Propose
Explore different ways to answer the same research question.
- Try a new formula, experiment, or change to the code
- Explain what changed and why it might help
- Save the starting point so each idea can be compared fairly
An autonomous, self-improving lab for frontier science and engineering.
Mutome tackles open-ended problems in mathematics, physics, engineering, and technical R&D by running many candidate research routes in parallel. AI models and search heuristics explore broad spaces inspired by evolutionary algorithms, while every promising protocol, code change, proof route, or parameter setting becomes a replayable run with metrics, artifacts, and recorded decisions.
Mutome turns an uncertain research candidate into a measured run.
A candidate can be a protocol, patch, proof strategy, parameter setting, simulation setup, or experiment variant. Mutome records what was tried, how it was evaluated, which artifacts were produced, and why the candidate was kept, hardened, or discarded.
Discovery stays broad: models, search heuristics, and researchers can propose many plausible routes across the search space.
Verification stays narrow: a candidate advances only when fixed tests, benchmark scripts, solvers, proof checkers, or reviewers reproduce the claim, leaving a command, metric or check, artifacts, and an evidence trail another researcher can inspect.
Randomnessin discovery.
Determinismin verification.
Three steps. Many loops.
Mutome explores ideas, tests them, and uses the results to decide what to try next. Each attempt is saved so others can understand what was done and repeat the test.
Verify
Test each idea to find out whether it actually works.
- Decide what counts as an improvement before running the test
- Compare the result with the starting point and a simpler approach
- Keep the test instructions and results so the check can be repeated
Iterate
Use what the tests reveal to choose what to try next.
- Build on ideas that passed the checks
- Keep failed attempts so the next search can learn from them
- Save the steps and reasons behind each decision
See what has actually been checked.
A promising idea, a repeatable result, and a checked proof are different things. These labels show what evidence supports each result.
Idea recorded
The idea and its purpose have been saved. This tells us what to test, not whether it works.
Result repeated
Running the saved test again produces the measured result. The instructions are kept so the check can be repeated.
Checked with a separate tool
A separate test, simulation, or verification tool checks the specific claim. Its conclusion applies to what it tested.
Proof checked by software
For claims that can be expressed mathematically, a proof checker verifies the submitted proof against its stated assumptions.
Reviewed by a researcher
A researcher examines the evidence, sources, and limitations to assess what the result supports and what still needs testing.