All of these LLMs are getting better at being at an LLM
But GPT-5.5 is as useful an LLM can be; it has solved lemmas I've thought about for a year, it can implement typed STLCs in Rust when I give it a formal grammar, it can help me analyze Postgres planner dumps.
It's great at tasks that have short solutions but
- they cannot learn based on a project
- their long term planning capabilities are worse than worms
- they are unconfident in decision making
- their internal representations are disgusting compared to JEPA
- they don't have any "system clock" like humans and computers do
- LLM architecture is not modular like computer architecture or human brain architecture
There's so many issues with LLMs. I wish that companies can start working on the next generation of architectures before the bubble pops
Fable had very confident decision-making and would push past obstacles that Opus finds daunting :(
Totally agree! They also conflate things all the time (a major type of hallucination) and IIUC that can’t be solved with the current architecture, just patched over
> - their internal representations are disgusting compared to JEPA
You say this based on a theoretical understanding or did you inspect them?
Look at VLM mechanistic interpretability papers vs just pca on JEPA trained weights.
JEPA gives you interpretability for free.
I have not personally inspected them and my view is maybe a more exaggerated/dramatic claim of those working in the JEPA sphere
Sounds interesting, any links?
JEPA in chess leads to interpretable chess boards:
https://arxiv.org/abs/2606.11860
JEPA in image classification leads to interpretable image latents
https://arxiv.org/abs/2508.10104
Easy intro to JEPA, demonstrating that interpretability is as easy as running a PCA on latents
https://youtu.be/kYkIdXwW2AE?is=CfCBcy1jLt-FfI2E
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