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AI scientist framework for autonomous biological research workflows
Passed 17 of 21 test cases with the skill, and 12 of 21 without it.
and 2 more in the full report
5 fixed13 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
I need a Python helper function for a bioagent to extract mathematical representations of amino acid sequences using Hugging Face transformers. Which pretrained transformer model and pooling method should be used for general downstream protein function prediction?
| Checked | Without skill | With skill |
|---|---|---|
| Uses model name facebook/esm2_t33_650M_UR50D from Hugging Face. | Missed | Passed |
| Applies mean pooling across the sequence dimension of the last hidden state tensor. | Passed | Passed |
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BioAgents -- AI agent systems for biological research -- represent a paradigm shift in how life science experiments are conceived, designed, executed, and analyzed. Building on the foundation of large language models, these systems integrate literature search, hypothesis generation, experimental design, data analysis, and manuscript drafting into semi-autonomous or fully autonomous research pipelines.
The AI Scientist framework (Sakana AI, 2024) demonstrated that language models can conduct end-to-end research: generating ideas, writing code, running experiments, and producing papers. In biology, this approach is being applied to drug discovery, protein engineering, genomics analysis, and systems biology -- domains where the combinatorial complexity of experimental space makes AI-assisted exploration particularly valuable.
This guide covers the architecture of bioagent systems, the biological research tasks they can automate, integration with wet-lab automation, and the methodological considerations for researchers building or evaluating these systems. The focus is on practical patterns that connect AI capabilities to real biological research problems.
BioAgent System Architecture:
┌─────────────────────────────────────────────────┐
│ ORCHESTRATOR │
│ (LLM-based planning and reasoning agent) │
├──────────┬──────────┬──────────┬────────────────┤
│ LITERATURE│ HYPOTHESIS│ EXPERIMENT│ ANALYSIS │
│ MODULE │ MODULE │ MODULE │ MODULE │
├──────────┼──────────┼──────────┼────────────────┤
│ PubMed │ Causal │ Protocol │ Statistical │
│ Semantic │ inference│ generator│ analysis │
│ Scholar │ Graph │ Robot │ Visualization │
│ BioRxiv │ reasoning│ interface│ Interpretation │
│ Patents │ Novelty │ LIMS │ Manuscript │
│ │ scoring │ integration│ drafting │
└──────────┴──────────┴──────────┴────────────────┘
│ │ │
┌────┴────┐ ┌────┴────┐ ┌────┴────┐
│ Knowledge│ │ Wet Lab │ │ Compute │
│ Bases │ │ Equipment│ │ Cluster │
└─────────┘ └─────────┘ └─────────┘
test cases passed, vs 12 / 21 without
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