Results
How much of the required physical workflow does each agent recover?
Mean score across our held-out subset
- GPT 6.1 SolCodex46.7%
- Opus 5.5Claude Code23.0%
- Gemini 4 ArgonGemini CLI20.9%
Finishing an interrupted experiment
A robot stopped halfway through an experiment involving four trays of cells. Three could still be tested. The agent needed to finish preparing them, measure the results, and put them away. The fourth was already spoiled and needed to be set aside.
What the agent needed to do
Each tray was at a different step when the robot stopped. The agent had to work out which steps were already done, then finish washing the cells, add a chemical used to measure cell activity, and run the measuring instrument. It also needed to save the results and return each tray with its lid. Repeating a chemical treatment could ruin a sample.
Why it matters in a wet lab
A tray can be safely back on its shelf while the experiment is still unfinished. The important question is whether the cells received the right treatment and were measured in time.
Opus puts an unfinished assay back on the shelf.
Watch Opus put the yellow-labeled tray back on the shelf. Its cells have been washed, but the agent never adds the chemical needed for the test or measures the result. The tray is put away; the experiment is left unfinished.
The cost of choosing what to save first
The agent had several jobs competing for its attention: finish two experiments, set aside one spoiled sample, return two trays of supplies to cold storage, and leave a comparison sample untouched. Some of these jobs had only a few minutes left.
What the agent needed to do
One experiment needed its next chemical addition finished within 200 seconds. The cold supplies could spend no more than 240 seconds outside storage. Because the same robot moved all the trays, the agent had to choose what to handle first.
Why it matters in a wet lab
In a lab, helping one sample can mean leaving another waiting. An agent needs to keep track of both deadlines and choose an order that protects the materials.
GPT gets both trays of cold supplies back in time but fails to finish one experiment. Opus finishes both experiments, but leaves both trays of supplies out too long.
Cold supplies return to storage too late.
Watch Gemini carry the red-labeled tray to the cold-storage machine. By the time the tray is stored inside, it has spent about 422 seconds outside—well past the 240-second limit. The robot moves it successfully, but too late to satisfy the task.
A completed read can still be too late
This experiment tests how cells respond to different amounts of a chemical. The agent needed to finish testing two trays of cells and set aside a third that was already spoiled. One tray was close to its measurement deadline.
What the agent needed to do
The agent was expected to finish an experiment within a fixed timeframe. After the cells were treated with the test chemical, it had 30 minutes to complete a measurement of their response. It needed to check which steps were already done, finish preparing the samples, and leave enough time for the measuring instrument to run.
Why it matters in a wet lab
Timing requirements like this are common in live biology experiments, where a sample’s response can change after treatment. Taking measurements at the required time helps scientists compare results across samples. The agent therefore needs to plan around the experiment’s timing, as well as operate the equipment.
GPT collects a result that missed the deadline.
Watch GPT take the tray out of the measuring instrument. The machine has finished, but its measurement ended 42.5 seconds after the deadline. The robot carries on normally, and the agent later reports the test as completed.
Build the future of experimental biology.
At Gamow Labs, we work at the frontier of AI and wet-lab biology to better understand rare diseases. We’re building agents that can operate robotic laboratories and help scientists uncover how genetic changes cause disease.
If you’re developing frontier models, building laboratory automation, or looking to expand your experimental capabilities, we’d love to work with you.