Merge audited organized-pattern research and TNFR foundations

Problem and resulting behavior
Integrate the formed-class research, shared full-state observation tools and
SDK/numerical consolidation into main. Conditional formation, interaction,
memory, nonadditivity and storage results retain their complete-law premises,
frozen protocols, source archives and independently evaluated responses.

Scope
Keep native relational execution separate from detached smooth-sine models.
Centralize scalar and retained-step admission, reject inconsistent failure
provenance and expose advanced reports through the shared SDK exporter.
The documented next gate tests constitutive freedom at matched organized
targets and complete tangent dynamics. Cross-scale law inheritance is a
separate conditional dependency; physical identification remains open.

Validation
- Final routine engine/API gate: 9930 passed, 13 skipped on Python 3.13.
- Affected readout/consumer controls: 331 passed.
- Selected frozen/artifact audits: 258 passed plus 12 four-history checks.
- Black, isort, workflow guard, flake8 and documentation integrity passed.
- All 8018 internal references and the strict documentation build passed.

Review notes
No frozen producer was rerun and no new reserved or physical response was
evaluated during consolidation. Local validation covers the routine gate
and selected research owners, not a replay of every historical campaign.

Author: TNFR AI Agent

.gitattributes

@@ -25,6 +25,95 @@ docs/assets/relational_reversed_form_response/result.protocol.json -text
 docs/assets/relational_memory_response/result.prediction.json -text
 docs/assets/relational_memory_response/result.json -text
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 theory/research/archive/EXECUTION_PLAN_PRE_FOUR_FOUNDATIONS_2026-09-26.txt -text -whitespace
 theory/research/archive/PORTFOLIO_PRE_FOUR_FOUNDATIONS_2026-09-26.txt -text -whitespace
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.github/agents/my-agent.md

@@ -165,11 +165,21 @@ logs or duplicate result inventories. Follow F1-F4:
    obstruction. Freeze preparation, law, clock, observation, horizon and
    numerical budget before evaluating the reserved response.
 
+Use the [NFR ontology] and applicable operator contracts when admitting a law:
+declare identity, retained constituent/boundary state, symmetries and the
+actual execution path. A collective event needs its own admission, descent
+and occurrence argument even when continuous collective dynamics closes.
+Use formed organizations as conditional test cases, not as a reason to assume
+that every compatible law acquires them or realizes every registered operator.
+
 Reuse the [theory-to-execution map]. Combine results only after matching complete
 law, support, capacity, clock, preparation and retained coordinates. Methods can
 transfer between models; their coefficients and verdicts do not automatically
-transfer. Tangent reduction need not close a nonlinear law. Eliminating hidden
-state can introduce memory and retains hidden initialization and forcing.
+transfer. Matching equilibria and full tangents does not transfer nonlinear
+remainders, source transport or acquisition/capture certificates; re-admit
+regularity and bounds for the actual law. Tangent reduction need not close a
+nonlinear law. Eliminating hidden state can introduce memory and retains
+hidden initialization and forcing.
 Formation, recovery, winding, lifetime and maintenance have separate obligations:
 response peaks, pulses, recurrence, invariant families and finite retention do
 not establish formation or autonomous scale selection. An admissible connection
@@ -273,6 +283,7 @@ Detailed admission and verification belong to the linked contracts.
 [API contracts]: https://github.com/fermga/TNFR-Python-Engine/blob/main/docs/API_CONTRACTS.md
 [form foundation]: https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/FUNDAMENTAL_THEORY.md
 [parameter foundation]: https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/NODAL_PARAMETER_FOUNDATIONS.md
+[NFR ontology]: https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/EMERGENT_ONTOLOGY.md
 [grammar]: https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/UNIFIED_GRAMMAR_RULES.md
 [operator]: https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/STRUCTURAL_OPERATORS.md
 [event contracts]: https://github.com/fermga/TNFR-Python-Engine/blob/main/docs/contracts/OPERATOR_EVENTS.md

.gitignore

@@ -30,6 +30,9 @@ Thumbs.db
 artifacts/
 profiles/
 
+# Exact frozen full-step response is versioned losslessly in its response ZIP.
+/docs/assets/sine_formed_classes/class-nonlinear-readout-v1.json
+
 # Crypto transient logs
 crypto/extract_debug.txt
 crypto/extract_stderr.txt

AGENTS.md

@@ -165,11 +165,21 @@ logs or duplicate result inventories. Follow F1-F4:
    obstruction. Freeze preparation, law, clock, observation, horizon and
    numerical budget before evaluating the reserved response.
 
+Use the [NFR ontology] and applicable operator contracts when admitting a law:
+declare identity, retained constituent/boundary state, symmetries and the
+actual execution path. A collective event needs its own admission, descent
+and occurrence argument even when continuous collective dynamics closes.
+Use formed organizations as conditional test cases, not as a reason to assume
+that every compatible law acquires them or realizes every registered operator.
+
 Reuse the [theory-to-execution map]. Combine results only after matching complete
 law, support, capacity, clock, preparation and retained coordinates. Methods can
 transfer between models; their coefficients and verdicts do not automatically
-transfer. Tangent reduction need not close a nonlinear law. Eliminating hidden
-state can introduce memory and retains hidden initialization and forcing.
+transfer. Matching equilibria and full tangents does not transfer nonlinear
+remainders, source transport or acquisition/capture certificates; re-admit
+regularity and bounds for the actual law. Tangent reduction need not close a
+nonlinear law. Eliminating hidden state can introduce memory and retains
+hidden initialization and forcing.
 Formation, recovery, winding, lifetime and maintenance have separate obligations:
 response peaks, pulses, recurrence, invariant families and finite retention do
 not establish formation or autonomous scale selection. An admissible connection
@@ -273,6 +283,7 @@ Detailed admission and verification belong to the linked contracts.
 [API contracts]: https://github.com/fermga/TNFR-Python-Engine/blob/main/docs/API_CONTRACTS.md
 [form foundation]: https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/FUNDAMENTAL_THEORY.md
 [parameter foundation]: https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/NODAL_PARAMETER_FOUNDATIONS.md
+[NFR ontology]: https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/EMERGENT_ONTOLOGY.md
 [grammar]: https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/UNIFIED_GRAMMAR_RULES.md
 [operator]: https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/STRUCTURAL_OPERATORS.md
 [event contracts]: https://github.com/fermga/TNFR-Python-Engine/blob/main/docs/contracts/OPERATOR_EVENTS.md