LabFactory: Building and Evaluating Executable AI Labs
cs.LG
Submitted: 2026-09-23
Updated: 2026-09-29
Code: https://github.com/JINGEWU/LabFactory
License: http://creativecommons.org/licenses/by/4.0/
The gist: Scientific tasks specify a desired capability, but realizing it often requires building a computational system tailored to the task---acquiring data, designing representations, training models,
Terminology
Abstract
Scientific tasks specify a desired capability, but realizing it often requires building a computational system tailored to the task---acquiring data, designing representations, training models, implementing tools, and deciding how they are used at inference. We present, a framework in which an AI builder turns a scientific brief into an executable AI lab: a task-specific solver that integrates models, knowledge resources, tools, and a controller behind a fixed interface. The builder develops and packages the lab in a metered workspace; a separate host then executes the delivered artifact on held-out inputs, with reference labels kept outside the solver's input interface, and scores its outputs under the task's protocol. This makes the delivered system, rather than the builder's account of its progress, the object of evaluation. We document 10 selected constructions across six scientific task categories---from molecular and genomic prediction to medical imaging, clinical decision support, and biomedical text---whose delivered labs exceeded their configured reference values on all 12 subtests under host-side execution. Four contain predictive models fitted during construction; the others assemble executable analysis environments, knowledge resources, and tool-driven workflows around a fixed platform LLM. Together they show that an AI agent can carry a scientific brief all the way to a working lab that can still be invoked, inspected, and checked after construction ends.
Sources
- MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks
- MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
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- Foundational Large Language Models for Materials Research
- BixBench: a Comprehensive Benchmark for LLM-based Agents in Computational Biology
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- Capabilities of GPT-4 on Medical Challenge Problems
- Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
- The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
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