Event arc
Handling extremely long context windows can enhance AI model performance and scalability.
Cluster
Loading the signal cluster
Collecting the cluster map, linked briefings, and market context.
AI BriefWire / Thread
What comes after attention? This startup says it already knows.
Subquadratic, a startup, has developed a sparse-attention model capable of handling a 12-million token context window. This innovation suggests a new approach beyond traditional attention mechanisms in AI. It could significantly improve processing of large-scale data in language models.
The New Stack AI
Signal trust
High-signal sourceSingle sourceEarly signal
Companies involved
No clear public-company linkage yet. This thread is still useful as a thematic signal.
Market lens
This technology could enable more powerful and efficient AI applications across industries.
Operator take
AI developers should explore sparse-attention models for large context tasks.
Source mix
Sources in this thread (1): The New Stack AI
How the thread developed
Read the development of the event across sources, timestamps, and editorial cues.
Latest signal
What comes after attention? This startup says it already knows.
Subquadratic, a startup, has developed a sparse-attention model capable of handling a 12-million token context window. This innovation suggests a new approach beyond traditional attention mechanisms in AI. It could significantly improve processing of large-scale data in language models.
Stories in this cluster
Open individual briefings or jump to the original reporting.
What comes after attention? This startup says it already knows.
Subquadratic, a startup, has developed a sparse-attention model capable of handling a 12-million token context window. This innovation suggests a new approach beyond traditional attention mechanisms in AI. It could significantly improve processing of large-scale data in language models.