I recall recent work [ACE](https://www.arxiv.org/abs/2510.04618) and [GEPA](https://arxiv.org/abs/2507.19457) where models get improved by adapting and adopting different kinds of prompt. The improvements will be expected to be more generalized than fine-tuning.
Readit NewsWeaver_zhu commented on Recursive Language Models (RLMs) alexzhang13.github.io/blo... · Posted by u/talhof8
IMO the author is a little over-claiming this work by naming 'recursive'. Quote from this blog:
> Lastly, in our experiments we only consider a recursive depth of 1 — i.e. the root LM can only call LMs, not other RLMs.
> but we felt that for most modern “long context” benchmarks, a recursive depth of 1 was sufficient to handle most problems.
I don't think a size 2 call stack algorithm should be regarded as 'recursive'.
It is not. It's a terrible comparison. Qwen, deepseek and other Chinese models are known for their 10x or even better efficiency compared to Anthropic's.
That's why the difference between open router prices and those official providers isn't that different. Plus who knows what open routed providers do in term quantization. They may be getting 100x better efficiency, thus the competitive price.
That being said not all users max out their plan, so it's not like each user costs anthropic 5,000 USD. The hemoragy would be so brutal they would be out of business in months