3 citations · 3 across the 5 of their papers we have counts for
5 papers
Event-Driven Language Models with Sparse Neural Activity for Neuromorphic Hardware
Simon Richter, Ruhai Lin, Jason Yik +4
Inference with transformer-based large language models (LLMs) is often limited by the memory-bound KV cache and quadratic attention cost. State-space models (SSMs) mitigate this th…
Scaling Latent Reasoning via Looped Language Models
Rui-Jie Zhu, Zixuan Wang, Kai Hua +30
Modern LLMs are trained to "think" primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training and under-leverages pre-training…
A Survey on Latent Reasoning
Rui-Jie Zhu, Tianhao Peng, Tianhao Cheng +30
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, especially when guided by explicit chain-of-thought (CoT) reasoning that verbalizes intermediate s…
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…
NeuroMorse: A Temporally Structured Dataset For Neuromorphic Computing
Ben Walters, Yeshwanth Bethi, Taylor Kergan +5
Neuromorphic engineering aims to advance computing by mimicking the brain's efficient processing, where data is encoded as asynchronous temporal events. This eliminates the need fo…