1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.NE2026
Large Language Models with At Most One Spike per Neuron
Zhuoya Zhao, Parsa Omidi, Aref Jafari +1
Leveraging their inherent sparse event-driven computation, spiking neural networks (SNNs) offer a promising path toward energy-efficient large language models (LLMs). Time-to-first…
cs.CL2026
Hierarchical Chain-of-Thought: Enhancing LLM Reasoning Performance and Efficiency
Xingshuai Huang, Derek Li, Bahareh Nikpour +1
Chain-of-Thought (CoT) prompting has significantly improved the reasoning capabilities of large language models (LLMs). However, conventional CoT often relies on unstructured, flat…
cs.LG2025★ 1 cited
Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Enhanced Model Architectures
Parsa Omidi, Xingshuai Huang, Axel Laborieux +3
Memory is fundamental to intelligence, enabling learning, reasoning, and adaptability across biological and artificial systems. While Transformer architectures excel at sequence mo…