A system cannot remain aligned if its memory refuses to learn from truth, feedback, recurrence, harm, repair, and time. When memory freezes around an old interpretation, principle becomes ideology. When memory updates coherently, principle remains alive.

1. Formal Definition

The Memory Update Law states that principle-guided systems require updateable memory to preserve coherence across time.

Principles do not operate in a static field. Conditions change. Evidence accumulates. Hidden debt returns. Recurrence reveals what was not repaired. Affected-node feedback clarifies what was misread. A prior interpretation may become incomplete, outdated, overgeneralized, or inverted.

A system that cannot update memory may continue to claim principle alignment while acting from obsolete or distorted maps.

Memory must update from:

truth;
evidence;
recurrence;
affected-node feedback;
failed predictions;
hidden debt return;
boundary stress;
legitimacy loss;
restoration outcomes;
time validation;
changed capacity;
changed context;
changed scale;
changed coupling.

Without memory update, principles become rigid, identity-bound, ideological, performative, or inverted.

With memory update, principles remain living constraint fields.

2. Canonical Form

Core form:

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principle coherence requires updateable memory

Learning form:

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M_update = f(Ψ, Τ, recurrence, Au, FI, H_return)

Failure form:

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memory frozen while field changes ⇒ ideology hardening + H↑ + ι↑

Restoration-valid form:

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feedback integrated + recurrence learned + principle field recalibrated ⇒ O↑ / H↓ over Τ

Related variables:

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O, H, ε, ι, Au, R, R_eff, BΣ, K, σ, µᵢ, Φ, Λ, ⊗, Γ, Π, ℛ, Θ, Σ, Ψ, Τ, FI, M, M_update, M_integrity, M_half_life, ☷ᵢ

Where:

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Variable	Meaning in this law
`M`	Memory state of the system
`M_update`	Ability of memory to update from feedback, recurrence, evidence, and time
`M_integrity`	Reliability, fidelity, continuity, and non-corruption of memory
`M_half_life`	How long valid memory remains accessible and effective
`☷ᵢ`	Principle field whose constraint logic must update without losing coherence
`Ψ`	Field and affected-node feedback that updates memory
`Τ`	Time validation and temporal learning horizon
`FI`	Feedback integrity required for learning
`Au`	Auditability required to know what happened and why
`H_return`	Hidden debt returning as evidence of prior misread or non-repair
`O`	Coherence preserved when memory updates correctly
`H`	Hidden debt accumulated when memory refuses truth
`ι / Ξ`	Inversion rises when old memory is defended against new evidence
`µᵢ`	Meaning / agent integrity; degrades when memory contradicts reality
`BΣ`	Boundary integrity; memory must update around consent, scope, and coupling history
`K / σ`	Slack / sovereignty; memory update should reduce repeated burden
`R / R_eff`	Restoration capacity required to repair what memory learns
`Φ`	Visible success proxy; may reward forgetting or selective memory
`Λ`	Compatibility between old interpretation and current field state
`⊗`	Coupling history that memory must preserve or revise
`Γ`	Classifies memory as valid, outdated, corrupted, selective, or identity-bound
`Π`	Controls, curation, suppression, narrative filters, or update gates
`ℛ`	Restoration action that repairs memory, debt, and trajectory
`Θ`	Humility required to revise memory without collapsing principle
`Σ`	Scope of memory update and principle recalibration
`L`	Legitimacy strengthened when memory updates truthfully

3. Core Mechanism

The law unfolds when a system either learns from time or defends memory against time.

Coherent memory update pathway

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event / action / claim occurs
→ field feedback emerges
→ recurrence and hidden debt are tracked
→ memory updates
→ principle field recalibrates
→ future action improves
→ O rises or stabilizes over time

Frozen memory pathway

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event / action / claim occurs
→ field feedback contradicts memory
→ contradiction is ignored or reclassified
→ old memory is defended
→ principle hardens into ideology
→ H↑ + ι↑ + L↓

The core mechanism is:

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memory that cannot update turns principle into ideology

Detailed mechanism:

A system forms memory.

Memory stores prior truth, experience, interpretation, harm, repair, success, failure, identity, doctrine, policy, precedent, or lesson.

The field changes or responds.

New evidence, feedback, recurrence, harm, boundary stress, or time validation reveals whether the memory remains accurate.

The system faces contradiction.

The old memory may no longer match the field. The prior interpretation may be incomplete, overfit, selective, or wrong.

The system either updates or defends.

Coherent systems update memory while preserving principle integrity. Incoherent systems defend old memory as identity, ideology, tradition, authority, or certainty.

Principles either remain alive or fossilize.

Updated memory keeps principle as a living constraint field. Frozen memory turns principle into rigid narrative.

Time reveals learning integrity.

If recurrence decreases and debt falls, memory update was valid. If recurrence persists, the system did not learn.

4. When This Law Applies

This law applies whenever a system uses memory, precedent, doctrine, policy, identity, experience, tradition, data, history, model weights, institutional learning, cultural narrative, or personal interpretation to guide future action.

It is especially important when:

a principle remains the same but context changes;
feedback contradicts prior interpretation;
recurrence shows repair failure;
hidden debt returns after apparent success;
an institution repeats the same failure;
AI systems retain flawed user or world models;
governance relies on outdated legitimacy assumptions;
cultures preserve traditions after conditions change;
security systems keep old threat models;
biological systems repeat chronic patterns;
restoration systems keep applying a method after recurrence;
memory becomes identity-bound;
people say “we have always done it this way”;
evidence is reclassified to protect an old narrative.

The law applies strongly when:

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field feedback contradicts stored memory

or when:

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recurrence persists but the system claims it has learned

Typical domains:

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Domain	Memory Update Expression
AI systems	User models, safety rules, reward policies, memory systems, and representation layers must update from feedback and audit.
Security	Threat models must update from new attacks, failures, incidents, and recurrence.
Institutions	Policy memory must update from harmed-node feedback and repair outcomes.
Medicine / biology	Recovery memory must update from tolerance, recurrence, flare, and adaptation history.
Economy	Market memory must include externalized cost, instability, and hidden debt return.
Governance	Legitimacy memory must update from public trust, repair, failure, and consequence.
Culture	Tradition must remain updateable under truth, harm, and changed conditions.
Restoration	Restoration learns only when recurrence and debt outcomes update future action.

5. When This Law Does Not Apply

This law should not be used to erase stable memory, continuity, tradition, identity, or principle.

Some memory should remain stable because it preserves truth, harm history, boundary lessons, restoration proof, identity continuity, or hard-earned coherence. Updating memory does not mean rewriting the past to fit the present.

False-positive cases:

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Case	Why it is not a failure to update
A system preserves memory of harm to prevent recurrence	Stable memory protects coherence
A principle remains valid despite changing circumstances	The principle field is stable while application updates
A boundary remains closed because time validation has not changed conditions	Memory preserves safety
A tradition remains coherent under audit	Continuity can be valid
A model refuses to update from low-quality or manipulative feedback	Update requires feedback integrity

Important distinction:

Memory must be updateable, not unstable. Coherent memory preserves what remains true and revises what time disproves.

6. Diagnostic Signature

Canonical diagnostic:

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principle coherence requires updateable memory

Warning signature:

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feedback contradicts memory
recurrence persists
memory defended
principle language remains
H↑
ι↑
⇒ ideology hardening

Common indicators:

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Diagnostic	Expected movement	Interpretation
`M_update`	↑ / intact	Memory must update from feedback and time
`M_integrity`	stable / ↑	Memory must remain accurate, continuous, and non-corrupt
`M_half_life`	appropriate	Useful memory should neither vanish nor fossilize
`Ψ`	integrated	Field feedback must update memory
`Τ`	active	Time validation informs memory
`FI`	intact	Feedback quality determines update quality
`Au`	intact	Memory update must be traceable
`H`	↓ if learned	Hidden debt should fall after valid update
`recurrence`	↓	Recurrence decreases when memory learns
`ι / Ξ`	↓	Inversion decreases when memory admits contradiction
`µᵢ`	stable / ↑	Meaning integrity improves when memory matches reality
`BΣ`	clearer	Boundaries update from history without overgeneralization
`L`	↑ under audit	Legitimacy improves when the system learns visibly
`Φ`	not sufficient	Performance metrics do not prove memory update

Additional diagnostics:

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Diagnostic	Use
Memory Update Integrity	Tests whether memory changes when truth requires it
Learning Integrity	Tests whether the system actually learns
Feedback Integration	Measures whether field signals alter future behavior
Recurrence Integration	Measures whether repeated failures update memory
Memory Half-Life	Tests whether valid lessons persist
Memory Integrity	Detects corruption, erasure, or narrative rewriting
Truth Responsiveness	Tests responsiveness to evidence
Ideology Hardening	Detects frozen memory defended as principle
Identity Binding	Detects memory fused with identity claims
Temporal Proof	Validates learning over time

7. Failure Pattern

If ignored, this law produces ideology hardening, selective memory, and repeated failure.

General failure pathway:

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memory formed
→ field changes / feedback contradicts
→ memory does not update
→ old interpretation is defended
→ recurrence continues
→ H↑
→ principle becomes ideology

Common failure modes:

Frozen Memory — memory cannot update from new truth.
Ideology Hardening — principle becomes rigid narrative.
Recurrence Ignored — repeated failure does not update future action.
Feedback Non-Integration — feedback is heard but not incorporated.
Memory Corruption — records are distorted, erased, or rewritten.
Selective Memory — convenient details persist while contradictory details disappear.
Identity-Bound Memory — memory cannot change because identity depends on it.
Principle Fossilization — living principle becomes fixed doctrine.
Learning Theater — system claims learning without behavioral update.
Narrative Lock — story overrides evidence.
Audit Avoidance — memory cannot be checked.
Inversion Persistence — old inversion survives because memory protects it.
Meaning Collapse — contradiction between memory and field breaks trust.
Legitimacy Debt — authority decays because the system does not learn.

Compact failure signature:

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M frozen + Ψ ignored + recurrence↑ ⇒ H↑ + ι↑ + L↓

8. Restoration Implications

Restoration requires repairing memory update pathways.

The first restoration question is not:

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What do we remember?

The first restoration question is:

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Can memory update when truth, feedback, recurrence, and time require it?

Restoration priorities:

Identify the stored memory or interpretation.
Audit whether field feedback contradicts it.
Track recurrence and hidden debt return.
Assess whether memory is updateable or identity-bound.
Restore auditability of records and claims.
Restore feedback integration.
Separate stable truth from outdated interpretation.
Repair hidden debt created by frozen memory.
Recalibrate principle fields with updated memory.
Time-validate that future action changes.

Relevant restoration arcs:

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Restoration Arc	Why it applies
Memory Update Restoration	Reopens memory to truth and time
Learning Integrity Restoration	Converts feedback into behavioral change
Feedback Integration	Ensures field signals update future action
Recurrence Integration	Treats repeated failure as learning signal
Truth Recovery	Recovers what memory suppressed or distorted
Memory Integrity Repair	Repairs corruption, erasure, or selective record
Identity Decoupling	Allows memory to update without identity collapse
Inversion Reduction	Removes old inverted interpretations
Auditability Restoration	Makes memory traceable and reviewable
Hidden Debt Reduction	Repairs cost of frozen memory
Coherence Trajectory Recalibration	Updates future action according to learned truth
Temporal Validation	Confirms learning holds over time

Minimal restoration sequence:

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identify memory claim
→ audit against Ψ / recurrence / H_return / Τ
→ distinguish stable truth from outdated interpretation
→ update M
→ repair debt
→ recalibrate ☷ᵢ execution
→ validate behavior change over Τ

Temporal validation requirement:

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M_update intact
M_integrity stable
feedback integrated
recurrence↓
H↓
ι↓
Au intact
FI intact
principle execution changes
O stable or rising
L stable or rising

9. Design Rule

Do not let memory become ideology.

Operational design requirements:

Treat memory as evidence-bearing, not identity-proof.
Preserve truthful continuity.
Keep interpretations updateable.
Track recurrence.
Track hidden debt return.
Preserve audit trails.
Preserve affected-node feedback.
Separate what happened from what it meant.
Separate stable principle from outdated application.
Prevent narrative filters from blocking correction.
Update policy, practice, model, or boundary from validated learning.
Time-validate that memory updates change behavior.

Avoid:

defending old memory against new truth;
erasing inconvenient history;
rewriting records to preserve legitimacy;
treating tradition as proof;
treating doctrine as proof;
treating past success as future coherence;
claiming learning without behavior change;
letting identity depend on never being wrong;
treating recurrence as anomaly forever;
letting AI memory personalize without correction;
letting institutional memory exclude harmed-node evidence;
letting cultural memory preserve harm as sacred.

10. Cross-Scale Expressions

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Scale / Layer	Expression of the Law
U0 — Substrate	Biological or physical systems update from stress history, tolerance, injury, recovery, and recurrence.
U1 — Energy / capacity	Memory must track capacity depletion and recovery, not assume old capacity remains.
U2 — Boundary / interface	Boundaries update from coupling history, harm, consent, and trust.
U3 — Process / execution	Procedures must change after validated failure.
U4 — Classification / claim	Interpretations must update when field outcomes contradict them.
U5 — Time / delay	Memory is validated by time and recurrence.
U6 — Field effect	Affected-node outcomes update memory if feedback integrity holds.
U7 — Recurrence / memory	Primary layer: recurrence shows whether memory has learned.
U8 — Environment / forcing	Changed environmental conditions require memory recalibration.

11. Examples

Example A — Institutional Learning Theater

Scenario:

An institution says it has learned from harm, but the same failure pattern recurs under new language.

Law expression:

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learning claim + recurrence↑ ⇒ M_update failure

Interpretation:

The institution may have updated messaging but not operational memory.

Example B — AI User Model Drift

Scenario:

An AI system retains outdated assumptions about a user and keeps acting on them despite correction, changed preferences, or new context.

Law expression:

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AI memory + correction ignored ⇒ representation debt↑

Interpretation:

Memory must remain user-correctable, scoped, and updateable.

Example C — Security Threat Model Fossilization

Scenario:

A security team defends an old threat model while new attack patterns repeatedly bypass controls.

Law expression:

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recurrence + old model defended ⇒ security M_update failure

Interpretation:

Threat memory must update from incident reality, not past doctrine.

Example D — Cultural Tradition Under Audit

Scenario:

A tradition is defended because it is old, even though affected-node feedback and recurring harm show that conditions have changed.

Law expression:

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tradition + feedback ignored ⇒ ideology hardening

Interpretation:

Continuity is valid only when memory remains truthful and updateable.

Example E — Biological Recovery Memory

Scenario:

A recovery plan keeps using an intervention that once helped, even though current tolerance, energy state, and recurrence patterns show it now overloads the system.

Law expression:

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old recovery memory + current σ mismatch ⇒ intervention debt↑

Interpretation:

Biological memory must update from current tolerance and ring-down, not only past success.

Example F — Governance Legitimacy Assumption

Scenario:

A governance system assumes legitimacy from historical authority, while current trust, repair, participation, and consequence signals have degraded.

Law expression:

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past legitimacy memory + current L↓ ignored ⇒ legitimacy debt↑

Interpretation:

Legitimacy memory must update from current field effects.

12. Relationship to Nearby Laws

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Related Law	Relationship
LAW-001 — Coherence Priority Law	Memory updates serve coherence
LAW-002 — Coherence Trajectory Law	Memory must track trajectory, not only events
LAW-006 — Time Validation Law	Time validates or invalidates memory
LAW-008 — Recurrence Validation Law	Recurrence is a primary memory update signal
LAW-009 — U4 / U6 Truth Law	Interpretive memory must update from field truth
LAW-010 — Hidden Debt Accumulation Law	Frozen memory accumulates debt
LAW-011 — Hidden Debt Return Law	Hidden debt return updates memory if allowed
LAW-013 — Auditability-Debt Law	Memory must remain auditable
LAW-016 — Inversion Formation Law	Frozen memory preserves inversion
LAW-027 — Meaning Collapse Threshold Law	Meaning collapses when memory contradicts reality
LAW-028 — Control Density to Meaning Loss Loop	Control may prevent memory update
LAW-030 — Slack Sovereignty Law	Memory should reduce repeated slack loss
LAW-031 — Observability Collapse Law	Poor observability prevents memory update
LAW-036 — Signal Artifact Law	Memory must distinguish signal from artifact
LAW-038 — Pattern Recognition Discipline Law	Memory must update without overfitting
LAW-039 — Identity-Binding Hard Rule	Memory should not be fused to identity prematurely
LAW-048 — Feedback Integrity Law	Feedback integrity enables memory update
LAW-052 — Stability Proof Law	Stability claims require memory of perturbation outcomes
LAW-061 — Restoration Sequencing Law	Memory stores correct sequence and updates from failure
LAW-064 — Restoration Debt Reduction Law	Restoration memory is valid when debt decreases
LAW-065 — Pseudo-Restoration Law	Pseudo-restoration often relies on memory non-update
LAW-067 — Temporal Proof Law	Memory must incorporate temporal proof
LAW-083 — Normalization Shield Law	Normalized memory can shield harm from audit
LAW-085 — Principle Constraint Field Law	Principle fields require updateable memory to remain alive
LAW-086 — Principle Inversion Law	Memory update detects and reduces inversion
LAW-087 — Shadow–Light Execution Law	Execution memory must distinguish possible from permissible
LAW-088 — Empathy–Sovereignty Law	Empathic models must update from the represented node
LAW-089 — Wisdom Timing Law	Timing decisions must update from memory and feedback
LAW-091 — Archetype Localization Law	Archetypal memory must localize to current field, not fossilize
LAW-095 — Meaning Directionality Law	Meaning memory directs future action
LAW-097 — Experience–Interpretation Separation Law	Memory must separate event from interpretation
LAW-098 — Awakening Stabilization Law	High-gain experience requires memory integration
LAW-101 — Paradox Dimensionality Law	Memory must update when paradox reveals missing dimension
LAW-102 — Legitimacy Audit Law	Legitimacy depends on visible learning
LAW-111 — Meaning Audit Law	Meaning claims require memory audit
LAW-127 — AI Decision Pipeline Law	AI pipelines require updateable memory, logs, and learning paths
LAW-128 — AI Representation Law	AI representation memory must remain correctable by represented parties

Aliases folded into this law:

Memory Update Law
Updateable Memory Law
Principle Memory Law
Memory Must Update Law
Anti-Ideology Memory Law
Learning Integrity Law
Truth-Responsive Memory Law

Deduplication note:

This law should remain the root memory-update law for principles, archetypes, meaning, restoration, AI representation, and institutional learning. LAW-097 separates experience from interpretation, LAW-098 covers stabilization after high-gain openings, LAW-102 covers legitimacy learning under audit, LAW-127 covers AI decision-pipeline memory, and LAW-128 specializes represented-party memory correction.

13. Operator Mapping

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Operator	Role in this law
`Γ`	Classifies memory as valid, outdated, corrupted, selective, identity-bound, or updateable
`Π`	Provides memory gates, curation, suppression, logging, retention, and update controls
`Ξ`	Captures inversion when old memory is defended against new truth
`⊗`	Coupling history must be remembered and updated
`ℛ`	Repairs hidden debt and recalibrates action after memory update
`Τ`	Validates memory through time, recurrence, and delayed effects
`Θ`	Enables revision without collapse into shame, defensiveness, or ideology
`Σ`	Defines scope of memory update and principle recalibration
`Ψ`	Field and affected-node feedback updates memory
`Λ`	Compatibility tests whether old memory still fits current field state

Coherent operator sequence:

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Ψ(feedback) + Τ(recurrence/time) → Au/FI(validate signal) → Θ(update humility) → Γ(classify memory status) → Σ(scope update) → ℛ(repair debt / recalibrate principle) → future action changes → Τ(validate learning)

Inverted operator sequence:

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feedback contradicts memory → Γ(old memory defended) → Π(filter contradiction) → M frozen → H↑ → Ξ / ι↑ → recurrence persists → L↓

14. Machine-Readable Summary

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id: "LAW-090"
name: "Memory Update Law"
type: "law"
status: "draft"
family:
  - "Principle, Archetype, and Meaning Laws"
summary: "Principles remain coherent only when memory can update from truth, feedback, recurrence, and time."
canonical_statement: "Principles remain coherent only when memory can update."
core_form: "principle coherence requires updateable memory"
learning_form: "M_update = f(Ψ, Τ, recurrence, Au, FI, H_return)"
failure_form: "memory frozen while field changes ⇒ ideology hardening + H↑ + ι↑"
restoration_valid_form: "feedback integrated + recurrence learned + principle field recalibrated ⇒ O↑ / H↓ over Τ"
variables:
  primary:
    - "M"
    - "M_update"
    - "M_integrity"
    - "M_half_life"
    - "☷ᵢ"
    - "Ψ"
    - "Τ"
    - "FI"
    - "Au"
    - "H_return"
  secondary:
    - "O"
    - "H"
    - "ε"
    - "ι"
    - "Ξ"
    - "R"
    - "R_eff"
    - "BΣ"
    - "K"
    - "σ"
    - "µᵢ"
    - "Φ"
    - "Λ"
    - "⊗"
    - "Γ"
    - "Π"
    - "ℛ"
    - "Θ"
    - "Σ"
    - "L"
diagnostics:
  - "Memory Update Integrity"
  - "Learning Integrity"
  - "Feedback Integration"
  - "Recurrence Integration"
  - "Memory Half-Life"
  - "Memory Integrity"
  - "Truth Responsiveness"
  - "Ideology Hardening"
  - "Identity Binding"
  - "Effective Auditability"
  - "Coherence Trajectory"
  - "Temporal Proof"
failure_modes:
  - "Frozen Memory"
  - "Ideology Hardening"
  - "Recurrence Ignored"
  - "Feedback Non-Integration"
  - "Memory Corruption"
  - "Selective Memory"
  - "Identity-Bound Memory"
  - "Principle Fossilization"
  - "Learning Theater"
  - "Narrative Lock"
  - "Audit Avoidance"
  - "Inversion Persistence"
  - "Meaning Collapse"
  - "Legitimacy Debt"
restoration_arcs:
  - "Memory Update Restoration"
  - "Learning Integrity Restoration"
  - "Feedback Integration"
  - "Recurrence Integration"
  - "Truth Recovery"
  - "Memory Integrity Repair"
  - "Identity Decoupling"
  - "Inversion Reduction"
  - "Auditability Restoration"
  - "Hidden Debt Reduction"
  - "Coherence Trajectory Recalibration"
  - "Temporal Validation"
related_laws:
  - "LAW-001"
  - "LAW-002"
  - "LAW-006"
  - "LAW-008"
  - "LAW-009"
  - "LAW-010"
  - "LAW-011"
  - "LAW-013"
  - "LAW-016"
  - "LAW-027"
  - "LAW-028"
  - "LAW-030"
  - "LAW-031"
  - "LAW-036"
  - "LAW-038"
  - "LAW-039"
  - "LAW-048"
  - "LAW-052"
  - "LAW-061"
  - "LAW-064"
  - "LAW-065"
  - "LAW-067"
  - "LAW-083"
  - "LAW-085"
  - "LAW-086"
  - "LAW-087"
  - "LAW-088"
  - "LAW-089"
  - "LAW-091"
  - "LAW-095"
  - "LAW-097"
  - "LAW-098"
  - "LAW-101"
  - "LAW-102"
  - "LAW-111"
  - "LAW-127"
  - "LAW-128"
related_invariants:
  - "INV-001"
  - "INV-002"
  - "INV-006"
  - "INV-077"
  - "INV-079"
  - "INV-080"
operator_sequence:
  coherent:
    - "Ψ feedback"
    - "Τ recurrence/time"
    - "Au/FI validate signal"
    - "Θ update humility"
    - "Γ classify memory status"
    - "Σ scope update"
    - "ℛ repair debt / recalibrate principle"
    - "future action changes"
    - "Τ validate learning"
  inverted:
    - "feedback contradicts memory"
    - "Γ old memory defended"
    - "Π filter contradiction"
    - "M frozen"
    - "H↑"
    - "Ξ / ι↑"
    - "recurrence persists"
    - "L↓"
aliases:
  - "Memory Update Law"
  - "Updateable Memory Law"
  - "Principle Memory Law"
  - "Memory Must Update Law"
  - "Anti-Ideology Memory Law"
  - "Learning Integrity Law"
  - "Truth-Responsive Memory Law"
deduplication_note: "Root memory-update law for principles, archetypes, meaning, restoration, AI representation, and institutional learning. LAW-097 separates experience from interpretation, LAW-098 covers stabilization after high-gain openings, LAW-102 covers legitimacy learning under audit, LAW-127 covers AI decision-pipeline memory, and LAW-128 specializes represented-party memory correction."
source: "content/archive/laws/technical.md"

15. Compact Card Version

LAW-090 — Memory Update Law

Principles remain coherent only when memory can update.

Core form:

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principle coherence requires updateable memory

Learning form:

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M_update = f(Ψ, Τ, recurrence, Au, FI, H_return)

Plain meaning:

Failure form:

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memory frozen while field changes ⇒ ideology hardening + H↑ + ι↑

Restoration-valid form:

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feedback integrated + recurrence learned + principle field recalibrated ⇒ O↑ / H↓ over Τ

Primary variables:

M, M_update, M_integrity, M_half_life, ☷ᵢ, Ψ, Τ, FI, Au, H_return, O, H, ι, Ξ, BΣ, K, σ, µᵢ, Γ, Π, ℛ, Θ, Σ, L

Diagnostic signature:

Feedback contradicts memory, recurrence persists, hidden debt returns, but the old interpretation is defended while principle language remains and behavior does not change.

Failure risk:

Frozen memory, ideology hardening, recurrence ignored, feedback non-integration, memory corruption, selective memory, identity-bound memory, principle fossilization, learning theater, narrative lock, audit avoidance, inversion persistence, meaning collapse, legitimacy debt.

Restoration priority:

Audit memory against feedback, recurrence, hidden debt return, and time; separate stable truth from outdated interpretation; update memory; repair debt; recalibrate principle execution; and time-validate that future behavior changes.

LAW-089 — Wisdom Timing Law

Registry

LAW-091 — Archetype Localization Law