Agents meet OKR: an object and key results driven agent system with hierarchical self-collaboration and self-evaluation
In this study, we introduce the concept of OKR-Agent designed to enhance the capabilities of
Large Language Models (LLMs) in task-solving. Our approach utilizes both self …
Large Language Models (LLMs) in task-solving. Our approach utilizes both self …
Knowledge graph enhanced recommendation for semantic structure construction and feedback in online learning
W Wu, S Yang, F Tian, X Wei, Y Pan - Physical Communication, 2025 - Elsevier
This paper presents a knowledge graph-enhanced recommendation system, inspired by
constructivist principles, to address challenges like information overload and fragmented …
constructivist principles, to address challenges like information overload and fragmented …
Unveiling Interactive Narratives: Enhancing MOOCs with Interaction Fiction
Abstract Massive Open Online Courses have become increasingly popular in the last few
years. However, they are often characterized by high dropout rates. This paper presents our …
years. However, they are often characterized by high dropout rates. This paper presents our …
Explicit-Constrained Single Agent for Enhanced Task-Solving in LLMs
In this study, we introduce the Explicitly Constrained Agent (EC-Agent), a novel approach
designed to enhance the task-solving capabilities of Large Language Models (LLMs). Unlike …
designed to enhance the task-solving capabilities of Large Language Models (LLMs). Unlike …
OKR-Agent: An Object and Key Results Driven Agent System with Hierarchical Self-Collaboration and Self-Evaluation
In this study, we introduce the concept of OKR-Agent designed to enhance the capabilities of
Large Language Models (LLMs) in task-solving. Our approach utilizes both self …
Large Language Models (LLMs) in task-solving. Our approach utilizes both self …