91–100 of 168 posts
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Can't the LLMs build scripts/tools etc that help generate creative ideas? e.g. have the LLM generate multiple lists of characters, themes etc and then have a script hooked up to a RNG pick from the lists to create more "creative" ideas.
That's pretty low-value creativity. Real innovation doesn't come from mad libs.
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If a new physics break through gets published today, no existing model will be able to fully integrate it - beyond a context window. If I put the paper in my session and it isnt in yours the model knows nothing. It wont retain it past that session. Models are trained, they do not learn.
I think GP is using a different level of abstraction from yours in their metaphor. You are saying: Pre-Training == Everything you store in your memory throughout your life. Model weights == The lessons you learned Context == whatever you're currently thinking about One inference run == one thought They are saying: Pre-Training == building the DNA template of human brain through millions of years evolution. Model weig…
Something about this is deeply funny to me: > In an iterated prisoner's dilemma game with communication, agents all settle upon the same strategy and they all defect at the same time, tanking their overall rewards. It’s not always consistent, but humans have a higher capability of self-awareness. It’s kind of telling that these Claudes don’t seem to consider this pretty obvious failure mode. Overall I think this all…
I agree - I think one of the biggest reasons memory systems fail in LLMs is that they have poor theory of mind - they're terrible at considering how others will react. Both humans yes, but also future versions of itself. When asked to give advice to itself, it pontificates at length about trivial stuff it already knows and fails to emphasize the stuff that was new or interesting
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> It’s not always consistent, but humans have a higher capability of self-awareness. It’s kind of telling that these Claudes don’t seem to consider this pretty obvious failure mode. We need better words to describe this than "self-awareness" or "consider". These words mean fundamentally different things when speaking about humans or clankers.
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Honestly I think it's memory that's holding agents back. They have a context window (short-lived) and some tricks with file recording, but that's not quite what is needed. Agents can't look back and correct their mistakes. People make mistakes, remember them, and do better next time. But agents? If they haven't written them down somewhere they'll make the same mistake again. Perhaps, we need agents that can relearn o…
Learning/state compression can emerge naturally in a huge swarm like this. It's crude and inefficient but so is everything about current LLM tech.
My master's research focuses on coordination among LLM-based agents, driven by the same motivations as the article.
One phenomenon I have focused on, though it did not appear in this specific work, is bounded rationality. Yes, agents lack social perception, they focus on one-to-one tasks and are trained in game theory and other maximization strategies. Yet, what intrigues me most is that we humans rely on heuristics precisely because our capacity to maximize gains is severely limited, a limitation that gives rise to social emergent phenomenas. As models become increasingly capable of complex reasoning, the question arises: will interactions between them give rise to the same social properties we exhibit?
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Learning/state compression can emerge naturally in a huge swarm like this. It's crude and inefficient but so is everything about current LLM tech.
Absurd generalizations like this usually require a citation of some kind to be taken seriously.
[1] After a training-time nudge, of course.
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Why? Unjustified linguistic analogy is a huge part of AI companies success. If we want to actually understand how LLMs work and differ in their operation from humans we need to develop appropriate language, not latch onto some totally unproven equivalence that makes Dario's eyes water with all the free boosting we are doing for him. Lay people might talk about traditional computers "thinking" but there's a reason act…
Precisely. This is akin to my dad, back in 1995, telling me to stop moving the mouse cursor in MS Paint because the computer was "thinking" (he heard the HDD buzzing). That I can understand.
But surely we can expect more from people here than from a carpenter in a tiny mountain village who bought his son a computer in the 90s. (The second one in town!)
Something about this is deeply funny to me: > In an iterated prisoner's dilemma game with communication, agents all settle upon the same strategy and they all defect at the same time, tanking their overall rewards. It’s not always consistent, but humans have a higher capability of self-awareness. It’s kind of telling that these Claudes don’t seem to consider this pretty obvious failure mode. Overall I think this all…
Something about this is deeply funny to me: > In an iterated prisoner's dilemma game with communication, agents all settle upon the same strategy and they all defect at the same time, tanking their overall rewards. It’s not always consistent, but humans have a higher capability of self-awareness. It’s kind of telling that these Claudes don’t seem to consider this pretty obvious failure mode. Overall I think this all…
In VC investing there is the saying that most of the value comes from the outliers. I think it’s the same for a lot of domains. I read an interesting article recently on LLMs homogeneity when writing fiction: https://arxiv.org/abs/2604.03136 It seem that at scale LLMs output is the average of their dataset, they all cluster around the same space, where human creativity comes with more variance, exploring way more of…