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RagnarD 11 hours ago

Being able to perform precise math in an LLM is important, glad to see this.

jdjdndnzn 10 hours ago | parent | next [-]

Just want to point out this comment is highly ironic.

This is all a computer does :P

We need llms to be able to tap that not add the same functionality a layer above and MUCH less efficiently.

Nuzzerino 10 hours ago | parent [-]

> We need llms to be able to tap that not add the same functionality a layer above and MUCH less efficiently.

Agents, tool-integrated reasoning, even chain of thought (limited, for some math) can address this.

RagnarD 9 hours ago | parent [-]

You're both completely missing the point. It's important that an LLM be able to perform exact arithmetic reliably without a tool call. Of course the underlying hardware does so extremely rapidly, that's not the point.

kruffalon 5 hours ago | parent | next [-]

Could you explain why that is?

koolala 5 hours ago | parent [-]

A tool call is like 100,000,000x slower isnt it?

kruffalon 3 hours ago | parent [-]

No idea really, but if it is speed related I would have thought that OP would have used faster rather than importance to try and make their point.

koolala 13 minutes ago | parent [-]

It's both. Being dirrctly a part of it makes it integrated into its intelligence for training and operation.

jdjdndnzn 7 hours ago | parent | prev [-]

The computer ALREADY does do math reliably. You are missing the point.

koolala 6 hours ago | parent | prev | next [-]

That would be cool. A way to read cpu assembly bytecode and then think in it.

It's slower than real cpu code obviously but still crazy fast for 'thinking' about it. They wouldn't need to actually simulate an entire program in a never ending hot loop like a real computer. Just a few loops would explain a lot about a process and calculate a lot of precise information.

5o1ecist 8 hours ago | parent | prev [-]

Why?