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Designing expressive sequence layers with efficient inference remains a central challenge in modern machine learning. Standard softmax attention achieves excellent sequence modeling performance through rich nonlinear token interactions, but it requires a key-value cache that grows linearly with sequence length, limitin...
Harbor Adapters are developed that port more than 80 benchmarks to evaluate arbitrary agents, and validated through rigorous code review and parity experiments, and Harbor-Index is introduced, a curated set of 82 difficult, diverse, and high-quality tasks spanning 29 benchmarks to support more reliable and comprehensiv...
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