Misplaced legs clearly indicate lack is spatial reasoning - the llm can reason about verbal idea of a bicycle but not about the actual object. The fact that this model got it correct gives me a pause. Did they figure out spatial reasoning? Or did this complain trickle down to the training set?
Or better context curation - less lossy compression saving back to context. Maybe even jettisoning part context into an external semantic store instead of conpression. Or placing less data into context to start with.
Verifying sources is a recursive problem - where do you stop? Humans have intuitive feel for it, but agents don’t or at least not yet (I wonder if intuition is just a secondary neural net which is currently being added to the agents as we speak).
Also as a human you are able to examine agents erroneous trajectory, real or imaginary, without contaminating your own. Agent have a problem with that - as soon as someone else’s thought is in the context it can lose track of provenance and veracity. Sometimes I think we need a bloom filter to retroactively assign “dirty” flag to invalidated or questionable token spans already in the context.
Even before AI I strongly believed the internet was eventually going to have to move to a web of trust model. I think World ID (formerly Worldcoin) can be a really cool part of the solution to this, though people widely criticize it without fully understanding it and make assumptions that are wrong. But I agree that trust and verification is increasingly a large problem and one of the best ways to combat that is to actually choose unique identities to trust.
> I wonder if intuition is just a secondary neural net which is currently being added to the agents as we speak
Arguably, intuition is primary neural net, the only thing an LLM has without CoT, it just is spiky so humans only notice where it's below-average and just dismiss the rest as normal. Of course it's going to lag behind in some areas compared to others.
The Trump and former president terms were likely firmly stuck together in the embedding space. The model doesn’t validate every single token it produces because validation itself requires tokens. A bloom filter of outdated embeddings will help, when the labs get around to adding it.
Right, it takes some effort, but it’s worth it for the benefits of sub-second latency and reduced operational overhead. We expect the WAL format for existing commands to remain relatively stable across Postgres versions, while newer commands may need additional handling.
reply