Maybe it means both but perhaps the emphasis is on speed.
How was the formalization of Python accomplished? What formal proof framework do you use?
How do you reproduce the optimizations across several instances / runs of the program? (since the source code doesn't change... assuming you don't redo the same work over and over?)
What visibility, tracking and logging is there into the actual code running? Debugging is an extremely important part of building software, especially if there's a demon switching out binary code constantly underneath. Even if the recompiler is perfect (it won't be), user code never is.
Why is the superoptimization and visibility into the data not surfaced back to the level of the source code? This seems like a waste.
- verification
- superoptimization specialized to a particular piece of hardware
- patching binaries while they are running
all great
That being said, I'm getting some weird vibes from the prose on this site. Little phrases and claims that seem intended for the purposes of marketing, but conflate things with clear technical meaning in confusing ways.
For instance: I watched the video ("a code execution model has to be right every time") which implicitly communicates something like "the model cannot hallucinate" ... of course, the agentic LM is still probabilistic, but it is wrapped with verification tools (to reason about semantic equivalence), and is likely post-trained to be good at using those tools.
This strikes me as a conflation between the model and the harness which is intended to strike a marketing point (like: "it doesn't hallucinate") but which is clearly a ridiculous claim to make (falling down the slope: any harness which does error correction for improper tool calls might also be a "model which makes no mistakes")
Also: "self-improving code"
that's not really it, is it? Self-improving code would be code that runs itself and improves itself. This is code I run, which Maxwell watches and improves ... no?
Related: I dislike the usage of the word "model" in the above discussions on the site. Do we want to call everything a "model" now? Can anyone invent a new frontier "model" by post training an agentic LM with tools?
Also, let's say you are optimizing kernels on GPUs. This can be subtly fraught: for instance, it was well-known for awhile that one should only use certain pieces of software (vLLM, for instance) to reproduce papers (which used vLLM) there are non-deterministic sources of randomness (including, of course, floating point) that would prevent one from recovering the performance claimed in a paper ...
> Self-improving code would be code that runs itself and improves itself.
But people who write JITs don't call the overall process (original code, JIT) "self-improving code", do they?
What is the "self" in (original code, JIT)? Is it the entire process? But the JIT doesn't make itself better as it runs? Is all code that runs in a managed runtime with dynamic analysis self-improving?
A much better term might be "dynamically-improving code"
But related and to the point of my original message: the phrase "self-improving" has been used in vogue by AI research / labs ... and this is another example of something which is not a clear term, and seems intended for marketing
The homepage does not do a very good job of explaining what we're looking at:
It appears to be a combination of two things:
1) A formalization of the Python language
2) An LLM which hooks into running programs and hot-swaps code with verifiably-equivalent behavior but better runtime performance, found via superoptimization and then verified via the formalization rules
This is brilliant, and in hindsight one of those "I can't believe nobody had thought of this yet" sort of things.
It's really unlike a compiler. For example, for a function like 'sort', the optimisation can:
- Use quicksort if the data distribution is generic.
- Use insertion sort if the data is nearly sorted
And so on... As long as these can be proven sound, Maxwell can pick the best optimisation in real time.