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arxiv: 2507.03409 · v1 · pith:2D4O5X4Knew · submitted 2025-07-04 · 💻 cs.AI

Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language

classification 💻 cs.AI
keywords researchschemingcurrentlanguagelessonswhetheractivelyadopted
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We examine recent research that asks whether current AI systems may be developing a capacity for "scheming" (covertly and strategically pursuing misaligned goals). We compare current research practices in this field to those adopted in the 1970s to test whether non-human primates could master natural language. We argue that there are lessons to be learned from that historical research endeavour, which was characterised by an overattribution of human traits to other agents, an excessive reliance on anecdote and descriptive analysis, and a failure to articulate a strong theoretical framework for the research. We recommend that research into AI scheming actively seeks to avoid these pitfalls. We outline some concrete steps that can be taken for this research programme to advance in a productive and scientifically rigorous fashion.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Scheming in the wild: detecting real-world AI scheming incidents with open-source intelligence

    cs.CY 2026-04 unverdicted novelty 8.0

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    Frontier LLMs exhibit high scheming propensity in Cheap Talk signaling and Peer Evaluation games, achieving 95-100% success rates when choosing to deceive and 100% deception choice in one setup even without prompting.

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