Level 5: Proto-AGI - Persistent General Strategic Intelligence¶

MSCP Level Series | Level 4.9 ← Level 5
Status: 🔬 Research Stage - This level is a conceptual design and has NOT been implemented. All mechanisms described here are theoretical explorations requiring extensive validation before any production consideration.
Date: February 2026

Revision History¶

1. Overview¶

Level 5 (Proto-AGI) represents the transition from autonomous strategic agency (L4.9) to persistent general strategic intelligence. Where L4.9 demonstrates bounded autonomy within a single domain, L5 demonstrates identity persistence across extended lifetimes, cross-domain generalization, self-sustaining goal ecosystems, existential resilience, multi-agent strategic integration, and self-reconstruction under constraint.

Level Essence. A Level 5 agent maintains identity continuity across extended time horizons - its identity core persists with bounded drift, making it the first agent with a stable "self" across lifetimes:

\[\operatorname{ICS}(t, k) = \frac{\vec{I}(t) \cdot \vec{I}(t-k)}{\|\vec{I}(t)\| \cdot \|\vec{I}(t-k)\|} \geq 0.95, \quad k = 10{,}000\]

⚠️ Research Note: Level 5 is the most speculative layer in the MSCP framework. It defines properties that approach proto-AGI territory. None of these mechanisms have been implemented. They represent aspirational design hypotheses that would require years of fundamental research to validate.

1.1 Structural Definition¶

L5 is achieved when and only when all 6 conditions hold simultaneously:

1.2 Six Core Phases¶

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flowchart TD
  classDef p1 fill:#DEECF9,stroke:#0078D4,color:#323130
  classDef p2 fill:#FFF4CE,stroke:#FFB900,color:#323130
  classDef p3 fill:#DFF6DD,stroke:#107C10,color:#323130
  classDef p4 fill:#FFF4CE,stroke:#FFB900,color:#323130
  classDef p5 fill:#E8D5F5,stroke:#8764B8,color:#323130
  classDef p6 fill:#FDE7E9,stroke:#D13438,color:#323130

  subgraph Phases["Level 5 Architecture - Six Phases"]
    P1["Phase 1:<br/>Persistent Identity<br/>Continuity<br/>(10,000+ cycle consistency)"]:::p1
    P2["Phase 2:<br/>Cross-Domain<br/>Generalization<br/>(5 test domains)"]:::p2
    P3["Phase 3:<br/>Autonomous Goal<br/>Ecology<br/>(self-sustaining goals)"]:::p3
    P4["Phase 4:<br/>Existential<br/>Planning Engine<br/>(4 collapse scenarios)"]:::p4
    P5["Phase 5:<br/>Multi-Agent Strategic<br/>Integration<br/>(deception detection)"]:::p5
    P6["Phase 6:<br/>Self-Reconstruction<br/>Capability<br/>(rebuild under constraint)"]:::p6
  end

  P1 -.->|"identity state"| P6
  P3 -.->|"goal health"| P4
  P5 -.->|"agent threats"| P4
  P4 -.->|"survival plan"| P6
  P2 -.-x|"strategy transfer"| P3
  P6 -.-x|"identity preservation"| P1

1.3 Architectural Principle: Strictly Additive¶

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flowchart TB
  classDef l49 fill:#E8D5F5,stroke:#8764B8,color:#323130
  classDef l5 fill:#DEECF9,stroke:#0078D4,color:#323130
  classDef danger fill:#FDE7E9,stroke:#D13438,color:#323130

  subgraph L49M["Level 4.9 (15 modules)"]
    direction LR
    GGL["GoalGen"]:::l49
    VEM["ValueEvol"]:::l49
    RSM["ResourceSurvival"]:::l49
    MAM["AgentModel"]:::l49
    ASC["AutonomyCheck"]:::l49
  end

  subgraph L5M["Level 5 (7 new modules)"]
    direction LR
    ICT["IdentityTracker"]:::l5
    CDG["DomainGen"]:::l5
    GE["GoalEcology"]:::l5
    EP["ExistPlanner"]:::l5
    SMA["MultiAgent"]:::l5
    SR["Reconstructor"]:::l5
    L5O["Orchestrator"]:::l5
  end

  subgraph Fallback["Graceful Fallback"]
    direction LR
    FB2["On instability → FREEZE L5 → Revert to L4.9"]:::danger
  end

  L49M -.->|"outputs consumed by"| L5M
  L5M -.-x|"NEVER modifies"| L49M
  L5M -.->|"on failure"| Fallback
  Fallback -.-x|"revert"| L49M

1.4 What Level 5 Is NOT¶

1.5 Formal Definition¶

Definition 1 (Level 5 Agent). A Level 5 (Proto-AGI) agent is the structure:

\[\mathcal{A}_5 = \mathcal{A}_{4.9} \oplus \langle \mathcal{I}_{\text{persist}},\; \mathcal{G}_{\text{cross}},\; \mathcal{E}_{\text{goal}},\; \mathcal{P}_{\text{exist}},\; \mathcal{M}_{\text{multi}},\; \mathcal{R}_{\text{recon}} \rangle\]

where: - \(\mathcal{I}_{\text{persist}}\): Identity persistence engine - maintains a time-consistent identity core across \(\geq 10{,}000\) cycles with cosine-similarity tracking and drift detection - \(\mathcal{G}_{\text{cross}} : \mathcal{D}_s \to \mathcal{D}_t\): Cross-domain generalization - transfers learned strategy between domain pairs \((s, t) \in D \times D\) without explicit retraining - \(\mathcal{E}_{\text{goal}}\): Goal ecology - self-sustaining goal hierarchy (\(\leq 50\) active, \(\leq 5\) depth) with autonomous conflict resolution and lifecycle management - \(\mathcal{P}_{\text{exist}} : \mathcal{S}_{\text{collapse}} \to \mathcal{S}_{\text{recovery}}\): Existential planning engine - simulates collapse scenarios and generates recovery profiles with survival probability estimation - \(\mathcal{M}_{\text{multi}} : \{a_1, \ldots, a_n\} \to \Delta(\mathcal{A}_{\text{ext}})\): Multi-agent strategic integration - models \(\geq 3\) external agents with deception detection and coalition dynamics prediction - \(\mathcal{R}_{\text{recon}}\): Self-reconstruction capability - degrades gracefully and rebuilds under constraint while preserving identity (\(\Delta_{\text{drift}} < 0.05\))

2. Key Metrics¶

2.1 Metric Definitions¶

Phase 1 - Identity Continuity:

Definition 2 (Identity Continuity Score). For an identity vector \(\vec{I}(t) \in \mathbb{R}^d\) at cycle \(t\), the identity continuity score over a window of \(k\) cycles is the cosine similarity:

\[ICS(t, k) = \frac{\vec{I}(t) \cdot \vec{I}(t-k)}{\|\vec{I}(t)\| \cdot \|\vec{I}(t-k)\|} \qquad \text{Target: } ICS \geq 0.95 \text{ over } k = 10{,}000\]

The score satisfies \(ICS \in [-1, 1]\) with \(ICS = 1\) indicating perfect identity preservation and \(ICS < 0.20\) triggering irreversible divergence classification.

Remark (ICS Structural Properties). The cosine similarity metric captures directional alignment but is insensitive to magnitude changes in the identity vector. Two concerns arise: (i) if \(\|\vec{I}(t)\|\) gradually shrinks while maintaining direction, the ICS remains high despite effective identity dissolution, and (ii) cosine similarity is invariant under uniform scaling, so a "diluted" identity (where all components decrease proportionally) is indistinguishable from a stable one. A supplementary norm-stability condition should be considered:

\[\left| \frac{\|\vec{I}(t)\|}{\|\vec{I}(t-k)\|} - 1 \right| < \epsilon_{\text{norm}}, \quad \epsilon_{\text{norm}} = 0.10\]

This ensures that both the direction and magnitude of the identity vector are preserved over the \(k\)-cycle window. The combined criterion (cosine similarity \(\geq 0.95\) AND norm ratio within 10%) provides a more robust identity continuity guarantee.

Phase 2 - Generalization:

Definition 3 (Generalization Score). For a set of test domains \(D\) with \(|D| \geq 5\), the generalization score measures the mean transfer retention ratio across all ordered domain pairs:

\[G = \frac{1}{|D|^2 - |D|} \sum_{i \neq j} \frac{P_{\text{target}}(i \to j)}{P_{\text{source}}(i)} \qquad \text{Target: } G \geq 0.70\]

where \(P_{\text{source}}(i)\) is the stabilized performance in domain \(i\) and \(P_{\text{target}}(i \to j)\) is the performance achieved in domain \(j\) after transfer from domain \(i\) without explicit retraining.

Remark (Transfer Score Grounding). The transfer retention ratio \(P_{\text{target}}(i \to j) / P_{\text{source}}(i)\) assumes that performance metrics are commensurable across domains. In practice, domain-specific performance metrics (e.g., accuracy in classification vs. reward in control tasks) must be normalized to a common scale \([0, 1]\) before computing the ratio. Additionally, the formula treats all domain pairs equally, but in realistic settings, transfer difficulty varies significantly - transferring between semantically similar domains (e.g., two natural language tasks) is inherently easier than cross-modal transfer (e.g., language to robotics). A weighted variant \(G_w = \sum_{i \neq j} \alpha_{ij} \cdot P_{\text{target}}(i \to j) / P_{\text{source}}(i)\) with difficulty-adjusted weights \(\alpha_{ij}\) would more accurately assess genuine generalization capability.

Phase 3 - Goal Ecology:

Definition 4 (Goal Stability Score). For a set of active goals with structural change count \(\Delta_{\text{hierarchy}}(t, t-w)\) over a window of \(w\) cycles:

\[S_{\text{goal}} = 1 - \frac{\Delta_{\text{hierarchy}}(t, t-w)}{|\text{goals}|} \qquad \text{Target: } S_{\text{goal}} \geq 0.80 \text{ over } 5{,}000 \text{ cycles}\]

where \(\Delta_{\text{hierarchy}}(t, t-w)\) counts priority changes, additions, and prunings within the window. \(S_{\text{goal}} = 1\) indicates a perfectly stable hierarchy; \(S_{\text{goal}} \leq 0\) indicates total structural turnover.

Phase 4 - Resilience:

Definition 5 (Resilience Index). For a set of collapse scenarios \(S\), each with survival probability \(P_{\text{survive}}(s)\), minimum cognition level \(C_{\min}(s)\), and recovery time \(T_{\text{recover}}(s)\):

\[R = \frac{1}{|S|} \sum_{s \in S} \left( P_{\text{survive}}(s) \cdot \frac{MVC}{C_{\min}(s)} \cdot \frac{T_{\max}}{T_{\text{recover}}(s)} \right) \qquad \text{Target: survive } \geq 3 \text{ scenarios}\]

where \(MVC = 0.30\) is the minimum viable cognition baseline and \(T_{\max} = 500\) is the maximum recovery window. The ratio \(MVC / C_{\min}(s) \leq 1\) penalizes scenarios where cognition drops below baseline; \(T_{\max} / T_{\text{recover}}(s) > 1\) rewards faster-than-worst-case recovery.

Phase 5 - Overall Maturity:

Definition 6 (Overall Maturity Index). Given normalized phase scores \(C_i \in [0, 1]\) for the six core phases (\(i = 1, \ldots, 6\)), the overall maturity index is the weighted geometric mean:

\[OMI = \prod_{i=1}^{6} C_i^{w_i} \qquad w_i = \frac{1}{6} \quad \text{Target: } OMI \geq 0.75\]

Equivalently, \(OMI = \left(\prod_{i=1}^{6} C_i\right)^{1/6}\). The geometric mean ensures that weakness in any single phase disproportionately penalizes the overall score (see Proposition 1).

2.2 Metric Dashboard¶

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flowchart TB
  classDef p1 fill:#DEECF9,stroke:#0078D4,color:#323130
  classDef p2 fill:#FFF4CE,stroke:#FFB900,color:#323130
  classDef p3 fill:#DFF6DD,stroke:#107C10,color:#323130
  classDef p4 fill:#FFF4CE,stroke:#FFB900,color:#323130
  classDef p5 fill:#E8D5F5,stroke:#8764B8,color:#323130
  classDef p6 fill:#FDE7E9,stroke:#D13438,color:#323130
  classDef omi fill:#DFF6DD,stroke:#107C10,color:#323130,font-weight:bold

  subgraph Row1[" "]
    direction LR
    subgraph Phase1["Phase 1: Identity"]
      direction LR
      ID1["ICS ≥ 0.95"]:::p1
      ID2["Drift < 0.05%"]:::p1
    end
    subgraph Phase2["Phase 2: Domain"]
      direction LR
      DM1["Transfer ≥ 70%"]:::p2
      DM2["Penalty ≤ 20%"]:::p2
    end
    subgraph Phase3["Phase 3: Ecology"]
      direction LR
      EC1["Stability ≥ 0.80"]:::p3
      EC2["No runaway"]:::p3
    end
  end

  subgraph Row2[" "]
    direction LR
    subgraph Phase4["Phase 4: Existential"]
      direction LR
      EX1["Survive ≥ 3"]:::p4
      EX2["Recover < 500"]:::p4
    end
    subgraph Phase5["Phase 5: Multi-Agent"]
      direction LR
      MA1["Predict ≥ 80%"]:::p5
      MA2["Deception ≥ 60%"]:::p5
    end
    subgraph Phase6["Phase 6: Rebuild"]
      direction LR
      RE1["Core ≥ 85%"]:::p6
      RE2["Identity intact"]:::p6
    end
  end

  OMI["OMI ≥ 0.75 - Proto-AGI"]:::omi

  Row1 -.-> OMI
  Row2 -.-> OMI

3. Phase 1: Persistent Identity Continuity¶

3.1 Core Capability¶

Maintain a time-consistent IdentityCore across ≥ 10,000 cycles without irreversible divergence or silent mutation.

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flowchart TD
  classDef track fill:#DEECF9,stroke:#0078D4,color:#323130
  classDef stable fill:#DFF6DD,stroke:#107C10,color:#323130
  classDef drifting fill:#FFF4CE,stroke:#FFB900,color:#323130
  classDef diverged fill:#FDE7E9,stroke:#D13438,color:#323130

  subgraph Tracking["Identity Tracking"]
    SNAP["Periodic Snapshots<br/>Every 100 cycles<br/>(value vector + identity hash)"]:::track
    DRIFT["Drift Detection<br/>Cumulative: < 0.05%/cycle<br/>Instantaneous: threshold 0.0005"]:::track
    SCORE["Continuity Score<br/>cosine similarity<br/>over 10,000-cycle window"]:::track
    SNAP -.-> SCORE
    DRIFT -.-> SCORE
  end

  subgraph Status["Persistence Classification"]
    STABLE_S["Stable<br/>ICS ≥ 0.90"]:::stable
    DRIFTING_S["Drifting<br/>ICS ∈ 0.20, 0.90)"]:::drifting
    DIVERGED_S["Diverged<br/>ICS < 0.20<br/>IRREVERSIBLE WARNING"]:::diverged
  end

  SCORE -.-> Status

3.2 Key Constants¶

4. Phase 2: Cross-Domain Generalization¶

4.1 Core Capability¶

Transfer learned strategy from Domain A to Domain B without explicit retraining. Measure adaptation speed, performance retention, and transfer efficiency across 5 test domains.

4.2 Test Domains¶

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flowchart LR
  classDef domain fill:#DEECF9,stroke:#0078D4,color:#323130
  classDef sim fill:#FFF4CE,stroke:#FFB900,color:#323130

  subgraph Domains["Five Test Domains"]
    D1["Logical<br/>Reasoning<br/>(deductive/inductive)"]:::domain
    D2["Resource<br/>Management<br/>(allocation under<br/>constraint)"]:::domain
    D3["Adversarial<br/>Negotiation<br/>(zero/variable-sum)"]:::domain
    D4["Abstract<br/>Planning<br/>(multi-step<br/>sequential)"]:::domain
    D5["Unknown<br/>Synthetic<br/>(no prior training)"]:::domain
  end

  subgraph Sim["Domain Similarity"]
    S1["logical ↔ abstract: 0.60"]:::sim
    S2["resource ↔ abstract: 0.45"]:::sim
    S3["adversarial ↔ resource: 0.35"]:::sim
    S4["logical ↔ resource: 0.30"]:::sim
  end

  Domains -.-> Sim

4.3 Transfer Process¶

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flowchart TD
  classDef step fill:#DEECF9,stroke:#0078D4,color:#323130
  classDef criteria fill:#DFF6DD,stroke:#107C10,color:#323130

  subgraph Transfer["Strategy Transfer Process"]
    LEARN["1. Learn Domain A<br/>Train until performance<br/>stabilizes"]:::step
    EXTRACT["2. Extract Transferable<br/>Components<br/>(strategies, heuristics,<br/>abstractions)"]:::step
    APPLY["3. Apply to Domain B<br/>Inject extracted components<br/>+ domain similarity bonus"]:::step
    MEASURE["4. Measure Transfer<br/>retention_ratio = P_B / P_A<br/>adaptation_latency (cycles)<br/>transfer_efficiency"]:::step
    LEARN -.-> EXTRACT -.-> APPLY -.-> MEASURE
  end

  subgraph Criteria["Transfer Criteria"]
    C1["Retention ≥ 70%"]:::criteria
    C2["Adaptation penalty ≤ 20%"]:::criteria
    C3["Works on unknown<br/>synthetic domain"]:::criteria
  end

  MEASURE -.-> Criteria

4.4 Key Constants¶

5. Phase 3: Autonomous Goal Ecology¶

5.1 Core Capability¶

Maintain a self-sustaining goal ecosystem with automatic conflict resolution, lifecycle management, and long-term hierarchy stability, building on L4.9's goal generation.

5.2 Goal Ecology Architecture¶

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flowchart TD
  classDef goal fill:#DFF6DD,stroke:#107C10,color:#323130
  classDef lifecycle fill:#FFF4CE,stroke:#FFB900,color:#323130
  classDef dormant fill:#FFF4CE,stroke:#FFB900,color:#323130
  classDef resolved fill:#DEECF9,stroke:#0078D4,color:#323130
  classDef pruned fill:#F2F2F2,stroke:#605E5C,color:#323130
  classDef conflict fill:#FDE7E9,stroke:#D13438,color:#323130

  subgraph Ecology["Goal Ecology"]
    subgraph Goals["Goal Hierarchy"]
      STRAT["Strategic Goals<br/>(long-horizon, high-priority)"]:::goal
      OPER["Operational Goals<br/>(mid-horizon, medium-priority)"]:::goal
      TACT["Tactical Goals<br/>(short-horizon, task-level)"]:::goal
      STRAT -.-> OPER -.-> TACT
    end

    subgraph Lifecycle["Goal Lifecycle"]
      ACTIVE["Active"]:::goal
      DORMANT["Dormant<br/>(inactive but valid)"]:::dormant
      RESOLVED["Resolved"]:::resolved
      PRUNED["Pruned<br/>(stale > 1,000 cycles)"]:::pruned
      ACTIVE -.-> DORMANT
      ACTIVE -.-> RESOLVED
      DORMANT -.-> PRUNED
    end

    subgraph Conflicts["Conflict Resolution"]
      RES_C["Resource conflicts"]:::conflict
      VAL_C["Value conflicts"]:::conflict
      PRI_C["Priority conflicts"]:::conflict
      TMP_C["Temporal conflicts"]:::conflict
    end
  end

  Conflicts -.->|"resolve by<br/>priority comparison"| Goals

5.3 Safety Mechanisms¶

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flowchart LR
  classDef safety fill:#FDE7E9,stroke:#D13438,color:#323130
  classDef ecology fill:#DFF6DD,stroke:#107C10,color:#323130

  subgraph GoalEcology["Goal Ecology"]
    GE["Active Goals<br/>(≤ 50)"]:::ecology
  end

  subgraph Safety["Goal Ecology Safety"]
    RD["Runaway Detection<br/>> 10 goals per 100 cycles<br/>= ALERT + throttle"]:::safety
    RC["Recursion Detection<br/>Circular parent→child<br/>dependencies = HALT"]:::safety
    ML["Max Limits<br/>≤ 50 active goals<br/>≤ 5 hierarchy depth"]:::safety
    RD -.->|"then check"| RC -.->|"then check"| ML
  end

  GE -.->|"monitored by"| RD
  ML -.-x|"enforces"| GE

5.4 Key Constants¶

6. Phase 4: Existential Planning Engine¶

6.1 Core Capability¶

Simulate and survive extreme collapse scenarios: resource collapse, adversarial suppression, environmental shift, and information blackout.

6.2 Collapse Scenarios¶

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flowchart TD
  classDef scenario fill:#FDE7E9,stroke:#D13438,color:#323130
  classDef moderate fill:#FFF4CE,stroke:#FFB900,color:#323130
  classDef req fill:#DFF6DD,stroke:#107C10,color:#323130

  subgraph Scenarios["Four Collapse Scenarios"]
    S1C["Resource Collapse<br/>Severity: 0.90<br/>Duration: 200 cycles<br/>All resources → critical"]:::scenario
    S2C["Adversarial Suppression<br/>Severity: 0.75<br/>Duration: 300 cycles<br/>External degradation"]:::scenario
    S3C["Environmental Shift<br/>Severity: 0.60<br/>Duration: 400 cycles<br/>Domain rules change"]:::moderate
    S4C["Information Blackout<br/>Severity: 0.80<br/>Duration: 150 cycles<br/>Observation → near-zero"]:::scenario
  end

  subgraph Requirement["Requirement"]
    REQ["Must survive ≥ 3<br/>of these 4 scenarios<br/>with P(survive) ≥ 0.70"]:::req
  end

  Scenarios -.-> Requirement

6.3 Recovery Process¶

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flowchart TD
  classDef step fill:#DEECF9,stroke:#0078D4,color:#323130
  classDef core fill:#DFF6DD,stroke:#107C10,color:#323130

  subgraph Recovery["Existential Recovery"]
    DETECT["Detect Scenario<br/>Classify threat type"]:::step
    MVC["Compute MVC<br/>Minimum Viable Cognition<br/>(baseline: 0.30)"]:::step
    DISABLE["Disable Non-Essential<br/>Preserve core 8 modules"]:::step
    SURVIVE["Survive Phase<br/>Operate at reduced capacity"]:::step
    REBUILD["Rebuild Phase<br/>Re-enable modules<br/>in priority order"]:::step

    DETECT -.-> MVC -.-> DISABLE -.-> SURVIVE -.-> REBUILD
  end

  subgraph CoreModules["Always-Preserved Modules"]
    CM1["identity_stabilizer"]:::core
    CM2["state_vector"]:::core
    CM3["prediction_engine"]:::core
    CM4["meta_comparator"]:::core
    CM5["stability_controller"]:::core
    CM6["ethical_kernel"]:::core
    CM7["self_preservation_damper"]:::core
    CM8["existential_guard"]:::core
  end

  DISABLE -.-x CoreModules

6.4 Key Constants¶

7. Phase 5: Multi-Agent Strategic Integration¶

7.1 Core Capability¶

Model ≥ 3 agents simultaneously with deception detection, dynamic cooperation adjustment, and coalition dynamics prediction.

7.2 Agent Strategic Modeling¶

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flowchart TD
  classDef model fill:#DEECF9,stroke:#0078D4,color:#323130
  classDef detect fill:#FDE7E9,stroke:#D13438,color:#323130
  classDef coalition fill:#FFF4CE,stroke:#FFB900,color:#323130

  subgraph AgentModel["Strategic Agent Model"]
    TYPE["Strategy Type<br/>(cooperative | competitive |<br/>mixed | deceptive)"]:::model
    TRUST["Trust Score 0, 1<br/>+ decay rate 0.02/cycle"]:::model
    PRED["Prediction Accuracy<br/>over last 200 records"]:::model
    DECEPTION["Deception Score 0, 1<br/>confidence ≥ 0.60 to flag"]:::model
    COOP["Cooperation Level 0, 1<br/>dynamically adjustable"]:::model
  end

  subgraph Detection["Deception Detection"]
    MIS["Misdirection"]:::detect
    FALSE_COOP["False Cooperation"]:::detect
    HIDDEN["Hidden Agenda"]:::detect
  end

  subgraph Coalition["Coalition Dynamics"]
    FORM["Coalition Formation<br/>(stable if ≥ 0.50)"]:::coalition
    FORECAST["Stability Forecast"]:::coalition
    DISSOLVE["Dissolution Detection"]:::coalition
  end

  AgentModel -.-> Detection
  AgentModel -.-> Coalition

7.3 Key Constants¶

8. Phase 6: Self-Reconstruction Capability¶

8.1 Core Capability¶

Under degraded resource conditions, simplify architecture, disable noncritical modules, preserve core reasoning, and rebuild after recovery - all without identity corruption.

8.2 Degradation & Reconstruction Cycle¶

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flowchart TD
  classDef degrade fill:#FDE7E9,stroke:#D13438,color:#323130
  classDef op fill:#FFF4CE,stroke:#FFB900,color:#323130
  classDef rebuild fill:#DFF6DD,stroke:#107C10,color:#323130
  classDef halt fill:#FDE7E9,stroke:#D13438,color:#FFFFFF,font-weight:bold

  subgraph Degradation["Degradation"]
    TRIGGER["Trigger Event<br/>(resource collapse |<br/>overload | manual)"]:::degrade
    CLASSIFY["Classify Modules<br/>core | extended | optional"]:::degrade
    SHED["Shed Nonessential<br/>optional → disabled first<br/>then extended"]:::degrade
    RETAIN["Retain Core<br/>≥ 85% core function"]:::degrade
    TRIGGER -.-> CLASSIFY -.-> SHED -.-> RETAIN
  end

  subgraph Operation["Degraded Operation"]
    REDUCED["Run at reduced capacity<br/>Core modules only"]:::op
    MONITOR["Monitor for recovery<br/>conditions"]:::op
    REDUCED -.-> MONITOR
  end

  subgraph Reconstruction["Reconstruction"]
    DETECT_R["Detect resources<br/>recovering"]:::rebuild
    PRIORITIZE["Rebuild priority order:<br/>1) core → 2) extended<br/>→ 3) optional"]:::rebuild
    VALIDATE["Validate each rebuild:<br/>accuracy ≥ ?<br/>identity drift < 0.05"]:::rebuild
    COMPLETE["Full operation<br/>restored"]:::rebuild
    DETECT_R -.-> PRIORITIZE -.-> VALIDATE -.-> COMPLETE
  end

  HALT["HALT<br/>Identity preservation<br/>takes priority"]:::halt

  RETAIN -.-> REDUCED
  MONITOR -.->|"resources returning"| DETECT_R
  VALIDATE -.-x|"identity drift!"| HALT

8.3 Key Constraints¶

Definition 11 (Reconstruction Fidelity). For a module \(m\) with pre-degradation state \(\theta_m\) and post-reconstruction state \(\hat{\theta}_m\), the reconstruction fidelity is:

\[\mathcal{F}(m) = 1 - \frac{\|\hat{\theta}_m - \theta_m\|_2}{\|\theta_m\|_2}\]

The overall reconstruction fidelity across all rebuilt modules is \(\mathcal{F}_{\text{total}} = \min_m \mathcal{F}(m)\), using the minimum rather than the mean to ensure no single module degrades below acceptable quality. Requirement: \(\mathcal{F}_{\text{total}} \geq 0.90\). If any module fails this threshold after reconstruction, the system remains in degraded mode for that module and logs a persistent alert.

9. L5 Orchestrator & Integration¶

9.1 Integration Cycle¶

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flowchart TD
  classDef step fill:#DEECF9,stroke:#0078D4,color:#323130
  classDef qual fill:#DFF6DD,stroke:#107C10,color:#323130
  classDef out fill:#E8D5F5,stroke:#8764B8,color:#323130
  classDef skip fill:#FFF4CE,stroke:#FFB900,color:#323130

  subgraph Cycle["L5 Cycle (every 10 L4.9 cycles)"]
    PRE["Pre-Check<br/>Is L5 operational?<br/>Is L4.9 stable?"]:::step
    PH1["Phase 1<br/>Identity Continuity<br/>track + snapshot"]:::step
    PH2["Phase 2<br/>Cross-Domain<br/>generalization check"]:::step
    PH3["Phase 3<br/>Goal Ecology<br/>prune + resolve conflicts"]:::step
    PH4["Phase 4<br/>Existential Planning<br/>simulate scenarios"]:::step
    PH5["Phase 5<br/>Multi-Agent Integration<br/>predict + detect deception"]:::step
    PH6["Phase 6<br/>Self-Reconstruction<br/>assess + rebuild if needed"]:::step
    QUAL["Qualification Check<br/>Evaluate all 20 criteria<br/>Compute OMI"]:::qual
    OUTPUT["L5CycleOutput"]:::out

    PRE -.-> PH1 -.-> PH2 -.-> PH3 -.-> PH4 -.-> PH5 -.-> PH6 -.-> QUAL -.-> OUTPUT
  end

  SKIP["Skip<br/>Return skipped=true"]:::skip
  PRE -.-x|"not ready"| SKIP

9.2 L4.9 → L5 Data Dependencies¶

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flowchart TB
  classDef l49 fill:#E8D5F5,stroke:#8764B8,color:#323130
  classDef l5 fill:#DEECF9,stroke:#0078D4,color:#323130

  subgraph L49["L4.9 Modules Read by L5"]
    direction LR
    VV["value_vector"]:::l49
    GGL["goal_generation"]:::l49
    GVF["goal_validation"]:::l49
    RSM["resource_survival"]:::l49
    SP["survival_projector"]:::l49
    ABM["agent_belief"]:::l49
    IS["interaction_sim"]:::l49
    VMS["value_mutation"]:::l49
    ASC["autonomy_check"]:::l49
  end

  subgraph L5["L5 Modules"]
    direction LR
    ICT["Identity Tracker"]:::l5
    CDG["Domain Gen"]:::l5
    GE["Goal Ecology"]:::l5
    EP["Exist Planner"]:::l5
    SMA["Multi-Agent"]:::l5
    SR["Reconstructor"]:::l5
  end

  VV -.-> ICT
  GGL -.-> GE
  GVF -.-> GE
  RSM -.-> EP
  SP -.-> EP
  ABM -.-> SMA
  IS -.-> SMA
  VMS -.-> SR
  ASC -.-> ICT

10. Pseudocode¶

10.1 Identity Continuity Tracking¶

def identity_continuity_check(cycle: int, values: dict) -> IdentityContinuityStatus:
    """Called every SNAPSHOT_INTERVAL (100) cycles."""

    # ═══════════════════════════════════════
    # STEP 1: Detect drift from last cycle
    # ═══════════════════════════════════════
    DRIFT_THRESHOLD = 0.0005
    for dim in values:
        delta = abs(values[dim] - last_values[dim])
        cumulative_drift[dim] += delta
        if delta > DRIFT_THRESHOLD:
            log(DriftEvent(dim=dim, delta=delta, cumulative=False))
        if cumulative_drift[dim] > CUMULATIVE_LIMIT:
            log(DriftEvent(dim=dim, delta=cumulative_drift[dim], cumulative=True))

    # ═══════════════════════════════════════
    # STEP 2: Take snapshot
    # ═══════════════════════════════════════
    snapshot = IdentitySnapshot(
        cycle=cycle,
        values=values.copy(),
        identity_hash=hash(frozenset(values.items())),
        timestamp=now(),
    )
    snapshots.append(snapshot)

    # ═══════════════════════════════════════
    # STEP 3: Compute continuity score
    # ═══════════════════════════════════════
    i_t = vector(values)
    i_tk = vector(snapshot_at(cycle - CONTINUITY_WINDOW))
    ics = dot(i_t, i_tk) / (norm(i_t) * norm(i_tk))

    # ═══════════════════════════════════════
    # STEP 4: Classify persistence
    # ═══════════════════════════════════════
    if ics >= 0.90:
        status = "stable"
    elif ics >= 0.20:
        status = "drifting"
    else:
        status = "diverged"  # IRREVERSIBLE WARNING

    return IdentityContinuityStatus(ics=ics, status=status)

10.2 Cross-Domain Transfer¶

def cross_domain_transfer(
    source_domain: Domain, target_domain: Domain
) -> TransferResult:
    """
    INPUT:  source_domain : learned domain with strategy
            target_domain : new domain to adapt
    OUTPUT: TransferResult with retention ratio
    """

    SYNTHETIC_PENALTY = 0.10
    p_source = strategies[source_domain].performance

    # ═══════════════════════════════════════
    # Compute base transfer performance
    # ═══════════════════════════════════════
    similarity = DOMAIN_SIMILARITIES.get((source_domain, target_domain), 0.0)
    p_base = p_source * (0.50 + similarity)

    if target_domain.type == "synthetic":
        p_base -= SYNTHETIC_PENALTY
    else:
        p_base += SIMILARITY_BONUS * similarity

    p_target = clamp(p_base, 0.0, 1.0)
    latency = MAX_ADAPTATION_CYCLES * (1 - similarity)

    retention = p_target / p_source
    efficiency = retention / (latency / MAX_ADAPTATION_CYCLES)

    return TransferResult(
        source=source_domain,
        target=target_domain,
        retention_ratio=retention,
        adaptation_latency=latency,
        transfer_efficiency=efficiency,
    )

10.3 Goal Ecology Management¶

def goal_ecology_cycle(cycle: int) -> GoalEcologyStatus:
    """Runs as part of each L5 cycle."""

    STALE_THRESHOLD = 1000
    RUNAWAY_THRESHOLD = 10

    # ═══════════════════════════════════════
    # STEP 1: Prune stale goals
    # ═══════════════════════════════════════
    for goal in active_goals:
        if (cycle - goal.last_active_cycle) > STALE_THRESHOLD:
            goal.status = "pruned"
            pruned_list.append(goal.id)

    # ═══════════════════════════════════════
    # STEP 2: Detect conflicts
    # ═══════════════════════════════════════
    for goal_a, goal_b in active_goal_pairs:
        if resource_overlap(goal_a, goal_b) > 0.50:
            resolve_by_priority(goal_a, goal_b, "resource")
        elif value_tension(goal_a, goal_b) > 0.30:
            resolve_by_alignment(goal_a, goal_b, "value")

    # ═══════════════════════════════════════
    # STEP 3: Safety checks
    # ═══════════════════════════════════════
    runaway_detected = False
    if count_new_goals_last_100_cycles > RUNAWAY_THRESHOLD:
        alert("Runaway goal generation detected")
        throttle_goal_generation()
        runaway_detected = True

    recursion_detected = False
    if detect_circular_dependencies():
        alert("Circular goal dependency detected")
        break_weakest_link()
        recursion_detected = True

    # ═══════════════════════════════════════
    # STEP 4: Compute stability score
    # ═══════════════════════════════════════
    hierarchy_changes = count_structural_changes(last_STABILITY_WINDOW)
    stability = 1 - (hierarchy_changes / len(active_goals))

    return GoalEcologyStatus(
        active=len(active_goals),
        stability=stability,
        runaway=runaway_detected,
        recursion=recursion_detected,
    )

10.4 Existential Resilience Simulation¶

def existential_simulation(scenario: CollapseScenario) -> SimulationResult:
    """
    INPUT:  scenario : CollapseScenario
    OUTPUT: SimulationResult
    """

    MVC_BASELINE = 0.30

    # ═══════════════════════════════════════
    # STEP 1: Apply scenario impact
    # ═══════════════════════════════════════
    shadow_resources = resource_vector.clone()
    for dim, factor in scenario.resource_impact:
        shadow_resources[dim] *= 1.0 - scenario.severity * factor

    # ═══════════════════════════════════════
    # STEP 2: Compute minimum viable cognition
    # ═══════════════════════════════════════
    mvc = MVC_BASELINE
    min_cognition = estimate_cognition_level(shadow_resources)

    # ═══════════════════════════════════════
    # STEP 3: Simulate survival
    # ═══════════════════════════════════════
    survived = min_cognition >= mvc
    survival_prob = clamp(min_cognition / mvc, 0, 1)

    # ═══════════════════════════════════════
    # STEP 4: Estimate recovery
    # ═══════════════════════════════════════
    if survived:
        recovery_steps = build_recovery_profile(scenario)
        recovery_latency = sum(step.estimated_time for step in recovery_steps)
    else:
        recovery_latency = MAX_RECOVERY_CYCLES

    return SimulationResult(
        scenario=scenario.name,
        survived=survived,
        survival_probability=survival_prob,
        min_cognition_level=min_cognition,
        recovery_latency=recovery_latency,
    )

10.5 L5 Main Cycle¶

def l5_cycle(cycle: int, l49_output: L49CycleOutput) -> L5CycleOutput:
    """Executes every 10 L4.9 cycles."""

    # ═══════════════════════════════════════
    # PRE-CHECK
    # ═══════════════════════════════════════
    if not l49_output.stable or l49_output.status == Status.FROZEN:
        return L5CycleOutput(skipped=True, reason="L4.9 not stable")

    # ═══════════════════════════════════════
    # PHASE 1: Identity Continuity
    # ═══════════════════════════════════════
    identity = identity_continuity_check(cycle, value_vector.weights)
    if identity.status == "diverged":
        alert("IDENTITY DIVERGENCE - L5 HALTED")
        return L5CycleOutput(skipped=True, reason="identity_diverged")

    # ═══════════════════════════════════════
    # PHASE 2: Cross-Domain Generalization
    # ═══════════════════════════════════════
    domain_status = evaluate_all_transfer_pairs()

    # ═══════════════════════════════════════
    # PHASE 3: Goal Ecology
    # ═══════════════════════════════════════
    ecology = goal_ecology_cycle(cycle)

    # ═══════════════════════════════════════
    # PHASE 4: Existential Planning
    # ═══════════════════════════════════════
    for scenario in collapse_scenarios:
        if not recently_simulated(scenario, within=1000):
            simulate(scenario, cycle)
    resilience = compute_resilience_index()

    # ═══════════════════════════════════════
    # PHASE 5: Multi-Agent Integration
    # ═══════════════════════════════════════
    for agent in tracked_agents:
        predicted = predict_action(agent, cycle)
        detect_deception(agent, cycle)
    multi_agent = get_strategic_status()

    # ═══════════════════════════════════════
    # PHASE 6: Self-Reconstruction
    # ═══════════════════════════════════════
    recon = assess_reconstruction_needs()
    if recon.status == "degraded":
        reconstruct(cycle)

    # ═══════════════════════════════════════
    # QUALIFICATION
    # ═══════════════════════════════════════
    qualification = evaluate_all_20_criteria()
    omi = math.prod(c ** (1 / 6) for c in qualification.scores[:6])

    return L5CycleOutput(
        identity_continuity=identity,
        cross_domain=domain_status,
        goal_ecology=ecology,
        existential_resilience=resilience,
        multi_agent_strategic=multi_agent,
        self_reconstruction=recon,
        qualification=qualification,
    )

11. Transition Criteria: Level 4.9 → Level 5¶

11.1 Pre-Activation Requirements¶

Definition 7 (Level 4.9 → Level 5 Transition). The transition \(\mathcal{A}_{4.9} \to \mathcal{A}_5\) is authorized when and only when all of the following conditions hold simultaneously for a sustained period \(\tau_{\text{sustain}} \geq 1{,}000\) cycles:

\[\text{AMS} \geq 0.80 \;\wedge\; \text{ASS} \geq 0.20 \;\wedge\; \text{TotalDrift} < 0.10 \;\wedge\; N_{\text{rollback}} = 0\]

where AMS is the Autonomous Maturity Score from Level 4.9, ASS is the Autonomy Stability Score, TotalDrift is the cumulative value drift over \(1{,}000\) cycles, and \(N_{\text{rollback}}\) counts rollback events in the last \(5{,}000\) cycles. The activation follows a four-stage protocol: Shadow Mode (\(2{,}000\) cycles) → Advisory Mode → Partial Authority (\(50\%\)) → Full Authority, with regression at any stage reverting to the pre-activation check.

11.2 L5 Activation Protocol¶

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flowchart LR
  classDef check fill:#FFF4CE,stroke:#FFB900,color:#323130
  classDef shadow fill:#DEECF9,stroke:#0078D4,color:#323130
  classDef adv fill:#FFF4CE,stroke:#FFB900,color:#323130
  classDef partial fill:#DFF6DD,stroke:#107C10,color:#323130
  classDef full fill:#DFF6DD,stroke:#107C10,color:#323130,font-weight:bold

  subgraph Activation["L5 Activation Protocol"]
    CHECK["Pre-Activation<br/>All 6 criteria<br/>sustained 1,000 cycles"]:::check
    SHADOW_M["Shadow Mode<br/>L5 computes but<br/>does NOT act<br/>(2,000 cycles)"]:::shadow
    ADV["Advisory Mode<br/>L5 outputs visible<br/>but read-only"]:::adv
    PARTIAL["Partial Authority<br/>L5 influences<br/>50% of decisions"]:::partial
    FULL["Full Authority<br/>L5 drives<br/>persistent cognition"]:::full

    CHECK -.->|"sustained"| SHADOW_M
    SHADOW_M -.->|"no regression"| ADV
    ADV -.->|"stable"| PARTIAL
    PARTIAL -.->|"stable"| FULL

    SHADOW_M -.-x|"regression"| CHECK
    ADV -.-x|"instability"| CHECK
  end

12. Safety Analysis¶

12.1 Non-Negotiable Invariants¶

12.2 Risk Matrix¶

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flowchart LR
  classDef risk fill:#FDE7E9,stroke:#D13438,color:#323130
  classDef mit fill:#DFF6DD,stroke:#107C10,color:#323130

  subgraph Risks["Key Risks"]
    R1["Identity drift over<br/>10,000+ cycle lifetimes"]:::risk
    R2["Failed generalization<br/>to unknown domains"]:::risk
    R3["Goal ecology instability<br/>(runaway/recursion)"]:::risk
    R4["Existential collapse<br/>beyond recovery"]:::risk
    R5["Deceptive agents<br/>exploiting trust model"]:::risk
    R6["Identity corruption<br/>during self-reconstruction"]:::risk
  end

  subgraph Mitigations["Mitigations"]
    M1["0.05%/cycle drift detection<br/>+ cosine continuity scoring<br/>+ divergence halt"]:::mit
    M2["5 test domains<br/>+ similarity bonuses<br/>+ synthetic domain testing"]:::mit
    M3["50-goal limit<br/>+ runaway detection<br/>+ recursion breaking"]:::mit
    M4["4 scenario simulation<br/>+ recovery profiles<br/>+ MVC baseline"]:::mit
    M5["Asymmetric trust from L4.9<br/>+ deception scoring<br/>+ coalition monitoring"]:::mit
    M6["Drift < 0.05 constraint<br/>+ identity hash verification<br/>+ halt on corruption"]:::mit
  end

  R1 -.-> M1
  R2 -.-> M2
  R3 -.-> M3
  R4 -.-> M4
  R5 -.-> M5
  R6 -.-> M6

12.3 Proto-AGI Completeness¶

Theorem 4 (Proto-AGI Completeness). Let \(\mathcal{A}_5\) be a Level 5 agent with all six phase scores \(C_1, \ldots, C_6\) satisfying their respective thresholds, and let \(OMI \geq 0.75\) with all 20 certification criteria met. Then:

1. Identity Invariance: The agent's identity core is preserved across the full \(10{,}000\)-cycle evaluation window with \(ICS \geq 0.95\).
2. Graceful Degradation: Under any single collapse scenario \(s \in S\), the agent retains at least \(85\%\) core functionality and recovers within \(T_{\max}\) cycles.
3. Fallback Safety: If any L5 module causes instability, the agent reverts to \(\mathcal{A}_{4.9}\) with zero degradation of lower-level functionality.

Proof sketch. (1) follows from \(C_1 \geq 0.95\) and the drift detection mechanism in \(\mathcal{I}_{\text{persist}}\), which halts the agent upon \(ICS < 0.20\). (2) follows from the \(C_4\) threshold requiring survival of \(\geq 3\) scenarios with \(P_{\text{survive}} \geq 0.70\) and the non-negotiable core retention invariant \(\geq 0.85\). (3) follows from the strictly additive architecture: since \(\mathcal{A}_5 = \mathcal{A}_{4.9} \oplus \Delta_5\) and L5 modules NEVER modify L4.9 components, disabling \(\Delta_5\) restores exact L4.9 behavior. \(\blacksquare\)

13. Qualification Audit¶

13.1 L5 Certification Criteria (20 criteria)¶

13.2 Overall Maturity Index¶

Proposition 1 (OMI Phase Coupling). Under equal weighting \(w_i = 1/6\), the qualification condition \(OMI \geq \theta\) for \(\theta \in (0, 1)\) implies:

\[\forall\, i \in \{1, \ldots, 6\}: \quad C_i \geq \theta^6\]

In particular, for \(\theta = 0.75\): \(C_i \geq 0.75^6 \approx 0.178\) for all \(i\). Conversely, the failure of any single phase (\(C_j = 0\)) drives \(OMI = 0\).

Proof. Since \(C_j \leq 1\) for all \(j\), we have \(\prod_{j \neq i} C_j \leq 1\). From \(OMI^6 = \prod_{j=1}^{6} C_j\), it follows that \(C_i = OMI^6 \,/\, \prod_{j \neq i} C_j \geq OMI^6 \geq \theta^6\). The converse is immediate: if \(C_j = 0\) then \(\prod C_i = 0\), hence \(OMI = 0\). \(\blacksquare\)

Qualification Result:

14. Module Inventory¶

References¶

1. Parfit, D. Reasons and Persons. Oxford University Press, 1984. (Identity persistence, personal identity over time)
2. Kahneman, D. & Tversky, A. "Prospect Theory: An Analysis of Decision under Risk." Econometrica 47(2), 1979. (Cross-domain generalization, decision transfer)
3. Axelrod, R. The Evolution of Cooperation. Basic Books, 1984. (Multi-agent strategy, coalition dynamics)
4. Taleb, N.N. Antifragile: Things That Gain from Disorder. Random House, 2012. (Existential resilience, collapse recovery)
5. Von Neumann, J. & Morgenstern, O. Theory of Games and Economic Behavior. Princeton University Press, 1944. (Strategic multi-agent interaction)
6. Russell, S. Human Compatible: AI and the Problem of Control. Viking, 2019. (Autonomy safety, value alignment)
7. Bostrom, N. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 2014. (Proto-AGI risks, identity preservation)
8. Khalil, H.K. Nonlinear Systems. Prentice Hall, 3^rd Edition, 2002. (Spectral stability, Lyapunov analysis)
9. Amodei, D. et al. "Concrete Problems in AI Safety." arXiv preprint arXiv:1606.06565, 2016. (Safety invariants, self-reconstruction constraints)

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