> they lack an explicit architecture for the executive control of attention found in humans
Deceptive terminology strikes again! The "attention" mechanism in transformers appears (to my understanding at least) to have about as much to do with human attention as the "neurons" in a multi-layer perceptron have to do with biological neurons.
That said, the core premise of building in something that mimics executive function is an intriguing one (which I assume has been explored before but it's not something I'm familiar with).
The first thing I do when I see a paper that claims transformers fundamentally can't do X or Y is to look at the models under test:
> To evaluate generalizability, we conducted tests of GPT-5 (41), Claude Opus 4.1 (42), and Gemini 2.5 Pro (43) from 2025 September
The problem with empirical negative results on LLMs is that they can't rule out that the alleged deficiencies disappear with increased scale and the right fine-tuning. It's like saying my dog has trouble with subject-verb agreement, so meat brains are "fundamentally limited in their capacity for grammar".
I can accept that current LLMs (even latest generation) might exhibit cognitive gaps similar to those we see in humans with deficient executive function, I can't accept these gaps as evidence of fundamental limits of the transformer architecture. LLMs are universal function approximators. Executive function is a function. Yes, yes, it's well-known that transformers have a circuit complexity limit set by layer count and whatever. The limit disappears once you allow for autoregression. Nobody cares about the limits of AI inside a single forward pass.
I have high confidence that with the right sort of training, executive function gaps in LLM can be addressed. I'm not convinced that the problem is the architecture per se.
> they lack an explicit architecture for the executive control of attention found in humans
Deceptive terminology strikes again! The "attention" mechanism in transformers appears (to my understanding at least) to have about as much to do with human attention as the "neurons" in a multi-layer perceptron have to do with biological neurons.
That said, the core premise of building in something that mimics executive function is an intriguing one (which I assume has been explored before but it's not something I'm familiar with).
this is a nice study but i don’t think it’s actually good argument
The first thing I do when I see a paper that claims transformers fundamentally can't do X or Y is to look at the models under test:
> To evaluate generalizability, we conducted tests of GPT-5 (41), Claude Opus 4.1 (42), and Gemini 2.5 Pro (43) from 2025 September
The problem with empirical negative results on LLMs is that they can't rule out that the alleged deficiencies disappear with increased scale and the right fine-tuning. It's like saying my dog has trouble with subject-verb agreement, so meat brains are "fundamentally limited in their capacity for grammar".
I can accept that current LLMs (even latest generation) might exhibit cognitive gaps similar to those we see in humans with deficient executive function, I can't accept these gaps as evidence of fundamental limits of the transformer architecture. LLMs are universal function approximators. Executive function is a function. Yes, yes, it's well-known that transformers have a circuit complexity limit set by layer count and whatever. The limit disappears once you allow for autoregression. Nobody cares about the limits of AI inside a single forward pass.
I have high confidence that with the right sort of training, executive function gaps in LLM can be addressed. I'm not convinced that the problem is the architecture per se.