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PISA is proposed, a block-sparse attention mechanism that employs a pyramid Top-$K selection strategy, and develops hardware-aware Triton kernels for both training and inference, fusing hierarchical routing and LogSumExp scoring without materializing the query-key score matrix.
Modular TTT is proposed, a framework that represents the inner learner as a directed acyclic graph and exposes the fast-weight network, loss function, learning rate, weight decay, and normalization as explicit design dimensions and finds that small learning-rate initialization, weight decay, and a single-layer nonlinea...
LoGo, a token-level dynamic local-global attention mechanism that uses attention span as a direct proxy for attention budget allocation, is proposed and results suggest that learned token-level span allocation is an effective and scalable way to improve the long-context performance-compute trade-off.
Frozen RNA-type evaluations show that RIBOSPAN learns state-of-the-art RNA representations, with a particularly clear advantage on long RNAs, and emerges as the strongest encoder-only RNA foundation model, achieving state-of-the-art performance in both full-transcript biological property prediction and zero-shot mutati...
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