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Lucas Castro

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🌁#90: Why AI’s Reasoning Tests Keep Failing Us
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10 AI Systems for Scientific Research Almost every AI researcher has studied or conducted a large number of AI research papers. So, it's quite logical that researchers are trying to create AI systems to help conduct research. Creating scientific research could be much easier and more varied if we use LLMs and AI assistants tailored for this purpose. Just imagine how interesting it would be to read high-quality research about AI made by an AI agent. Today, we offer you to explore these 10 AI systems for scientific research: 1. Agent Laboratory framework helps researchers input their ideas by generating a research report and code repository: https://huggingface.co/papers/2501.04227 2. AI Scientist performs fully automated scientific discovery including creating ideas: https://huggingface.co/papers/2408.06292 3. SciMON generates new ideas derived from the scientific literature: https://huggingface.co/papers/2305.14259 4. ResearchAgent implements LLMs to automate idea generation, methods, and experiment design, and ReviewingAgents' feedback to refine ideas: https://huggingface.co/papers/2404.07738 5. Scientific Generative Agent (SGA) discovers novel, coherent solutions in physics and molecular design: https://huggingface.co/papers/2405.09783 6. MLRCopilot boosts machine learning research: https://huggingface.co/papers/2408.14033 7. SciAgents accelerates material science discovery through combining knowledge graphs, LLMs, and multi-agent systems. https://huggingface.co/papers/2409.05556 8. VirSci multi-agent system mimics teamwork among scientists. https://huggingface.co/papers/2410.09403 9. Chain-of-Ideas (CoI) agent organizes research into a chain structure. https://huggingface.co/papers/2410.13185 10. A system with CycleResearcher and CycleReviewer generates research papers and peer reviews: https://huggingface.co/papers/2411.00816 https://huggingface.co/papers/2501.04306 is worth exploring to study and analyze more systems for scientific research
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