Topic: HongWU: Hierarchical On-demand Cognitive Big Model with World Utility
Abstract: This talk introduces HongWU (Hierarchical On-demand Machine-Cognitive Model with World Utility), a unified cognitive framework designed to address fundamental bottlenecks facing contemporary large-scale models: training data depletion, insufficient alignment with human intent, and inadequate grounding in physical systems. The HongWU framework integrates physical models, multi-source data, and intelligent tools into a unified tool matrix orchestrated by the foundation model. Through parameter efficient Fine-tuning and Human-in-the-loop feedback, the model dynamically aligns its objectives with human needs. Its federated knowledge engine, multi-level spatiotemporal reasoning, and multi-agent workflow ensure physically consistent reasoning and enable scalable management of complex engineering systems.
Short Bios: Professor Min Chen is a Professor and Doctoral Supervisor at the School of Computer Science, South China University of Technology. He is an IEEE Fellow, IET Fellow, and AAA Fellow, serving as Chief Scientist of a National Key Research and Development Programme. Professor Chen has been named a Clarivate Highly Cited Researcher for eight consecutive years (2018‒2025). With over 56,000 citations on Google Scholar and an H-index of 104, his academic influence is globally recognized. He has published more than 200 papers in top venues including Science, Nature Communications, and CCF Class A conferences, with 34 ESI Highly Cited Papers and a single paper cited over 6,060 times. He got IEEE ICC Best Paper Award in 2012, IEEE Communications Society Fred W. Ellersick Prize in 2017, the IEEE Jack Neubauer Memorial Award in 2019, and IEEE ComSoc APB Oustanding Paper Award in 2022. His research focuses on cognitive computing, Large Language Model, big data analytics, Embodied AI, and edge intelligence, etc.