> Sometimes after a night’s sleep, we wake up with an insight on a topic or a solution to a problem we encountered the day before.

The current crop of models do not "sleep" in any way. The associated limitations on long term task adaptation are obvious barriers to their general utility.

> When conversing with LLMs, I never get the feeling that they have a solid grasp on the conversation. When you dig into topics, there is always a little too much vagueness, a slight but clear lack of coherence, continuity and awareness, a prevalence of cookie-cutter verbiage. It feels like a mind that isn’t fully “there” — and maybe not at all.

One of the key functions of REM sleep seems to be the ability to generalize concepts and make connections between "distant" ideas in latent space [1].

I would argue that the current crop of LLMs are overfit on recall ability, particularly on their training corpus. The inherent trade-off is that they are underfit on "conceptual" intelligence. The ability to make connections between these ideas.

As a result, you often get "thinking shaped objects", to paraphrase Janelle Shane [2]. It does feel like the primordial ooze of intelligence, but it is clear we still have several transformer-shaped breakthroughs before actual (human comparable) intelligence.

1. https://en.wikipedia.org/wiki/Why_We_Sleep 2. https://www.aiweirdness.com/