4 accepted papers
Chain-of-Thought (CoT) prompting improves large language models (LLMs) on difficult reasoning tasks, but it generates long natural-language rationales that are poorly optimized towards higher-level machine efficiency and intelligence. We propose *Communicative Language Symbolism Routing* (CLSR), a t…
Neural models are equivalent to dynamic systems from a physics-inspired view, implying that computation on neural networks can be interpreted as the dynamical interactions between neurons. However, existing work models neuronal interaction as a weight-based linear transformation, and the nonlinearit…
Recent advances in natural language processing have relied heavily on using Transformer-based language models. However, Transformers often require large parameter sizes and model depth. Existing Transformer-free approaches using state-space models demonstrate superiority over Transformers, yet they…
This paper investigates the dynamics-inspired neuromorphic architecture for visual representation learning following Hamilton's principle. Our method converts weight-based neural structure to its dynamics-based form that consists of finite sub-models, whose mutual relations measured by computing pat…