2026 3rd International Conference on Artificial Intelligence and Natural Language Processing (AINLP 2026)

Prof. Yang Yang (IEEE Fellow)

The Hong Kong University of Science and Technology, China

Professor Yang Yang is currently the Director of Shanghai Center, The Hong Kong University of Science and Technology (HKUST), China. He is also an adjunct professor with the Department of Broadband Communication at Peng Cheng Laboratory, and the Chief Scientist of IoT at Terminus Group, China. Yang's research interests include multi-tier computing networks, 5G/6G systems, AIoT technologies and applications, and advanced wireless testbeds. He has published more than 380 papers and filed more than 120 technical patents in these research areas. He is a fellow of the IEEE.

Title: NASCA: Network Agentic Service Customization Architecture

Abstract: In wireless networks, the inherent conflict between limited computational resources and high-intensity computing workloads makes it extremely challenging to deploy complex tasks based on large AI models in edge devices. Traditional methods rely on aggregating data and transmitting it to cloud platforms for centralized processing; however, this approach is unsustainable as it strains long-distance backhaul transmission and energy-intensive data centers, while also failing to guarantee data security and personal privacy. To address these challenges, we propose the Network Agentic Service Customization Architecture (NASCA) for supporting personalized services. By leveraging the collaborative functions among distributed edge devices, we achieve fast, low-cost, and sustainable edge computing for large AI models. We analyze the engineering issues involved in the coordinated scheduling of multi-element resources—such as communication, sensing, computing, and storage—among edge devices, and verify the feasibility and service efficiency of different parallel processing schemes for large AI models.

Prof. Yunwen Chen

DataGrand Co., Ltd., China

Chen Yunwen, Chairman of Daguan Data. He holds a PhD in Computer Science from Fudan University and is an expert under the national ‘Ten Thousand Talents Programme’, a recipient of the State Council’s Special Allowance, one of the first experts to be awarded the senior-most professional title in artificial intelligence, and a Distinguished Member of the Chinese Computer Society. He has applied for nearly a hundred national technical invention patents and has received honours including the ACM International Data Mining Competition championship and the Wu Wenjun Artificial Intelligence Award.

Prof. Hai Zhao

Shanghai Jiao Tong University, China

Hai Zhao is a Tenured Professor and Ph.D. Supervisor at the School of Computer Science and Engineering, Shanghai Jiao Tong University (SJTU), where he also serves as Director of the Institute of Artificial General Intelligence (IAGI). His research focuses on natural language processing (NLP) and foundational deep learning methodologies.

He has authored over 200 academic publications, including more than 100 papers in CCF A/B-tier conferences and 20+ papers in Chinese Academy of Sciences (CAS) Q1 SCI-indexed journals, among which four appear in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). His work has garnered over 15,000 Google Scholar citations. He currently serves as a Member of the Technical Committee on Chinese Information Processing under the China Computer Federation (CCF), and Deputy Director of the Artificial Intelligence Technical Committee of the Shanghai Computer Society.

In service to the academic community, he held the role of Senior Area Chair for the Linguistic Analysis, Morphology, and Word Segmentation tracks at ACL 2017–2019, acts as Executive Editor for both the ACL Rolling Review (ARR) and Transactions of the Association for Computational Linguistics (TACL), and has served as (Senior) Program Committee Member for AAAI, IJCAI, and NeurIPS in recent years. He has led top-performing systems on major international NLP benchmarks including RACE, SQuAD 2.0, HotpotQA, and HellaSwag, with his team being the first to develop an NLU system that surpasses human performance on several of these tasks.

In 2023, he published Natural Language Understandingwith Tsinghua University Press—the first Chinese textbook-monograph hybrid focused on large language models. He leads the development of the BatGPT series of LLMs, which have been deployed in vertical industrial applications and adopted by multiple national agencies; notably, he built Shanghai’s first government-procured LLM application system. He also pioneered BriLLM, the first brain-inspired large language model to depart from conventional machine learning paradigms. Named an Elsevier Highly Cited Researcher for four consecutive years (2022–2025).

Title: Scaling Law, A Fluke, Not a Path: A Mathematical Theory of AGI and its Brain-inspired LLM Exploration

Abstract: Current AGI research is fragmented across capability-, mechanism-, and endogeneity-focused paradigms, with most industrial roadmaps over-relying on unsupervised scaling of large language models (LLMs)—a path lacking rigorous mathematical grounding. We demonstrate that pure symbolic LLM systems face three insurmountable barriers: a topological gap preventing discrete symbols from capturing high-dimensional continuous physical manifolds, a causal gap blocking progression beyond Pearl’s associational tier using observational data alone, and a thermodynamic gap rendering perfect prediction physically impossible under Bekenstein and Bremermann limits. Scaling also faces structural economic collapse: marginal performance gains decay asymptotically as training costs grow superlinearly.

To resolve these limitations, we derive necessary and sufficient conditions for computable AGI: an endogenous compression-driven self-consistency loop enabling autonomous convergence without external input, and exogenous physical grounding via perception-action coupling that aligns internal fixed points to real-world states. We further prove the mandatory existence of a MetaPred self-referential prediction module supporting infinite recursive metacognition, with direct empirical alignments to cortical semantic mapping and neural oscillation mechanisms in biological brains.

For practical deployment, we propose the Signal-Fully-connected Flow (SiFu) non-representational learning paradigm and the BriLLM-MetaPred architecture, which delivers brain-inspired AGI capabilities on commodity GPU hardware without relying on spike neural network infrastructure. This work provides the first mathematically falsifiable framework for AGI development, moving beyond heuristic scaling toward physically grounded, sustainable intelligence.

Prof. Fenghua Huang

Yango University, China

Huang Fenghua (born 1982), Professor, Ph.D. (post-doctoral), High-Level Talent of Fujian Province (Class B), Outstanding Teacher of Fujian Province, Dean of the School of Artificial Intelligence and Dean of the Institute of Intelligent Engineering Technology at Yango University, Head of the National First-Class Undergraduate Program in "Computer Science and Technology," Director of the Fujian Provincial Key Laboratory of Spatial Information Perception and Intelligent Processing, Director of the Fujian University Engineering Research Center for Spatial Data Mining and Application, Visiting Scholar at the University of North Carolina at Chapel Hill, Fujian Provincial Science and Technology Commissioner for the 2022–2026 term (Team Initiator), Distinguished Professor under the "Yango Scholar" program, Master's Supervisor at Fuzhou University for both Computer Technology and Surveying Engineering programs, Senior Member of the Institute of Electrical and Electronics Engineers (IEEE Senior Member), and Council Member of the Fujian Association for Artificial Intelligence. He currently serves concurrently as Vice Chairman of the Big Data Education Alliance (Fujian) and Adjunct Researcher at the Monash University Suzhou Science and Technology Research Institute in Australia. He was previously selected for the Fujian Province ABC High-Level Talent Program, the Fujian Provincial University New Century Outstanding Talents Support Program, the Fujian Provincial University Outstanding Young Scientific Research Talents Cultivation Program, and the Fujian Provincial Overseas High-Level Visiting Scholar Program for Outstanding Academic Leaders in Undergraduate Universities. He has served as General Chair for 7 sessions of well-known international academic conferences and as Guest Editor and Peer Reviewer for several SCI-indexed international journals.

His primary research interests include data mining, machine learning, and remote sensing image processing. Over the past five years, he has led more than 20 vertical research projects at the national, provincial, and municipal levels, as well as over 10 horizontal enterprise-funded projects. He has published more than 50 high-quality academic papers, been granted 20 national patents and over 10 software copyrights, and authored or co-authored 5 monographs and textbooks.