10 papers
Modular TTT: Rethinking Test-Time Training as Composable Modules
Bohao Tang, Zhen Qin, Yuqi Pan +3
Test-time training (TTT) views sequence modeling as an online learning problem in which fast weights are updated by an internal learning rule. Despite the growing number of TTT var…
Beyond Transformers: Linear Attention Policy for Open-Vocabulary Object Goal Navigation
Jiahong Zhang, Yifan Lin, Yandong Zhang +6
Open-Vocabulary Object Goal Navigation (OVON) requires agents to operate under partial observability, making effective internal state updates critical for navigation performance. T…
A Systematic Analysis of Hybrid Linear Attention
Dustin Wang, Rui-Jie Zhu, Steven Abreu +9
Transformers face quadratic complexity and memory issues with long sequences, prompting the adoption of linear attention mechanisms using fixed-size hidden states. However, linear…
SVL: Empowering Spiking Neural Networks for Efficient 3D Open-World Understanding
Xuerui Qiu, Peixi Wu, Yaozhi Wen +5
Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. However, existing SNNs still exhibit a significant performance gap compared t…
SpikingBrain: Spiking Brain-inspired Large Models
Yuqi Pan, Yupeng Feng, Jinghao Zhuang +16
Mainstream Transformer-based large language models face major efficiency bottlenecks: training computation scales quadratically with sequence length, and inference memory grows lin…
SpikingBrain2.0: Brain-Inspired Foundation Models for Efficient Long-Context and Cross-Platform Inference
Yuqi Pan, Jinghao Zhuang, Yupeng Feng +16
Scaling context length is reshaping large-model development, yet full-attention Transformers suffer from prohibitive computation and inference bottlenecks at long sequences. A key…