arXiv:2605.31514v1 Announce Type: cross Abstract: Much research has been carried out on large language models (LLMs) and LLM-powered agentic workflows. However, many works within the field state emergence of, ascribe to, or assume, generalised anthropomorphic attributes to them (e.g., morality or understanding of natural language). Our goal is not to argue in favour or against the existence of these attributes, but to point out that these conclusions could be incorrect. For this we build and train a simple neural network on the videogame Age of Empires II, and note that any entity in a suffici
This paper is part of a growing body of research questioning the anthropomorphic interpretations of AI capabilities, particularly LLMs, as the field matures and faces calls for more rigorous evaluation.
A strategic reader should care because unchecked assumptions about AI consciousness or human-like understanding can lead to flawed policy, investment, and development decisions, overstating current capabilities.
The paper encourages a more grounded and less anthropocentric view of AI systems, potentially shifting the rhetoric around human-like AI towards a focus on functional capabilities rather than emergent sentience.
- · AI ethicists
- · Pragmatic AI development
- · AI safety researchers
- · AI hype cycle
- · Anthropomorphic AI narratives
The paper directly challenges the notion that LLMs inherently possess human-like attributes like morality or understanding.
This reframing could lead to more realistic expectations and a de-escalation of certain AI anxieties or exaggerated claims.
Long-term, a more disciplined approach to AI attribution might foster more robust and explainable AI systems, as developers focus on measurable functionalities.
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Read at arXiv cs.AI