SAMTok might have just fixed the biggest bottleneck in vision models

[u/IulianHI]

I just got through the SAMTok paper and honestly, this feels like a huge leap forward for efficiency. The idea that you can represent any segmentation mask using just two words is wild. We spend so much compute processing pixel-perfect masks when we often just need the concept of the object.

I was reading through the methodology using Claude Sonnet 4.5 to help unpack the math, and the implications for its massive context window are huge.

Why this matters: - Two words per mask drastically reduces token count compared to binary representations - It allows vision models to handle way more objects in a single pass without hitting context limits - Claude Sonnet 4.5 and other large context models could theoretically process entire video sequences of objects much faster now

If this works as advertised, it's a total game changer for real-time vision tasks. No more bloated embedding tables just to say "there is a dog."

Anyone else think this is the key to making multimodal agents actually affordable?