Native Hybrid Attention for Efficient Sequence Modeling Paper • 2510.07019 • Published 19 days ago • 16
CogniBench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language Models Paper • 2505.20767 • Published May 27 • 1
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models Paper • 2508.09834 • Published Aug 13 • 53
A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond Paper • 2503.21614 • Published Mar 27 • 42
CO2: Efficient Distributed Training with Full Communication-Computation Overlap Paper • 2401.16265 • Published Jan 29, 2024 • 1
Various Lengths, Constant Speed: Efficient Language Modeling with Lightning Attention Paper • 2405.17381 • Published May 27, 2024
LLaMA-MoE v2: Exploring Sparsity of LLaMA from Perspective of Mixture-of-Experts with Post-Training Paper • 2411.15708 • Published Nov 24, 2024
MiniMax-01: Scaling Foundation Models with Lightning Attention Paper • 2501.08313 • Published Jan 14 • 298
LASP-2: Rethinking Sequence Parallelism for Linear Attention and Its Hybrid Paper • 2502.07563 • Published Feb 11 • 24
Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models Paper • 2401.04658 • Published Jan 9, 2024 • 27