SMat-Attention: Structured Long-Context Sequence Modeling
arXiv:2609.36062v1 Announce Type: new Abstract: Long-context sequence models face a fundamental tradeoff: softmax attention uses flexible token-level interactions at quadratic cost, whereas linear attention obtains linear-time training and constant-time decoding by compressing…
- 01.arXiv:2609.36062v1 Announce Type: new Abstract: Long-context sequence models face a fundamental tradeoff: softmax attention uses flexible token-level interactions at quadratic cost, whereas linear attention obtains linear-time training and constant-time decoding by compressing…
This material is provided as an extended analytical summary.
Read full article at sourceRelated Intelligence
All stories →Five weird details from Anthropic’s IPO filing
Anthropic has finally filed its S-1 prospectus with the SEC, a document designed to give investors a look under the hood of its business, from how much its executives are making and plans for spending its IPO billions to the risks that could derail its ambitions. At more than…
Anthropic’s IPO filing reveals its strategy for keeping enterprise customers hooked
Anthropic’s S-1 shows more than half of its $100,000-plus customers now use both the Claude Platform and a first-party app, a cross-selling playbook reminiscent of Amazon that’s making enterprise customers stickier.
FairStress: тест на демографическую справедливость моделей
📌 FairStress: тест на демографическую справедливость моделей Институт Аллена опубликовал набор FairStress для проверки того, меняет ли языковая модель своё решение о человеке из-за его расы, пола, религии, возраста, инвалидности или ориентации в сферах найма, кредитования,…