The lab is a workflow, not a prompt
Scientific work unfolds across notes, instruments, protocols, and team discussion. A useful AI system must fit into that chain of evidence rather than pretend the entire task can be solved in a single exchange.
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Research
Designing scientific copilot workflows for real lab work
This paper examines how AI systems can support lab planning, literature synthesis, and experimental note-taking without disrupting how researchers already work.
Oct 22, 2025·6 min read
Traceability matters
Researchers told us they trusted the system more when it showed where a suggestion came from, what assumptions it made, and what still required verification. That insight shaped our workflow design more than any single model metric.
A human-centered model of speed
Faster science does not mean skipping review. It means reducing the time spent on repeated setup, fragmented search, and format conversion so expert attention stays on the real decision points.
Related research
A few related reads to continue exploring the MAPLE-GLOBAL ecosystem.
Research
System card for MAPLE Reasoning 1
A practical overview of capabilities, failure modes, and deployment safeguards for our reasoning model.
Dec 5, 2025·8 min read
Research
Training specialist models on domain-rich corpora
A playbook for balancing breadth, precision, and retrieval when models learn from deep specialist material.
Nov 12, 2025·7 min read