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Agentic pelican on a bicycle

robert-glaser.de

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Re: Agentic pelican on a bicycle

#31

Post by tptacek »

measurablefunc wrote: post #16

Iterating a Markov chain does not make it any more or less "agentic". This is yet another instance of corporate marketing departments redefining words b/c they are confused about what exactly they're trying to build & sell.

It's agentic because it's an iterated loop that relies on tool calls. The conversion of prompt to SVG is (presumably) a pure product of inference. But the rasterized SVG that the loop evaluates isn't; it's the product of hardcoded svg->jpg translation code (the model isn't inferring the raster). The loop is thus, in some small way, "grounded" (though not as firmly as a coding agent is grounded in a type-checking compiler's refusal to admit a hallucinated API).

Re: Agentic pelican on a bicycle

#32

Post by measurablefunc »

tptacek wrote: post #31
measurablefunc wrote: post #16

Iterating a Markov chain does not make it any more or less "agentic". This is yet another instance of corporate marketing departments redefining words b/c they are confused about what exactly they're trying to build & sell.

It's agentic because it's an iterated loop that relies on tool calls. The conversion of prompt to SVG is (presumably) a pure product of inference. But the rasterized SVG that the loop evaluates isn't; it's the product of hardcoded svg->jpg translation code (the model isn't inferring the raster). The loop is thus, in some small way, "grounded" (though not as firmly as a coding agent is grounded in a type-checking comp…

How does your argument work if I move the rasterization into the Markov chain? Or is your assumption that (SVG, JPG) pairs can never be encoded w/ a neural network?

Re: Agentic pelican on a bicycle

#33

Post by simonw »

davesque wrote: post #2

I feels like it's a bit hard to take much from this without running this trial many times for each model. Then it would be possible to see if there are consistent themes among each model's solutions. Otherwise, it feels like the specific style of each result could be somewhat random. I didn't see any mention of running multiple trials for each model.

Oddly enough, I've found models are actually quite consistent in their drawings of pelicans riding bicycles.

I remember I even had one case where there was a stealth model running in preview via Open Router and I asked it for an SVG of a pelican riding a bicycle and correctly guessed the model vendor based on the response!

Re: Agentic pelican on a bicycle

#34

Post by tptacek »

measurablefunc wrote: post #32
tptacek wrote: post #31

Earlier quoted context omitted.

It's agentic because it's an iterated loop that relies on tool calls. The conversion of prompt to SVG is (presumably) a pure product of inference. But the rasterized SVG that the loop evaluates isn't; it's the product of hardcoded svg->jpg translation code (the model isn't inferring the raster). The loop is thus, in some small way, "grounded" (though not as firmly as a coding agent is grounded in a type-checking comp…

How does your argument work if I move the rasterization into the Markov chain? Or is your assumption that (SVG, JPG) pairs can never be encoded w/ a neural network?

If that was what was actually happening you'd have a point, but it isn't.

Re: Agentic pelican on a bicycle

#35

Post by measurablefunc »

tptacek wrote: post #34
measurablefunc wrote: post #32

Earlier quoted context omitted.

How does your argument work if I move the rasterization into the Markov chain? Or is your assumption that (SVG, JPG) pairs can never be encoded w/ a neural network?

If that was what was actually happening you'd have a point, but it isn't.

The definitions are not coherent. It's obvious enough to anyone who understands the technical details.

Re: Agentic pelican on a bicycle

#36

Post by tptacek »

measurablefunc wrote: post #35
tptacek wrote: post #34

Earlier quoted context omitted.

If that was what was actually happening you'd have a point, but it isn't.

The definitions are not coherent. It's obvious enough to anyone who understands the technical details.

The technical definition of an agent is an LLM being called in a loop, some of which calls include tool definitions. That's exactly what this is.

Re: Agentic pelican on a bicycle

#37

Post by orbital-decay »

lubujackson wrote: post #4

What I take from this is that LLMs are somewhat miraculous in generation but terrible at revision. Especially with images, they are very resistant to adjusting initial approaches. I wonder if there is a consistent way to force structural revisions. I have found Nano Banana particularly terrible at revisions, even something like "change the image dimensions to..." it will confidently claim success but do nothing.

Revision should be much easier than generation, e.g. reflection style CoT (draft-critique-revision) is typically the simplest way to get things done with these models. It's always possible to overthink, though.

Nano Banana is rather terrible at multi-turn chats, just like any other model, despite the claim it's been trained for it. Scattered context and irrelevant distractors are always bad, compressing the conversation into a single turn fixes this.

Re: Agentic pelican on a bicycle

#38

Post by measurablefunc »

tptacek wrote: post #36
measurablefunc wrote: post #35

Earlier quoted context omitted.

The definitions are not coherent. It's obvious enough to anyone who understands the technical details.

The technical definition of an agent is an LLM being called in a loop, some of which calls include tool definitions. That's exactly what this is.

"Effectful loops" or "augmented loops" are much more descriptive of what is actually going on & do not confuse the reader w/ incoherent definitions of "agency".

Re: Agentic pelican on a bicycle

#39

Post by [deleted] »

andy99 wrote: post #9

This wasn’t just “add more details”—it was “make this mechanically coherent.” The overall text doesn’t appear to be AI written, making this all the more confusing. Is AI making people write this way now on their own? Or is it actually written by an LLM and just doesn’t look like it?

[deleted]

Re: Agentic pelican on a bicycle

#40

Post by ilaksh »

lubujackson wrote: post #4

What I take from this is that LLMs are somewhat miraculous in generation but terrible at revision. Especially with images, they are very resistant to adjusting initial approaches. I wonder if there is a consistent way to force structural revisions. I have found Nano Banana particularly terrible at revisions, even something like "change the image dimensions to..." it will confidently claim success but do nothing.

The prompt just said to iterate until they were satisfied. Adding something like "don't be afraid to change your approach or make significant revisions" would probably give different results.
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