0. Registry Classification

1. Purpose

1.1 What This Arc Repairs

Classifier / Feedback Integrity Restoration repairs systems where response policy, classification, interpretation, or feedback loops no longer correspond to real system state.

In biological / medicine-adjacent mapping, this arc is conceptual only. It does not diagnose, treat, or prescribe. It describes restoration geometry for systems where signal classification and feedback policy have drifted from coherence.

This arc applies when apparent improvement in a proxy does not equal improved resilience, or when the system selects the wrong response because feedback is contaminated, delayed, over-weighted, under-weighted, or misclassified.

This arc repairs classifier / feedback failure by:

• stopping premature policy reselection;
• restoring auditability over signal, response, and outcome;
• restoring feedback integrity;
• distinguishing real coherence from proxy improvement;
• identifying overresponse, underresponse, wrong response, and locked response patterns;
• separating current signal from old recurrence memory;
• retesting response policy under field conditions;
• routing follow-on repair to boundary, timing, circulation, delivery, recurrence, or temporal proof arcs;
• validating that resilience improves, not merely visible Φ.

Classifier / Feedback Integrity Restoration is the canonical arc for restoring the system’s ability to read state and choose response correctly.

1.2 Core Restoration Function

This arc restores response-policy coherence by repairing classifier fidelity and feedback integrity so the system can distinguish O from Φ, signal from noise, present state from recurrence memory, and correct response from maladaptive policy.

Classifier / Feedback Integrity Restoration prevents proxy improvement from being mistaken for resilience.

2. Use Conditions

2.1 When to Apply

Use this arc when:

• the system appears better by a proxy but resilience is not improving;
• response policy changes too quickly after a transient signal;
• response policy remains locked after conditions change;
• the classifier overreacts to low-threat signal;
• the classifier underreacts to meaningful signal;
• feedback loops are contaminated by noise, recurrence memory, or boundary leakage;
• current signal is confused with old pattern;
• response policy produces repeated recurrence;
• apparent symptom reduction, metric improvement, or local quiet may be hiding H;
• biological, security, AI, institutional, or platform systems need to retest whether their response policy fits field conditions.

Examples:

• a conceptual biological system reduces a visible proxy but becomes less tolerant of perturbation;
• a security classifier lowers alert volume by missing important signal;
• an AI safety classifier reduces visible risk by over-refusing valid requests;
• an institution changes policy after one noisy event and creates broader instability;
• a platform uses appeal reversal data but fails to update the underlying classifier;
• a system treats old recurrence memory as present signal and keeps selecting an outdated response.

2.2 When Not to Apply

Do not apply this arc when:

• the primary failure is boundary leakiness and RA-068 must occur first;
• the primary failure is delivery geometry or throughput and RA-070 must occur first;
• the primary failure is clearance or stasis and RA-071 must occur first;
• the primary failure is timing window error and RA-072 must occur first;
• the system lacks enough signal visibility to audit classifier behavior;
• active harm requires immediate stabilization first;
• classifier repair would suppress valid alarm signal;
• feedback correction is being used to ignore affected-node signal;
• the biological / medical case requires clinical evaluation rather than conceptual systems mapping.

Classifier / Feedback Integrity Restoration must not become metric-relabeling theater.

2.3 Required Preconditions

Before this arc begins, the following must be true:

If required preconditions fail:

Arc cannot validly begin.

The system must route to Boundary / Barrier Stabilization, Audit Surface Expansion, Observability Restoration, Timing Window Repair, Circulation Clearance Restoration, Recurrence Memory Repair, or Biological Temporal Proof.

3. Failure / Damage Signature

3.1 Pre-State Across S

3.2 Primary Failure Links

3.3 Origin-Layer Localization

Canon rule:

A response policy is not valid because the visible proxy improves. It is valid only when field coherence, resilience, and recurrence behavior improve.

4. Restoration Objective

4.1 Canonical Objective

Restore classifier fidelity and feedback integrity so response policy fits actual state rather than proxy appearance.

Formal objective:

FI ↑
classifier_fidelity ↑
feedback_integrity ↑
response_policy_fit ↑
proxy_improvement_risk ↓
wrong_response_policy ↓
signal_provenance ↑
resilience ↑
ι ↓
recurrence ↓
Φ/O divergence ↓

Expanded objective:

Convert noisy, proxy-driven, or locked response policy into field-corrective classification that improves resilience and coherence over time.

4.2 Non-Goals

This arc does not aim to:

• suppress valid alarm signals;
• force neutral response when action is required;
• treat all proxy improvement as false;
• eliminate all classifiers;
• replace field validation with internal confidence;
• overfit response policy to a single incident;
• make response policy infinitely flexible;
• confuse classifier adjustment with full recovery;
• declare resilience from one quiet interval;
• provide medical diagnosis, treatment, or prescription in biological contexts.

5. Operator Sequence

5.1 Minimal Operator Scaffold

Σ O-not-Φ invariant + Θ premature policy reselection damping → Au signal / response / outcome trace → FI feedback restoration → Π boundary-noise check → ℛ response-policy repair routing → Λ field-fit retest → Τ resilience and recurrence proof

Reference sequence from the registry:

stop premature policy reselection
→ restore auditability
→ restore FI
→ distinguish O from Φ
→ retest response policy

Universal grammar alignment:

Σ + Θ → Au + FI → Π → ℛ → Λ → Τ

Classifier / Feedback Integrity Restoration may route into Boundary / Barrier Stabilization, Geometry / Delivery Restoration, Circulation Clearance Restoration, Timing Window Repair, Recurrence Memory Repair, Biological Temporal Proof, Goodhart Repair, or AI Classifier / Evaluator Restoration.

5.2 Operator Step Table

5.3 Sequence Notes

This arc is O-not-Φ-gated, FI-gated, response-policy-gated, and temporal-proof-gated.

The sequence must distinguish:

signal
noise
proxy
state
classification
feedback
response policy
field outcome
resilience

The following steps cannot be skipped:

stop premature policy reselection
restore signal auditability
restore feedback integrity
separate O from Φ
check boundary contamination
retest response policy
validate resilience
monitor recurrence

If response policy changes before signal provenance is clear, drift can worsen.

If classifier accuracy improves internally but field outcomes do not improve, restoration is incomplete.

If proxy improvement is still used as proof, the arc fails.

6. Restoration Phases

Phase 0 — Identify Classifier / Response Policy

Purpose: Name the classifier or feedback loop being repaired.

Actions:

• identify classifier, sensor, evaluator, feedback loop, interpretation rule, or response policy;
• identify what it claims to detect;
• identify current response;
• identify visible proxy;
• identify affected field;
• identify whether the policy is overactive, underactive, locked, or unstable.

Validation:

classifier object named
claimed signal visible
response policy identified

Phase 1 — Stop Premature Policy Reselection

Purpose: Prevent unstable signal from triggering maladaptive policy.

Actions:

• pause unnecessary policy switching;
• avoid overfitting to one transient signal;
• identify whether conditions are stable enough to change response;
• prevent urgency from selecting the wrong policy;
• preserve necessary protective response where valid;
• define minimum evidence threshold for policy change.

Validation:

premature policy reselection ↓
response instability ↓
K ↑

Phase 2 — Restore Auditability

Purpose: Make classification and response visible enough to evaluate.

Actions:

• trace input signal;
• trace source and provenance;
• trace classifier threshold or rule;
• trace response policy;
• trace timing;
• trace outcome;
• trace proxy metric;
• trace recurrence and field feedback.

Validation:

Au_eff ↑
signal_provenance ↑
response policy reconstructible

Phase 3 — Restore Feedback Integrity

Purpose: Ensure feedback corrects the classifier and response policy.

Actions:

• connect field outcome to response policy;
• connect recurrence to classifier adjustment;
• connect resilience to policy validity;
• connect affected-node signal where relevant;
• separate delayed effects from immediate proxies;
• ensure feedback can change the policy.

Validation:

FI ↑
feedback_integrity ↑
field feedback loss ↓

Phase 4 — Distinguish O From Φ

Purpose: Prevent proxy improvement from certifying restoration.

Actions:

• identify visible proxy improvement;
• test whether resilience improves;
• test whether recurrence decreases;
• test whether hidden debt decreases;
• test whether response policy improves field coherence;
• identify if proxy improvement masks wrong response.

Validation:

proxy_improvement_risk ↓
Φ/O divergence ↓
ι ↓

Phase 5 — Retest Response Policy

Purpose: Determine whether the selected response fits current conditions.

Actions:

• test overresponse;
• test underresponse;
• test no-response;
• test delayed response;
• test alternative response;
• test whether boundary stabilization is needed first;
• test whether timing, circulation, or recurrence repair is needed;
• select the lowest-distortion response that improves O.

Validation:

response_policy_fit ↑
wrong_response_policy ↓
classifier_fidelity ↑

Phase 6 — Route Follow-On Repair

Purpose: Send repair to the layer revealed by classifier restoration.

Actions:

• route boundary contamination to RA-068;
• route delivery geometry to RA-070;
• route clearance failure to RA-071;
• route timing failure to RA-072;
• route recurrence lock to RA-073;
• route temporal validation to RA-074;
• route AI evaluator analogs to RA-058.

Validation:

R ↑
origin-layer repair path visible
response policy no longer isolated

Phase 7 — Temporal Resilience Proof

Purpose: Confirm policy improves resilience over time.

Actions:

• monitor recurrence;
• monitor perturbation tolerance;
• monitor field outcomes;
• monitor hidden debt;
• monitor classifier fidelity;
• monitor response-policy stability;
• monitor whether proxy improvement remains aligned with O;
• retest under changed conditions.

Validation:

resilience ↑
recurrence ↓
feedback_integrity stable or ↑
Φ/O divergence ↓

7. Gates

7.1 Required Gates

7.2 Gate Failure Rule

If any required gate fails:

∅ — Classifier / Feedback Integrity Restoration cannot validly proceed in that form.

The system must either:

• restore auditability;
• reduce boundary contamination;
• stop premature response-policy changes;
• distinguish O from Φ;
• restore field feedback;
• route to boundary, timing, clearance, recurrence, or temporal proof repair;
• withhold response-policy or recovery claims until resilience proof exists.

8. Diagnostics

8.1 Required Diagnostic Trends

8.2 Arc-Specific Diagnostic Thresholds

Suggested thresholds:

FI ↑
classifier_fidelity ↑
feedback_integrity ↑
response_policy_fit ↑
proxy_improvement_risk ↓
wrong_response_policy ↓
signal_provenance ↑
resilience ↑
ι ↓
recurrence ↓
Φ/O divergence ↓

Classifier / Feedback Integrity Restoration is not complete if:

response policy changes before signal is understood
proxy improvement is still treated as recovery
feedback cannot change classifier behavior
field outcomes contradict evaluator confidence
boundary noise still contaminates classification
wrong response policy continues
resilience does not improve
recurrence is not monitored
temporal proof is absent

9. Anti-Patterns / False Restorations

9.1 Common False Versions

This arc is being simulated, not executed, if:

• the classifier is renamed but response policy remains wrong;
• a proxy improves while resilience worsens;
• feedback is collected but cannot update policy;
• field signal is dismissed because internal metrics improved;
• response policy is changed after one noisy event;
• classifier thresholds shift without tracing false positives or false negatives;
• old recurrence memory is treated as present signal;
• apparent quiet is treated as classifier success;
• the system tests only in the context where the proxy improved;
• biological conceptual mapping is mistaken for clinical validation.

9.2 Named Anti-Pattern Links

10. Completion Criteria

10.1 Post-State Signature

10.2 Temporal Proof

Classifier / Feedback Integrity Restoration cannot be certified by one improved proxy. It requires field validation and recurrence reduction over time.

Template:

Completion requires FI ↑,
classifier_fidelity ↑,
feedback_integrity ↑,
response_policy_fit ↑,
proxy_improvement_risk ↓,
wrong_response_policy ↓,
signal_provenance ↑,
resilience ↑,
ι ↓,
recurrence ↓,
and Φ/O divergence ↓ across U7.

Minimum temporal proof:

• response policy improves field outcomes;
• resilience improves under perturbation;
• recurrence decreases;
• feedback can update classifier behavior;
• proxy improvement remains aligned with O;
• boundary noise no longer contaminates signal;
• temporal validation confirms policy stability.

10.3 Completion Statement

Canonical format:

This arc is complete only when classifier fidelity and feedback integrity are restored, response policy fits field conditions, proxy improvement no longer substitutes for coherence, resilience improves, and recurrence of the wrong response policy decreases over time.

11. Cross-Links

11.1 Related Restoration Arcs

11.2 Related Failure Modes

11.3 Related Diagnostics

Au, Au_eff, H, O, ε, ι, BΣ, K, R, FI, 𝓓, τ_resp, classifier_fidelity, feedback_integrity, response_policy_fit, proxy_improvement_risk, wrong_response_policy, signal_provenance, resilience, recurrence, Φ/O divergence

11.4 Related Laws / Invariants

INV — Proxy improvement is not resilience.
INV — Classifier fidelity requires field feedback.
INV — Response policy must be retested after state change.
INV — O must not be inferred from Φ alone.
LAW — Premature policy reselection creates recurrence.
LAW — Feedback integrity loss locks wrong response.
LAW — Misclassified signal routes repair to the wrong layer.
LAW — Φ quiet is not O restoration.

12. Domain Notes

12.1 Biology / Medicine

Conceptual systems mapping only.

Check:

• classifier fidelity;
• feedback integrity;
• response policy;
• proxy improvement;
• field outcome;
• resilience;
• recurrence;
• timing;
• boundary contamination;
• perturbation tolerance.

This arc does not provide diagnosis, treatment, or medical advice. It maps a systems pattern: a response can look locally successful while the system remains less resilient if the classifier is reading the wrong signal or optimizing the wrong proxy.

12.2 AI / Cognitive Infrastructure

Check:

• classifier thresholds;
• evaluator feedback;
• refusal / routing policy;
• user correction signal;
• memory influence;
• field validation;
• false positives;
• false negatives;
• reward model behavior.

AI classifier restoration is the direct cross-domain analog: the system must not treat evaluator confidence or benchmark improvement as field coherence.

12.3 Security

Check:

• alert classification;
• false-positive and false-negative rates;
• incident triage;
• anomaly thresholds;
• response policy;
• feedback from confirmed incidents;
• field outcomes;
• recurrence.

Security systems fail when response policy follows alert metrics rather than true risk and resilience.

12.4 Platform Governance

Check:

• moderation classifiers;
• appeal reversals;
• support triage;
• ranking classifiers;
• trust scores;
• user reports;
• feedback routing;
• recurrence of misclassification.

Platform systems require classifier feedback integrity when appeal, report, or enforcement outcomes should update the classifier but do not.

12.5 Economy

Check:

• risk scoring;
• credit classification;
• fraud flags;
• eligibility rules;
• repayment performance as proxy;
• resilience of borrower or worker;
• field outcomes;
• recurrence of burden.

Economic classifiers fail when visible payment, retention, or compliance is mistaken for health while hidden burden increases.

12.6 CMS / Meaning / Archetypes

Check:

• symbolic classification;
• legitimacy recognition;
• taboo labeling;
• archetypal overfit;
• recurrence memory;
• field response;
• meaning fidelity.

Meaning systems require classifier restoration when a symbol, role, or signal is repeatedly misread and the same response policy keeps returning.

13. Machine-Readable Metadata

id: "RA-069"
title: "Classifier / Feedback Integrity Restoration"
aliases:
  - "Classifier / FI Restoration"
family_primary: "Biology / Medicine / Classifier Integrity"
families_secondary:
  - "Core"
  - "Biology / Medicine"
  - "Classifier Integrity"
  - "Feedback Integrity"
  - "Coherence"
  - "Boundary"
  - "Signal"
  - "Damping"
  - "Timing"
  - "Provenance"
  - "Restoration Capacity"
  - "Cross-Domain"
treatment: "Canon Parent Arc"
status: "Canon-Ready"
scope:
  - "Biological"
  - "Medical-Adjacent Conceptual"
  - "Personal Systems"
  - "Institutional"
  - "AI"
  - "Security"
  - "Cross-Domain"
u_layers:
  failure_origin:
    - "often U3 classifier / response policy"
    - "often U4 interpretation narrative"
    - "often U5 feedback / recurrence / timing layer"
  symptom_visible:
    - "U4 visible response / policy shift / apparent improvement / repeated misread / response justification"
  repair_required:
    - "same or lower than the layer where classifier fidelity or feedback integrity failed"
  validation:
    - "U6"
    - "U7"
operators:
  scaffold: "Σ O-not-Φ invariant + Θ premature policy reselection damping → Au signal / response / outcome trace → FI feedback restoration → Π boundary-noise check → ℛ response-policy repair routing → Λ field-fit retest → Τ resilience and recurrence proof"
  sequence:
    - "Σ"
    - "Θ"
    - "Au"
    - "FI"
    - "Π"
    - "ℛ"
    - "Λ"
    - "Τ"
state_variables:
  primary:
    - "Au"
    - "Au_eff"
    - "H"
    - "O"
    - "FI"
  secondary:
    - "ε"
    - "ι"
    - "BΣ"
    - "K"
    - "R"
    - "𝓓"
    - "τ_resp"
    - "Φ"
diagnostics:
  - "classifier_fidelity"
  - "feedback_integrity"
  - "response_policy_fit"
  - "proxy_improvement_risk"
  - "wrong_response_policy"
  - "signal_provenance"
  - "resilience"
  - "recurrence"
  - "Φ/O divergence"
gates_required:
  - "FI-Gate"
  - "HR-Gate"
  - "MS-Gate"
  - "Au-Actuation"
  - "BΣ-Gate"
  - "Λ-Gate"
  - "☷ᵢ"
linked_failure_modes:
  - "Proxy Improvement Without Resilience"
  - "Wrong Response Policy"
  - "Classifier Drift"
  - "Feedback Integrity Loss"
  - "False O From Φ"
  - "Premature Policy Reselection"
  - "Overresponse"
  - "Underresponse"
  - "Signal Misclassification"
  - "Response Policy Lock"
  - "Field Feedback Loss"
  - "Classifier Overfitting"
  - "Classifier Underfitting"
  - "Recurrence Misread"
linked_restoration_arcs:
  - "RA-004"
  - "RA-005"
  - "RA-006"
  - "RA-007"
  - "RA-008"
  - "RA-012"
  - "RA-014"
  - "RA-025"
  - "RA-026"
  - "RA-036"
  - "RA-058"
  - "RA-068"
  - "RA-070"
  - "RA-071"
  - "RA-072"
  - "RA-073"
  - "RA-074"
anti_patterns:
  - "Metric Relabeling Theater"
  - "Proxy Recovery"
  - "Feedback Inertia"
  - "Premature Policy Reselection"
  - "Threshold Shuffle"
  - "Internal Confidence Capture"
  - "Quiet-as-Policy Proof"
  - "Recurrence Memory Capture"
  - "Field-Blind Evaluation"
completion_tests:
  - "feedback integrity increases"
  - "classifier fidelity increases"
  - "feedback integrity increases"
  - "response policy fit increases"
  - "proxy improvement risk decreases"
  - "wrong response policy decreases"
  - "signal provenance increases"
  - "resilience increases"
  - "inversion index decreases"
  - "recurrence decreases"
  - "Φ/O divergence decreases"
summary: "Classifier / Feedback Integrity Restoration repairs proxy improvement without resilience, wrong response policy, classifier drift, and feedback distortion by stopping premature policy reselection, restoring auditability and feedback integrity, distinguishing O from Φ, and retesting response policy through field validation."

Final Calibration Rule

Classifier / Feedback Integrity Restoration answers six questions:

What classifier, feedback loop, or response policy is selecting the wrong response?
What proxy improvement is being mistaken for O or resilience?
What signal provenance and field feedback are needed to retest the classifier?
Where is the system overresponding, underresponding, locking, or prematurely reselecting policy?
What follow-on repair is revealed by restored feedback integrity?
How is response-policy fit proven over time without metric relabeling, proxy recovery, feedback inertia, or quiet-as-policy proof?