2 papers
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
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…