> The cognition is a skill that is only possible on online learning which is the autonomous part the authors refer to, that is, learning by observing, interacting.
This is the "Claude Code" part, or even the ChatGPT (web interface/app) part. Large context window full of relevant context. Auto-summarization of memories and inclusion in context. Tool calling. Web searching.
If not LLMs, I think we can say that those systems that use them in an "agentic" way perhaps have cognition?
> This is the "Claude Code" part, or even the ChatGPT (web interface/app) part. Large context window full of relevant context. Auto-summarization of memories and inclusion in context. Tool calling. Web searching.
From what I've been learning in my uni, this is said pre-programmed. Cognition is really the ability from, out of no context, no knowledge of what you are capable of, to learn something. These tool calling and web searching are, in the end, MCP functions provided by the LLM provider themselves.
It's an entire academic discussion, about how things start. For example babies: they someone have a knowledge base on how to breath, how to cry, but they have absolute no knowledge on how to speak and it learns by the interactions with the parents.
LLMs try as much as they can create this by inference and pre-programmed functions, but they don't have a graph of memories with utility to weight their relevance in the context. As others said, the context window dies as soon as you close the session.
They also don't have the epistemic approach that is to know that another agent knows about something just by observing the environment they were all put in.
No, no they don't. Actual learning survives beyond "sufficient context window".
Start a new chat, and the "agentic" system will be as clueless as before
They can write to the filesystem, and future instances can read it (and write more). The agentic system does not remain as clueless as its LLM's first instantiation.
Do you need a notebook to remember who you are? The point is to update the model weights so it learns.
No, but I don't need floating point weights either. My evolved biological systems work very differently from artificial digital systems.
My point is that it's not the "model" that is the thing that is demonstrating cognition here, it's the "system" that uses the model and stores information and can retrieve it later. To that system, these notes are more the equivalent of my memories than my notebooks.
These are not "memories", as with every new session these are entirely new, never-before seen files that the "system" may or may not use.
And it is not "learning" (which was the initial claim) as the system never learns beyond what was already there in the training data, and any new information you supply are new data, from scratch, that is immediately forgotten once the session is over.
It's easy to prove: start a new session in your project and ask "what is this project about". Two days from now, in a new session, ask the same thing. Observe how in both cases it re-reads the files, greps source code etc. Meanwhile a system with actual memories and learning wouldn't have to do that.
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Memento notebooks are not learning