Shared structure
Fit groups that have their own local coefficients while sharing a smaller global parameter set.
CONSTRAINED FITTING · ENGINEERING EVIDENCE
When convergence isn't enough.
Caldrivon specializes in fitting models with many local groups and a small set of shared parameters. For supported models, it can enforce coefficient bounds and produce checkable statements about optimality and rounding error.
A result is certified only when the stated conditions are proved. If the search budget is exhausted, the engine abstains rather than inventing certainty.
TECHNICAL NOTE / R176
REAL-DATA EVALUATION · 11 NOV 2026
40,223
public observations in three selected test subsets
01Household powerCERTIFIED
02Bikeshare tripsCERTIFIED
03Steel energyABSTAINED
A solver can report convergence. A certificate has to say what was proved.
THE WORKING PRINCIPLE01 / PUBLIC-DATA TRIAL
A real test, including the case that did not pass.
R176 was evaluated on selected subsets of three published datasets. The figures below come from the development-run report and its repeat run, not an independent customer test.
40,223REAL OBSERVATIONS
2 / 3FITS CERTIFIED
0SYNTHETIC INPUTS REPORTED
| Public dataset | Tested rows | R176 outcome | Reported time |
|---|---|---|---|
| Household electricity | 20,156 | Certified | 219 ms |
| Capital Bikeshare | 17,379 | Certified · 12 local constraints binding | 150 ms |
| Steel plant energy | 2,688 | Abstained · search budget exhausted | Not reported |
COMPARISON / SCIPY
The difference on the bicycle fit.
In the stated trial, tuned SciPy reported convergence but did not meet the preset objective tolerance on the Bikeshare case. It met that tolerance on household electricity. This is a case-specific comparison, not a claim that Caldrivon beats SciPy generally.
REPRODUCIBILITY / SCOPE
The outcome repeated.
A second development run reproduced the two reported certificates, the steel abstention, and the SciPy tolerance decisions. Independent case selection, external certificate checking, and customer workloads remain untested.
Review the scope. The original datasets are public; the tested subsets, fitted models, certificates, timing method and comparison settings have not yet been released as a complete independently reproducible benchmark package.
Read technical note 02 / THE SPECIALTY
Many local groups. A few shared parameters. A fit you can interrogate.
The method is built for a defined family of constrained fitting problems. It does not certify arbitrary models or every dataset.
Explicit limits
Respect bounds such as nonnegative coefficients, and test the proposed fit against the specified model.
Proof or abstention
Certify optimality and bound rounding error when the evidence closes. Otherwise report the limit rather than an unsupported result.
WHAT ISN'T CLAIMED
No universal solver superiority, no guaranteed certification within a fixed budget, and no independently verified customer deployment.
03 / SEPARATE ENGINEERING TRACK
Python repair, with a review trail.
Caldrivon also has an engineering workflow for bounded Python repair proposals, dependency inspection, trusted regression checks and human-controlled acceptance.
DIFFERENT EVIDENCE
R176's numerical fitting results do not validate Python code repair on customer repositories. That work requires its own case-by-case evaluation.
01
Define the failure
Start from an owned, reproducible Python issue and a clear test specification.
02
Inspect the impact
Review dependencies and define what code may be examined or changed.
03
Evaluate a candidate repair
Use bounded proposals and permitted tests against a known baseline.
04
Keep the decision human
Record what passed, what failed and what remains uncertain before any acceptance.
04 / WORK TOGETHER
Start with one difficult case.
We are looking for carefully scoped technical evaluations, not unrestricted access to a production system.
A suitable fitting problem
- Real observations with many local groups and some shared parameters
- Defined objective, coefficient bounds and target tolerance
- A baseline result for comparison
- Data approved for a narrow evaluation
Outside the initial scope
- Guaranteed certificates on arbitrary models
- Production changes or live customer credentials
- Confidential data without an agreed handling process
- Claims of general solver superiority or certified isolation
A DIRECT ENGINEERING CONVERSATION
Have a model that is difficult to fit convincingly?
Send the model class, constraints, approximate problem size and what your current solver reports. We can discuss whether a bounded, potentially paid evaluation makes sense. Python repair enquiries are welcome separately.
Caldrivon is an independent engineering project under active qualification. R176 timings, certificate outcomes and the SciPy comparison are developer-reported evaluations on preselected public-data subsets, not independent verification or proof of performance on other workloads. No customer deployments or general guarantees are claimed. Every engagement requires agreed scope, permissions and acceptance criteria.