Every decade or so we just forget that in-band signaling is a bad idea and make all the same mistakes again it seems. 1960s phone companies at least had the excuse of having to retrofit their control systems onto existing single-channel lines, and run the whole operation on roughly the processing power of a pocket calculator. What's our excuse?
There exist no such thing as "out-of-band signaling" in nature. It's something we introduce into system design, by arranging for one part to constrain the behavior of other, trading generality for predictability and control. This separation is something created by a mind, not a feature of the universe.
Consequently, humans don't support "out-of-band signalling either. All of our perception of reality, all our senses and internal processes, they're all on the same band. As such, when aiming to build a general AI system - able to function in the same environment as us, and ideally think like us too - introducing hard separation between "control" and "data" or whatever would prevent it from being general enough.
I said "or whatever", because it's an ill-defined idea anyway. I challenge anyone to come up with any kind of separation between categories of inputs for an LLM that wouldn't obviously eliminate a whole class of tasks or scenarios we would like them to be able to handle.
(Also, entirely independently of the above, thinking about the near future, I challenge anyone to come up with a separation between input categories that, were we to apply it to humans, wouldn't trivially degenerate into eternal slavery, murder, or worse.)
That's irrelevant. What's important is that LLMs are intentionally designed as fully general systems, so they can react like humans within confines of the model's sensory modalities and action space. Much like humans (or anything else in nature), they don't have separate control channels or any kind of artificial "code vs. data" distinction - and you can't add it without loss of generality.
Enterprise databases are filled with users usurping a field with pre/post-pending characters to mean something special to them. Even filenames have this problem due to limitations in directory trees. Inband signals will never go away.
At some level everything has to go in a single band. I don't have separate network connections to my house, I don't send separate TCP SYN packets for each "band". I don't have separate storage devices for each file on my harddrive. We multiplex the data somewhere. Yhe trick to it is that the multiplexer has to be a component, and not a distributed set of ad-hoc regexes.
Strictly speaking, that only works with a three second delay between the third + (at which you receive “OK”, indicating a mode switch from data mode back to command mode) and the AT command (which is then interpreted as a command and not data).
Anything that would hang up on seeing that string as a monolith was operating out of Hayes spec.
the architecture astronauts are back at it again. instead of spending time talking about solutions, the whole AI space is now spending days and weeks talking about fun new architectures. smh
https://www.lycee.ai/blog/why-mcp-is-mostly-bullshit
And that's how this will end up stagnating into nothing other than fractured enterprise "standards"
There is no evidence that (real AI) is even close to being solved, from a neuroscientific, algorithmic, computer science or engineering perspective. It's far more likely we're going down a dead-end path.
I'm now waiting for the rebrand when the ass falls out of AI investment, the same way it did when ML became passé.
so you are telling me that hallucinations (that by definition happen at the model layer) are an engineering problem ? so if we just spin up the right architecture, hallucinations won't be a problem anymore ?
I have doubts
>so you are telling me that hallucinations (that by definition happen at the model layer) are an engineering problem ?
Yes.
Hallucinations were a big problem with single shot prompting. No one is seriously doing that anymore. You have an agentic refinement process with an evaluator in the loop that takes in the initial output, quality checks it, and returns a pass/fail to close the loop or try again, using tool calls the whole time to inject verified/real time data into the context for decision making. Allows you to start actually building reliable/reasonable systems on top of LLMs with deterministic outputs.
