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Practical Benefits Unclear

Multiple commenters question what actual benefits this approach provides over external tool calling, asking for benchmarks, speed comparisons, and concrete use cases beyond elegance.

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Commenters remain skeptical about the practical utility of internalizing computation within transformer weights, questioning why this "elegant" approach is superior to leveraging faster, more reliable external tools. Critics highlighted a frustrating lack of benchmarks and released model weights, suggesting that without concrete evidence of speed or training advantages, the project currently feels like a theoretical repackaging of older neurosymbolic ideas. One particularly pointed perspective argued that just as humans outsource complex logic to computers, models should rely on external systems rather than inefficiently simulating internal machines. Ultimately, while the concept holds some curiosity for low-budget experimentation, the consensus is that its real-world value remains largely unproven.

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This seems like it has some potential, but is pretty much useless as it is. Shame there are no weights released - let alone the "compiler" tool they used to actually synthesize computational primitives into model weights. It seems like a "small model" system that's amenable to low budget experiments, and I would love to see what this approach can be pushed towards. I disagree with the core premise, it's basically the old neurosymbolic garbage restated, but embedding predefined computational primitives into LLMs could have some uses nonetheless.
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This shows the downside of using AI to write up your project. I see the eloquent sentences, but don't get the message. > This works, but the actual execution happened outside the model. The model specified the computation, then waited for an external system to carry it out. > Our transformer also emits a program, but instead of pausing for an external tool, it executes that program itself, step by step, within the same transformer. What's the benefit? Is it speed? Where are the benchmarks? Is it that you can backprop through this computation? Do you do so? Why is it good that it's "inside" the model? Just making it more elegant and nice? The tool was already "inside" the overall hybrid system. What's the actual problem?
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Interesting... But why? What is the benefit, other than increasing our understanding of model architectures? Our brains can also simulate turing machines, slowly. We automated that with computers that are faster and more reliable. So why not allow a model to use external much faster and reliable tools, just as we do?