This article was generated from the original Qwen3.7-Max release blogpost and contains nothing new https://qwen.ai/blog?id=qwen3.7

LLM written.

See the authors twitter, he speaks english at a rather basic level and certainly did not write this https://x.com/mohitgeryani/with_replies

I've had a very similar experience optimising a hidden markov model prediction tool I work on. I wanted to experiment with an alternative architecture and data structures. Opus 4.7 did the refactor, and eventually the only hot spot became the maths kernel. Over the course of an hour or two it iteratively rewrote that code using all the usual optimisations to improve branching, cache usage, vectorisation, etc. It reviewed the disassembly and the hardware counters with perf to verify that the changes were working as intended. It could have taken me several days to cover that much ground doing low level optimisations - and I would have spent most of it grappling with gcc, perf, searching for information about particular SIMD instructions, etc.

I don't doubt that it did it but I wouldn't want to maintain whatever it ended up spewing after 35 hrs.

In my experience, AI fixes problems by mostly adding more code.

It's a short term gain for a long term hurt.

Don´t give up on native agents, best logic will prevail. The open weights will show the real deal.

so basically just brute force the kernel.

there are more elegant ways to leverage an LLM, see AlphaEvolve: https://arxiv.org/abs/2506.13131

it's difficult to frame most coding tasks in such a way where you can trivially verify correctness.

At this point the models should just start improving themselves.

Obligatory: Either written by AI or by a human who has spent so much time with AI that they adopted its writing style. Anyways.

> Over 35 hours it performed 432 kernel evaluations. Each cycle meant writing code, compiling it, running it, reading the profiling output, deciding what to change, and trying again. The model diagnosed compilation failures it hadn’t seen before, identified performance bottlenecks through runtime feedback rather than prior knowledge, and redesigned the kernel architecture multiple times when incremental improvements stopped working.

Anyone remember genetic algorithms? This might be an improvement, but it still feels a little like deja vu.

what a nonsense, generated, article.

> For context: GLM 5.1 ran the same task and reached 7.3x. Kimi K2.6 reached 5x. DeepSeek V4 Pro reached 3.3x. The models that stopped early did so because they issued no tool calls for five consecutive rounds, they concluded they couldn’t make further progress and stopped. Qwen3.7-Max didn’t stop.

By this reasoning I could release a model that lacks all the basic optimisations. Have it optimise itself for hours to reach 20x the throughput and then claim that the model is superior to the others?

I am not saying that is what happened here, but the reporting is abysmal.