Origin

Philosophy

Explore objects and mappings before deriving equation representations. Search paths, historical derivation lineages and falsification tests belong to the candidate record.

Architecture & control flow

1. 01

   Domain Resolver 2.0 extracts a field and runs the underlying Forge discovery layer for tensions.

2. 02

   Six domain, seven math and seven physics operators compose, deduplicate and cluster paths into NovelMapping objects.

3. 03

   Twelve mapping seeds and twelve prior traces label known reuse versus candidate ontology shifts.

4. 04

   Mappings generate equation ASTs, small probes and deterministic CoMath outputs; specific ideas include kill tests and persistent path memory.

Architecture outline derived from this version’s control flow.

Inputs & outputs

Inputs

Direction, mode, tensions, D/M/P operator libraries and known mapping/prior-trace libraries.

Outputs

Ontology path/object/mapping/trace JSONL, ASTs, simulation reports, CoMath outcomes, reviewer verdicts, specific ideas and failed-pattern memory.

Implemented components

• First-class mappings, Python D/M/P operators, path memory, mapping-to-AST construction, concrete edits for four repair operators and deterministic CoMath output functions.

Implementation & evidence scope

Contemporary probes hardcode heat/decay/graph/stochastic dynamics and mostly use AST IDs; generated equations are not generally solved. Mapping novelty labels reflect a small built-in library, not external prior-art validation; v5.6 ideas do not independently traverse a complete Aurora chain. CoMath outputs are deterministic and template-driven, without machine proof, real agent research or human expert validation.

Code & bundled material

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