Making Science Programmable.
Dynamical Systems turns physical science into trainable experience.
We make every experiment verifiable, replayable, and compounding.
Research
Can a Self-Driving-Lab Agent Tell When the Evidence Is Enough?
We turn the historical record of a lab into source-located replay tasks that measure evidence-boundary judgment before a self-driving lab is trusted to run on its own. Across six frontier models and 1,872 trajectories, agents reach a valid decision on 90% of runs and the reference-equivalent path on 72%, and no model clears the benchmark.
Scaling Test-Time Verification for Novel Materials
Crystal diffusion models encode property signals in their hidden states that they never use during sampling. Probe-gradient guidance steers unconditional generation toward target properties at test time, comparable to conditional models at over 50x the speed.
The Missing Layer in Autonomous Science
Multi-turn RL in verified campaign environments lifts hypothesis accuracy from 55.2% to 79.3% on held-out campaigns, surpassing GPT-5.4 with 3B active parameters.