Autonomous R&D
Research that keeps learning between runs.
What does R&D need to run on its own?
Observe, model, act, verify, learn — with the reasoning and the memory outside the model, so every cycle starts from everything the last ones established, and every decision the system makes can be audited and replayed.
Automation executes a plan a person made. Autonomous R&D chooses the next experiment itself, judges the outcome and updates what it knows — which only works if what it knows is kept explicitly and grows with every cycle.
Autonomous R&D, in four points
A model of what is known
Hypotheses, results, failures and their conditions are kept as explicit claims tied to the experiments that support or contradict them.
Choosing the next action
The next experiment is chosen by how much it would change what is known and what it costs, then run by agents, instruments or robots.
Deterministic judgement
Each outcome is checked against the model and the evidence the same way every time, so the loop does not drift.
Reasoning as the artifact
Every decision is kept as replayable reasoning tied to the version of the model it came from: audit, reproduction and handover without reconstruction.
Where it applies
Autonomous R&D that remembers what it learned
Self-driving lab cycles, process optimisation and robotics, where each decision is made from everything the earlier cycles established — including their failures.
Read the article →In autonomous R&D, the reasoning is the artifact
Decisions kept as replayable reasoning: audit without reconstruction, handover between teams and partners, and regulated R&D that needs reproducible records.
Read the article →GitMir publishes what its work produces and how it is measured. It does not publish the representation or the algorithms behind this line. This is work in progress: when there is a result worth reporting, it will appear here as a measurement, with method, scope and limitations.