微软与约克大学论文：LLM类人属性测试存在根本缺陷

New Microsoft + York Univ paper argues that LLMs should not be treated as human-like without clear tests and narrower claims.

Many studies ask whether LLMs have things like understanding, empathy, anxiety, or self-awareness, but they often build those ideas into the test from the start.

The author shows that, in principle, the old strategy game can implement logic gates, train a tiny perceptron, and serve as a substrate for computation.

If the same language model could be rebuilt inside a game, with goats moving around as bits, would we still say it “understands,” “feels anxiety,” or “has empathy” when it produces the same sentence?

The point is not that the game is secretly intelligent, but that the same computation can be represented in a very different form.

If an LLM-like system were rebuilt inside that game, its answers might stay similar, but people would probably find its “feelings” or “understanding” much less convincing.

The authors argue that this shows a big measurement problem: many human-like claims about LLMs may depend on the interface and the observer, not only on the system itself.

The paper is not saying LLMs definitely lack human-like attributes, or that all talk of AI cognition is nonsense.

It is saying that many experiments smuggle the conclusion into the setup: they assume the model has, or cannot have, a human-like property, then interpret behavior through that assumption.

Link – arxiv. org/abs/2605.31514

Title: "If LLMs Have Human-Like Attributes, Then So Does Age of Empires II"