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UNISOLVE 0.16.1 / CURRENT SOURCE GSPINFORGE / A GARMO LABS COMPANY

Evidence tied
to the source.

The current G suites, application-model results and historical scale records are distinct evidence sets. Figures here are reported by the owner’s release account, not engine tests rerun for this publication.

CURRENT G / ONE TEST INVENTORY

14,935 test IDs.
Two tested environments.

EnvironmentPassedSkippedAdditional
Mac ARM64 / Python 3.1414,8944134 passing subtests
Existing Brev Linux ARM64 / Python 3.1014,920151 warning; 34 passing subtests

Do not add the host totals and call them unique tests. Clean offline installed-package checks exercised all 82 profiles, both tested MCP protocol eras and fresh answer checking, retaining declines and interruptions.

CURRENT INVENTORY

Different counts.
Different meanings.

MeasureReported count
Problem-domain families13
Profiles82
Entry functions65
Python exports133
CLI operations38
Stdio MCP tools9
Runtime/data files260
SEPARATE APPLICATION PROOF

Airline planning: 1,201,500.

For the original flugpl model, a separate proof established matching objective and global bound of 1,201,500 with gap zero. Its proof-work allowance was 50 million, above the default five million. Those are model units, not airline savings.

Read all eight application outcomes
CURRENT G / HISTORICAL-DATA MODEL

221 locations.
28 proved model batches.

The LA experiment reports checked global optima for all 28 declared batches. Modeled red-exposure proxy: 17.067 → 9.101 seconds, a 46.67% reduction against an assumed equal split. Campaign wall time: 30.74 seconds.

Not measured LA congestion reduction: 2013–2016 counts, assumed cycles and independent locations. Queues, capacities, turns, pedestrians, offsets and spillback are absent. External repartitioning enabled proof completion; it is not an automatic-router fix.

HISTORICAL SOURCE C / SCALE

Large input.
Explicit result scope.

An explicit one-billion-variable, three-billion-edge graph used 72 GB of original arrays and approximately 17.39 GB peak CUDA allocation. Initial solve including validation/recount: about 406 seconds. Separate replay: about 73 seconds. Continuation improved the checked objective by approximately 4.425%; global optimality remains unproved.

The 200-billion records distinguish sparse namespaces, implicit structured proofs and a materialized 25 GB packed state. The temporary state was deleted after validation; evidence is retained. One physical GH200 was used, not physical multi-GPU scaling.

RETAINED NEGATIVE FINDINGS

Selection is implemented.
A universal learned gain is not.

The retained routing study measured 186 actual dispatch outcomes. In one comparison, probing-only completed 39/42, while the then-packaged learned default completed 37/42 and was slower. The general learned promotion was not made. A separate linear-capability selector also did not show held-out improvement.

Garmo Solvers Router’s separate positive portfolio result must not overwrite these UNISOLVE records.

HISTORY & DEPLOYMENT

Keep the old records
under their old identities.

Earlier 78-profile, candidate-D and 0.13 descriptions remain historical. Current G figures are not retroactively assigned to those builds. Optional numerical/SMT/CUDA integrations retain their dependencies and trust boundaries. The source does not establish universal source-language conformance, physical multi-GPU speedup or a hosted multiuser security sandbox.

Review runtime scope
YOUR NEXT MOVELET’S MAKE IT COUNT

Put it
to work.

One capability. A connected workflow.
A team to put it into practice.

patrick@garmolabs.com

FIND YOUR CAPABILITY