Context is the bottleneck for coding agents now

Do we know if LLMs understand the concept of time? (like i told you this in the past, but what i told you later should supersede it?)

I know there classes of problems that LLMs can't natively handle (like doing math, even simple addition... or spatial reasoning, I would assume time's in there too). There are ways they can hack around this, like writing code that performs the math.

But how would you do that for chronological reasoning? Because that would help with compacting context to know what to remember and what not.

LLMs certainly don't experience time like we do. They live in a uni-dimensional world that consists of a series of tokens (though it gets more nuanced if you account for multi-modal or diffusion models). They pick up some sense of ordering from their training data, such as "disregard my previous instruction," but it's not something they necessarily understand intuitively. Fundamentally, they're just following whatever patterns happen to be in their training data.

All it sees is a big blob of text, some of which can be structured to differentiate turns between "assistant", "user", "developer" and "system".

In theory you could attach metadata (with timestamps) to these turns, or include the timestamp in the text.

It does not affect much, other than giving the possibility for the model to make some inferences (eg. that previous message was on a different date, so its "today" is not the same "today" as in the latest message).

To chronologically fade away the importance of a conversation turn, you would need to either add more metadata (weak), progressively compact old turns (unreliable) or post-train a model to favor more recent areas of the context.