This makes sense to me because I've been having very accurate results with models from even 2+ years ago... but I had to "hold them right." Even when reasoning models and coding agents were just a gleam in Altman's and Amodei's eyes, I could tell a lot of the unrealized gains lay in building the right tools, harnesses and guardrails to manage the context and guide the model. (Relevant subthread as example: https://news.ycombinator.com/item?id=44171519)

But this article hints at deeper wins to be had. Consider that these models are operating on source code, which is a verbose, noisy, textual serialization of the intended syntax / semantic trees. TFA improves accuracy by retro-fitting some structure onto the text. But what if models could operate directly on these underlying structures themselves?

As a data point, there are projects like OpenRewrite, which encode a ton of information, from formatting to types with globally resolved dependencies for each symbol in what they call a "Lossless Semantic Tree", so that there is ~0 ambiguity about the code. When I worked with OpenRewrite (in the era before LLMs, how quaint!) compared to other tools, it produced the best results for code transformations with the highest fidelity to the surrounding code.

Now imagine if the agent has access to such detailed information. It would not have to waste tokens figuring incidental things out like formatting. Although I haven't tested it out myself, I believe Moderne (the maintainers of OpenRewrite) when they say that agents armed with LST-based tools make extremely accurate changes.

This is essentially the same reason why the answer to "Which is better, Vim or Emacs?" is "IntelliJ."

Now consider that these models are STILL operating on text as an input and output mode! What if they were multi-modally trained on source code and docs and their syntax / semantic trees? I don't even know what this would look like, but I'd bet this would produce the most accurate coding models ever -- probably neurosymbolic in the truest sense.