I think these conversations are directed by the parties funding the efforts. Example: "we (large company) want a fast AV2 decoder" -> they pay a specialized team to do it -> this team works in C for the most part, so it is done in C. If there were financial incentives to do it in Rust, they'd pay more for a Rust decoder.
JPEG XL is mainly based on unique image-specific research, but you're right to say a lot of the techniques are compatible with videos in theory (the XYB color space comes to mind). AVIF is an AV1 OBU in an image-specific container, and required a lot of image-specific engineering to make AV1's tools useful for images; see libaom's tune "iq", and the same in SVT-AV1. The compression gains translated when engineering effort went into creating bespoke implementations, and the same may happen for LLMs if I had to guess.
I'm a big fan of JPEG XL, but even its most dedicated fans have given up the argument that it is the best for compression efficiency. AVIF's generational leap took place in August 2024 with Tune Still Picture in SVT-AV1-PSY, so much so that Google integrated it into their own encoder and has done very impressive work optimizing it further for the human visual system. JPEG XL's strongest quality is its featureset; lossless JPEG recompression, for example, is really incredible
If you run the `validate.py` script available in the repo, you should see correlation numbers similar to what I've pre-tested & made available in the README: fssimu2 achieves 99.97% linear correlation with the reference implementation's scores.
fssimu2 is still missing some functionality (like ICC profile reading) but the goal was to produce a production-oriented implementation that is just as useful while being much faster (example: lower memory footprint and speed improvements make fssimu2 a lot more useful in a target quality loop). For research-oriented use cases where the exact SSIMULACRA2 score is desirable, the reference implementation is a better choice. It is worth evaluating whether or not this is your use case; an implementation that is 99.97% accurate is likely just as useful to you if you are doing quality benchmarks, target quality, or something else where SSIMULACRA2's correlation to subjective human ratings is more important than the exactness of the implementation to the reference.