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TNFR AI Agent
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Merge relational coefficient and research consolidation
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@@ -53,6 +53,8 @@ graph invalidation and persistence retain their separate responsibilities.
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| Regional orientation relative to a supplied rate source |[`source_relative_form.py`](src/tnfr/physics/source_relative_form.py); reuses the regional form observer |
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| Conditional relational phase/form execution |[`dynamics/relational.py`](src/tnfr/dynamics/relational.py); one admitted field and atomic Euler step, reused by the SDK |
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| Prepared relational pattern observations |[`physics/relational_observations.py`](src/tnfr/physics/relational_observations.py); supplied regions/reference lifts, shared winding and regional support accounting |
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| Conditional coefficient uncertainty |[`physics/relational_observations.py`](src/tnfr/physics/relational_observations.py); exact three-sample stencils delegate to shared outward jet bounds, with declared preparation/clock/error premises |
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| Retained P2 acquisition audit |[`research/relational_acquisition.py`](src/tnfr/research/relational_acquisition.py); read-only protocol/archive and all-step error reconstruction; does not import the producer or evolve a graph |
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| Protected relational basins |[`physics/relational_capture.py`](src/tnfr/physics/relational_capture.py); reflected, full-state local and acute-sector theorems sharing exact phase/energy enclosures for conditional ideal-law limits |
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| Validated relational transit |[`physics/relational_transit.py`](src/tnfr/physics/relational_transit.py); exact reflected ODE enclosure using shared rational intervals, Taylor derivatives and signed-diagonal comparison; read-only proof computation, not live engine evolution |
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| Coherence and equilibrium kernel |[`common.py`](src/tnfr/metrics/common.py)|
@@ -220,6 +222,25 @@ thin adapter; the SDK's `relational_report_to_dict` supplies the exact rational
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JSON projection for the existing writer. Reports are observations, not live
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checkpoints or authenticated execution histories.
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The same observation owner exposes graph-independent coefficient-jet and
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three-sample bounds. Exact rational stencil/error propagation delegates to the
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shared outward interval kernel and mathematical-pi enclosure, rather than a
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second coefficient estimator. These are module-level APIs, not `Network`
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methods: the caller supplies preparation, affine timing, baseline/gain and
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independent uncertainty. The existing exact report exporter supports both
classifies the supporting branches without scheduling additional campaigns.
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### How this could connect to physical reality
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@@ -267,6 +288,7 @@ seeds; direct `TNFR.create(..., seed=...)` sets the topology seed.
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| Compare an attachment |`relational_attachment`| Fresh separate/joined fields for a hypothetical connection; does not change live support |
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| Compare a bridge relocation |`relational_relocation`| Supplied atomic bridge exchange preserving internal support; passive budget and recovery are separate obligations |
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| Apply a capture or transit theorem |`Network.relational_*capture`| Read-only sufficient certificates with support-specific hypotheses |
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| Bound a prepared coefficient response |`bound_relational_coefficient_from_jet`, `bound_relational_coefficient_from_samples`| Module-level observers with declared uncertainty; no graph evolution or preparation authentication |
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The [regional and relational guide](https://github.com/fermga/TNFR-Python-Engine/blob/main/docs/guides/REGIONAL_AND_RELATIONAL.md)
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provides preparations and examples for joint dynamics. The
@@ -300,6 +322,7 @@ belong to the [structural field guide](https://github.com/fermga/TNFR-Python-Eng
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| What constrains a change of connections? |[Event storage, passivity and selection limits](https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/nodal/RELATIONAL_PATTERN_COMPOSITION.md#support-event-premise-admission)|
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| Can a pattern survive a relocated connection? |[Passive relocation and recovery](https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/nodal/RELATIONAL_PATTERN_COMPOSITION.md#identity-preserving-bridge-relocation)|
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| How does hidden state produce memory? |[Derived pattern memory](https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/nodal/RELATIONAL_PATTERN_MEMORY.md)|
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| What can identify a coefficient, and what does not select its law? |[Coefficient, memory and preparation admission](theory/nodal/RELATIONAL_EXCHANGE_ADMISSION.md#coefficient-synergy-audit)|
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| Which terms are assumptions, results or observations? |[Classified glossary](https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/GLOSSARY.md)|
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| How will physical claims be tested? |[Measurement and reserved evaluation](https://github.com/fermga/TNFR-Python-Engine/blob/main/theory/research/FIVE_STAGE_EXECUTION_PLAN.md#supporting-measurement-bridge)|
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@@ -317,9 +340,11 @@ does not make them interchangeable with the main generative model.
|`docs/assets/`, `artifacts/`| Preserved published evidence and local protocols/source archives/responses; missing local evidence remains unavailable |
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|[applications/](https://github.com/fermga/TNFR-Python-Engine/blob/main/applications/README.md)| Optional arithmetic applications with explicit verification boundaries |
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