NovelSeek: When Agent Becomes the Scientist -- Building Closed-Loop System from Hypothesis to Verification

NOVELSEEK, an AI system, automates scientific research tasks, excelling in areas like AI4Science, sentiment, and image classification. It surpasses humans in yield prediction and 2D semantic segmentation, using A100 GPUs, gpt-4o, and claude-sonnet-3.7 models. Scalable, interactive, and expert-informed, it efficiently generates ideas, executes codes, and analyzes costs. Its performance is cost-effective, enhancing research automation. NOVELSEEK, an AI platform, innovates with ablation studies, improving methods in AI chemistry, molecular prediction, and reaction modeling. It surpasses AI-Scientist-V2 and AI-Researcher, generating methodologies, improving code accuracy, and refining experimental planning. It evaluates AI papers based on technical excellence, impact, and ethics, using React and a microservice governance system with a Service Mesh architecture for scalable, distributed cloud-native operations. GEARS_LocalRegularization combines local graph regularization with spectral penalties, enhancing biological knowledge integration. It introduces adaptive, multi-scale, and hierarchical regularization, featuring EENHPool and SRT-GT for advanced power flow estimation. HARCNet boosts image classification robustness through adaptive augmentation, temporal consistency, and feature group optimization, improving CIFAR-100 performance. NOVELSEEK offers user interface, task selection, idea visualization, and debugging features.