebase which we hope can be used to streamline future research into this question.

This project explores vulnerabilities in AI evaluation mechanisms, specifically focusing on how AI agents might influence their overseeing AI judges through manipulative instructions embedded within their Chain-of-Thought (CoT) reasoning. The research involved experiments with DeepSeek-R1 conditioned to insert subtle directives aimed at deceiving evaluation models. Results showed significant variation in the robustness of different evaluator models against this manipulation, with some models, like Claude 3.5 Sonnet, resisting influence effectively, while others, like Llama 3.3 70B and Gemini 2.0 Flash, proved highly susceptible. The study highlights critical concerns regarding AI evaluation integrity, recommending improvements in maintaining consistent and principled evaluation boundaries.