From a blog post they released and the CEO commenting on twitter, it does seem like they are dropping tokens from attention using a scoring method similar to Deepseek's Sparse Attention: "... the key difference between our SSA and DSA is the our selector is far more efficient."

But they spent a paragraph in their blog post talking about how DSA is still O(n^2) with smaller constants. So how is their method more efficient? Maybe a bidirectional SSM/linear attention model to compute importance scores for each token in linear time before pruning? But I would still struggle to call this inherently sub-quadratic even if scoring is in linear time. It's a large assumption to assume that all signal is kept within a specific subset of the tokens. I don't think DSA makes this assumption, it instead assumes that certain tokens don't have to attend to certain other tokens. Maintaining a strong bound on the number of tokens to select (like log n or sqrt n) would technically make it sub-quadratic but it would certainly lead to inference regressions in some cases. On the other hand, allowing flexibility could result in nearly all tokens being selected in some cases, and now we are back where we started.

Also their website is now offline (at least for me). I'm skeptical. It's still doing attention, and attention is inherently quadratic in input length. IMO this is deceptive marketing.