Spark

Philosophy

Resolve the identity of the validated candidate and the presence of numerical evidence before novelty claims. Unknown units or residuals require repair rather than a silent pass.

Architecture & control flow

1. 01

   The CLI indexes actual equation/conjecture payloads by idea IDs and resolves physical abstractions through physics_model_id.

2. 02

   Canonical term signatures and Jaccard distance compare against 22 known equation families.

3. 03

   Term roles select one of four probes—linear decay, quadratic gradient flow, circle projection or memory decay—to produce residual, energy and stability traces.

4. 04

   PhysicsTester emits mutation signals from energy, constraint, limit and unit checks; Aurora sends unresolved numerical evidence to repair.

Architecture outline derived from this version’s control flow.

Inputs & outputs

Inputs

Actual genomes, physical abstractions, conjecture/object IDs, the known-equation library and small probe configurations.

Outputs

Full payload arrays and per-kind JSONL, canonical-prior metrics, residual samples, PhysicsTester reports and mutation signals, with separate research and Aurora contracts.

Implemented components

• Abstract-unit parsing, rejection of unknown residuals, a 22-family prior index, four genuinely numerical small probes and interface audits.

Implementation & evidence scope

Probes select fixed small systems by term category rather than solve arbitrary genomes. The 5.5 audit identified coefficient-unit and harvest-viability gaps; failed equations could still be packaged as ideas. Synthetic priors and default counterexample fields cannot establish original mathematical theorems.

Code & bundled material

The introduction draws on bundled notes, changelogs and central code. Software tests, synthetic diagnostics and scientific effectiveness use different evidence standards.