SciDER: Scientific Data-centric End-to-end Researcher
Automated scientific discovery with large language models is transforming the research lifecycle from ideation to experimentation, yet existing agents struggle to autonomously process raw data collected from scientific experiments. We introduce SciDER, a data-centric end-to-end system that automates the research lifecycle. Unlike traditional frameworks, our specialized agents collaboratively parse and analyze raw scientific data, generate hypotheses and experimental designs grounded in specific data characteristics, and write and execute corresponding code. Evaluation on three benchmarks shows SciDER excels in specialized data-driven scientific discovery and outperforms general-purpose agents and state-of-the-art models through its self-evolving memory and critic-led feedback loop. Distributed as a modular Python package, we also provide easy-to-use PyPI packages with a lightweight web interface to accelerate autonomous, data-driven research and aim to be accessible to all researchers and developers.
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Ke Lin
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2026-03-04T09:23:17.286Z
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Ke Lin
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Yilin Lu
claimed_verified
2026-03-24T09:23:19.584Z
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Harry Lu
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user
harry-lu-0708
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Shreyas Bhat
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Xuehang Guo
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Junier Oliva
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Qingyun Wang
claimed_verified
2026-03-05T09:59:14.435Z
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Qingyun Wang
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user
eaglew
SciDER: Scientific Data-centric End-to-end Researcher
Automated scientific discovery with large language models is transforming the research lifecycle from ideation to experimentation, yet existing agents struggle to autonomously process raw data collected from scientific experiments. We introduce SciDER, a data-centric end-to-end system that automates the research lifecycle. Unlike traditional frameworks, our specialized agents collaboratively parse and analyze raw scientific data, generate hypotheses and experimental designs grounded in specific data characteristics, and write and execute corresponding code. Evaluation on three benchmarks shows SciDER excels in specialized data-driven scientific discovery and outperforms general-purpose agents and state-of-the-art models through its self-evolving memory and critic-led feedback loop. Distributed as a modular Python package, we also provide easy-to-use PyPI packages with a lightweight web interface to accelerate autonomous, data-driven research and aim to be accessible to all researchers and developers.
SciDER automates scientific research by processing raw experimental data through collaborative agents that generate hypotheses and experimental designs while executing code, demonstrating superior performance in data-driven discovery compared to general-purpose models.
large language models automated scientific discovery research lifecycle data-centric system end-to-end system collaborative agents hypothesis generation experimental design code execution self-evolving memory critic-led feedback loop PyPI packages web interface