Wei Zhang

Sohan Shankar, Yi Pan, Hanqi Jiang et al.

This position and survey paper identifies the emerging convergence of neuroscience, artificial general intelligence (AGI), and neuromorphic computing toward a unified research paradigm. Using a framework grounded in brain physiology, we highlight how synaptic plasticity, sparse spike-based communication, and multimodal association provide design principles for next-generation AGI systems that potentially combine both human and machine intelligences. The review traces this evolution from early connectionist models to state-of-the-art large language models, demonstrating how key innovations like transformer attention, foundation-model pre-training, and multi-agent architectures mirror neurobiological processes like cortical mechanisms, working memory, and episodic consolidation. We then discuss emerging physical substrates capable of breaking the von Neumann bottleneck to achieve brain-scale efficiency in silicon: memristive crossbars, in-memory compute arrays, and emerging quantum and photonic devices. There are four critical challenges at this intersection: 1) integrating spiking dynamics with foundation models, 2) maintaining lifelong plasticity without catastrophic forgetting, 3) unifying language with sensorimotor learning in embodied agents, and 4) enforcing ethical safeguards in advanced neuromorphic autonomous systems. This combined perspective across neuroscience, computation, and hardware offers an integrative agenda for in each of these fields.

Afrar Jahin, Arif Hassan Zidan, Wei Zhang et al.

With the rapid advancement of Artificial Intelligence (AI), Large Language Models (LLMs) have significantly impacted a wide array of domains, including healthcare, engineering, science, education, and mathematical reasoning. Among these, mathematical reasoning remains a particularly challenging capability, often requiring multi-step logic and abstract generalization. While prior work has explored LLM performance on reasoning tasks, comprehensive evaluations that span both depth and breadth across model families remain limited. In this study, we present a systematic evaluation of mathematical reasoning abilities across eight leading LLMs, including two recent DeepSeek models, using three independent benchmark datasets. Our analyses reveal several key findings: (1) DeepSeek-R1 performs competitively with o1 across most domains and achieves the highest accuracy on the MMLU Formal Logic benchmark; (2) distilled variants, such as DeepSeek-1.5B, exhibit substantial performance degradation; and (3) Gemini 2.0 Flash achieves the lowest response latency. Beyond quantitative metrics, we explore how architectural choices, training paradigms, and optimization strategies contribute to variation in reasoning performance. These findings provide new insights into the capabilities and limitations of current LLMs in mathematical domains, and offer guidance for the development of future models better aligned with rigorous reasoning demands.