The framework is particularly suited to three types of academic research scenarios:
- Automation of Literature ResearchInput the command "Find the latest CVPR papers", and the intelligent body will automatically complete the complete process of arXiv search, PDF download, key information extraction, and abstract generation, which is a significant increase in efficiency compared with traditional manual operation.
- Data collation and analysis: Researchers can upload CSV files of experimental data and obtain the analysis results directly through natural language commands (e.g., "Calculate standard deviation and plot trend"), avoiding the need to rewrite pandas code.
- A cross-modal studyThe AI Lab's AI benchmarks are based on the GAIA benchmarks for academic tasks, including: processing image/graph data from papers when combined with multimodal extensions, e.g., extracting graphs from PDFs and generating descriptive text. Tests by Tencent AI Lab show an accuracy of 82.3% on academic tasks on the GAIA benchmark.
All functions are realized based on open source models, avoiding the risk of sensitive data leakage through third-party APIs.
This answer comes from the articleCognitive Kernel-Pro: a framework for building open source deep research intelligencesThe
May not be reproduced without permission:AI productivity tools " What are some typical scenarios where Cognitive Kernel-Pro is used in academic research?
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