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cs.CL2025
Method Decoration (DeMe): A Framework for LLM-Driven Adaptive Method Generation in Dynamic IoT Environments
Hong Su
Intelligent IoT systems increasingly rely on large language models (LLMs) to generate task-execution methods for dynamic environments. However, existing approaches lack the ability…
cs.AI2025
Active Thinking Model: A Goal-Directed Self-Improving Framework for Real-World Adaptive Intelligence
Hong Su
Real-world artificial intelligence (AI) systems are increasingly required to operate autonomously in dynamic, uncertain, and continuously changing environments. However, most exist…
cs.CE2025
Difference-Guided Reasoning: A Temporal-Spatial Framework for Large Language Models
Hong Su
Large Language Models (LLMs) are important tools for reasoning and problem-solving, while they often operate passively, answering questions without actively discovering new ones. T…