Really intriguing set of techniques to improve accuracy by generating multiple solutions. Even with the work to predict the most likely solutions, it's not clear to me based on the description how this could all be done efficiently. Would definitely be really impressive if it pans out on real-world use cases. Will look to kick the tires on this if I can get some time.
> it's not clear to me based on the description how this could all be done efficiently.
Depends how you define efficiency. The power use of this rig is a lot less than the large data centers that serve trillion parameter models. The page suggests that the final dollar cost per request is an order of magnitude lower than the frontier models charge.
Seems like the key insight is to train a small model that acts as a heuristic for embeddings that resemble quality code. I imagine a lot depends on how well this model is trained. And you could probably create specialized versions for different languages and domains.
Another interesting approach could be to use this set up with a language like Clojure or Common Lisp which facilitates interactive development. If you could hook up the agent directly to a REPL in a running program, then it could run tests with a lot less overhead.
I'm super confused. The small model "cost field" `rag-api/geometric_lens/cost_field.py` was trained on PASS_TASKS like "Write a function that counts vowels in a string." and FAIL_TASKS like "Write a function that converts a regular expression string to an NFA using Thompson's construction, then converts the NFA to a DFA.".
So it seems like it's a difficulty classifier for task descriptions written in English.
This is then used to score embeddings of Python code, which is a completely different distribution.
Presumably it's going to look at a simple solution, figure out it lands kinda close to simple problems in embedding space and pass it.
But none of this helps you solve harder problems, or distinguish between a simple solution which is wrong, and a more complex solution which is correct.
> But none of this helps you solve harder problems, or distinguish between a simple solution which is wrong, and a more complex solution which is correct.
It does because hallucinations and low confidence share characteristics in the embedding vector which the small neural learns to recognize. And the fact that it continuously learns based on the feedback loop is pretty slick.
I think the goal is to have a light heuristic that helps find plausibly useful solutions. They're still going to go through a testing phase as a next step, so this is just a very simple filter to decide what's even worth testing.