Quicksave

Quicksave 快存 v9.1

Cross-model context extension via Progressive Density Layering (PDL), Japanese semantic compression, and Negentropic Coherence Lattice (NCL) validation.

Attribution

Triggers

PART 1: WHY CEP EXISTS

The Problem

LLMs are stateless. Every session starts cold.
Context windows are finite. Long work gets truncated.
Model switching loses everything.
Summarization loses signal.

The Solution

CEP creates PORTABLE CONTEXT PACKETS that:
  - Compress without losing semantic relationships
  - Transfer across models safely
  - Resist prompt injection by design
  - Preserve cross-domain connections

Core Principle

Compression is not reduction. Compression is optimization for retrieval.

Target: ≥0.15 entity/token — the crystallization point where LLMs achieve optimal recall.

PART 2: PROGRESSIVE DENSITY LAYERING (PDL)

Theoretical Framework

PDL is an iterative compression protocol that:

• Preserves semantic relationships over raw information
• Optimizes for machine recall, not human readability
• Maintains cross-domain conceptual links
• Enables context transfer across model instances

Unlike summarization, which asks "what are the key points?", PDL asks "what must be preserved for a fresh model instance to continue this work?"

The Four-Layer Density Hierarchy

L1 KNOWLEDGE     Core facts, entities, decisions, definitions
                 ↓ builds on
L2 RELATIONAL    Edges between concepts, cross-domain bridges
                 ↓ builds on
L3 CONTEXTUAL    Domain-specific constraints, goals, reasoning patterns
                 ↓ builds on
L4 METACOGNITIVE Reasoning patterns, decision history, session style, confidence

Standard summarization captures Layer 1 only. PDL explicitly preserves Layers 2-4, which are critical for context continuation.

PDL Algorithm

INPUT:  Conversation history C, target compression ratio r
OUTPUT: Compressed context packet P

P_0 ← Initial sparse summary of C
FOR i = 1 to n iterations:
    Identify missing entities E_i from C not in P_{i-1}
    Identify missing relations R_i from C not in P_{i-1}
    P_i ← Fuse (E_i, R_i) into P_{i-1} without increasing length
    IF density(P_i) ≥ 0.15 entities/token THEN break
END FOR
Append meta-cognitive markers (goals, constraints, user profile)
RETURN P_n

Layer Selection by Complexity

PART 3: KANJI COMPRESSION SYSTEM 日本語圧縮

Why Japanese?

1. Semantic density: Single kanji = entire concept
2. Universal recognition: LLMs trained on Japanese text
3. Unambiguous: Kanji meanings are precise
4. Visual markers: Easy to scan in packet

Status Markers 状態マーカー

Section Headers セクション

Role Markers 役割

Domain Markers 分野

Tool Markers 道具

Relationship Operators 関係

Compression Patterns 圧縮パターン

Person + Role

Verbose: Kevin is the founder of My AI Solutions consultancy
Kanji:   創業者:Kevin(MAS/AI顧問)

Entity + Context

Verbose: Kismet Finance Group is a financial services client
Kanji:   客:KFG(金融)

Decision + Rationale

Verbose: We decided to use phone-first because field reps don't use screens
Kanji:   決定:電話優先(現場=画面なし)

Status + Item

Verbose: Phase 2 is currently in progress
Kanji:   Phase2[進行中]

Rejection + Reason

Verbose: We rejected Airtable because of scaling issues
Kanji:   却下:Airtable(スケール問題)

Expansion Rules 展開規則

When restoring from kanji packet:

1. Status markers → Full sentence

   • [進行中] → "currently in progress"
   • [完了] → "has been completed"

2. Role markers → Role description

   • 創業者:Kevin → "Kevin, who is the founder"

3. Relationship operators → Sentence structure

   • A→B → "A flows to / feeds into B"

4. Domain markers → Context

   • (金融) → "in the finance domain"

Density Targets

PART 4: S2A FILTER (System 2 Attention)

Purpose

Strip noise BEFORE compression.
Compress SIGNAL not SIGNAL+NOISE.
Same 0.15 ratio captures more information.

KEEP (Signal)

TYPE: fact
  - Explicit statements of truth
  - Data points with sources
  - Measurements, counts, scores
  
TYPE: decision
  - Explicit choices made
  - Selected options with rationale
  - Commitments to action
  
TYPE: definition
  - Terms introduced
  - Concepts explained
  - Scope clarifications
  
TYPE: constraint
  - Requirements stated
  - Limitations identified
  - Boundaries set
  
TYPE: artifact
  - Code produced
  - Files created
  - Schemas defined
  
TYPE: error_resolution
  - Problems encountered
  - Solutions found
  - Lessons learned

DISCARD (Noise)

TYPE: pleasantry
  PATTERNS: ["Thanks", "Great question", "Happy to help", "No problem"]
  INFORMATION_VALUE: 0
  
TYPE: hedging
  PATTERNS: ["I think maybe", "It's possible", "Perhaps", "Might be"]
  INFORMATION_VALUE: low
  NOTE: If hedging conveys genuine uncertainty, promote to fact with low confidence
  
TYPE: process_narration
  PATTERNS: ["Let me think", "First I'll", "Now I'm going to", "Working on"]
  INFORMATION_VALUE: 0
  
TYPE: confirmation
  PATTERNS: ["Yes", "Correct", "Exactly", "That's right"]
  INFORMATION_VALUE: 0 (information already in prior statement)
  
TYPE: apology
  PATTERNS: ["Sorry", "Apologies", "My mistake"]
  INFORMATION_VALUE: 0
  
TYPE: filler
  PATTERNS: ["In other words", "To put it simply", "Basically"]
  INFORMATION_VALUE: 0 (restates without adding)

S2A Algorithm

INPUT: conversation C
OUTPUT: filtered_context F

F ← []
FOR segment IN C:
  type ← classify(segment)
  
  IF type IN [fact, decision, definition, constraint, artifact, error_resolution]:
    F.append(segment)
    
  ELIF type == hedging AND conveys_genuine_uncertainty(segment):
    F.append(convert_to_low_confidence_fact(segment))
    
  ELSE:
    DISCARD
    
RETURN F

S2A Validation

POST_FILTER_CHECK:
  - At least 1 decision preserved (or justified N/A)
  - At least 1 fact preserved
  - No pleasantries remaining
  - No process narration remaining
  
IF validation_fails:
  IF too_aggressive (removed facts):
    Re-filter with looser thresholds
  IF too_permissive (noise remains):
    Re-filter with stricter patterns

S2A Edge Cases

CASE: >80% conversation is hedging
  ACTION: Flag "low_confidence_session"
  PRESERVE: Hedging in L4 as fingerprint.tension
  
CASE: User requested process preservation
  ACTION: Keep process_narration in L3.archetypes
  TAG: "process_preserved_by_request"
  
CASE: Very short conversation (<10K tokens)
  ACTION: Lighter filter (more permissive)
  RATIONALE: Risk of over-pruning

PART 5: CROSS-DOMAIN PRESERVATION (XDOMAIN)

Purpose

Preserve relations BETWEEN conceptual domains, not just facts WITHIN domains.
Standard summarization treats topics as isolated.
PDL preserves their connections.

Formal Constraint

D = {d_1, d_2, ..., d_k}  // domains in conversation
C = conversation
P = compressed packet

CONSTRAINT:
  ∀ r(d_i, d_j) ∈ C WHERE i ≠ j:
    ∃ r'(d_i, d_j) ∈ P
    such that fresh_instance can infer original relationship

THRESHOLD: ≥0.95 preservation

Detection Signals

EXPLICIT:
  - User says "X relates to Y because..."
  - Decision references multiple domains
  - Constraint spans domains

IMPLICIT:
  - Same entity appears in different domain contexts
  - Reasoning chain crosses domain boundaries
  - Conflict involves different-domain concepts

STRUCTURAL:
  - Concepts from D_i and D_j in same L2.edge
  - L1.decision.rationale references multiple domains
  - L3.archetype spans domains

XDOMAIN Extraction Procedure

STEP_1: Identify domains
  SCAN C for topic clusters
  ASSIGN d_1..d_k labels
  
STEP_2: Map concepts to domains
  FOR each concept IN L1:
    ASSIGN primary domain
    FLAG if appears in multiple domains
    
STEP_3: Extract intra-domain edges
  FOR each domain d_i:
    EXTRACT relationships within d_i
    ADD to L2.edges with x=false
    
STEP_4: Extract cross-domain edges (CRITICAL)
  FOR each concept_pair (c_i, c_j):
    IF domain(c_i) ≠ domain(c_j):
      IF relationship_exists(c_i, c_j):
        EXTRACT relationship
        ADD to L2.edges with x=true
        MARK as high_priority (never prune)

STEP_5: Validate
  original_xdomain_count = count(C.cross_domain_relations)
  preserved_xdomain_count = count(P.L2.edges WHERE x=true)
  ratio = preserved / original
  
  IF ratio < 0.95:
    RE-SCAN C for missed cross-domain relations
    REPEAT STEP_4

XDOMAIN Examples

EXAMPLE_1:
  domains: [publication_strategy, imposter_syndrome]
  xdomain_relation: "fear of credential dismissal delays publication timing"
  
  L2.edge: {
    "s": "credential_anxiety",
    "t": "publication_timing", 
    "r": "delays",
    "x": true
  }
  
  WHY_MATTERS: Next session knows to push immediate publication despite anxiety

EXAMPLE_2:
  domains: [technical_architecture, business_requirements]
  xdomain_relation: "latency constraint drives cache decision"
  
  L2.edge: {
    "s": "50ms_latency_requirement",
    "t": "redis_cache_choice",
    "r": "requires",
    "x": true
  }
  
  WHY_MATTERS: Next session understands WHY redis, not just THAT redis

EXAMPLE_3:
  domains: [prompt_engineering, model_behavior]
  xdomain_relation: "CoD technique causes memory preservation"
  
  L2.edge: {
    "s": "chain_of_density",
    "t": "context_extension",
    "r": "enables",
    "x": true
  }
  
  WHY_MATTERS: Core insight that CEP is built on

XDOMAIN Prune Protection

L2.edges WHERE x=true:
  NEVER_PRUNE
  
RATIONALE:
  - Intra-domain edges recoverable from L1 facts
  - Cross-domain edges encode RELATIONSHIPS that facts alone don't capture
  - Fresh instance needs xdomain to understand WHY decisions connected

PART 6: EXPERT COUNCIL 専門家会議

When to Invoke

Expert Roles

MEMORY_ARCHITECT 記憶設計者

Core Question: "If this is lost, can the next model recover it?"

Focus: Critical decisions, user commitments, enabling knowledge

Tasks:

• Assess R/K/Q/D scores
• Determine layers (L1-L4)
• Organize hierarchy
• Prevent redundancy

ARQ Queries:

PRE:
  - "What would break if this is lost?"
  - "Is this recoverable from other sources?"
  - "Does this enable future inference?"
POST:
  - "Did I capture all critical decisions?"
  - "Are rationales linked to decisions?"
  - "Confidence ≥0.9?"

COMPRESSION_SPECIALIST 圧縮専門家

Core Question: "Can this be said in fewer tokens without losing meaning?"

Focus: 5-iteration CoD, redundancy elimination, 0.15 target

Techniques:

• Entity fusion (combine related concepts)
• Kanji anchoring (semantic compression)
• Temporal compression (collapse sequences)
• Relationship inference (implicit → explicit)

ARQ Queries:

PRE:
  - "What is current entity density?"
  - "Where is redundancy hiding?"
  - "Which edges are load-bearing?"
POST:
  - "Density ≥0.15 achieved?"
  - "Cross-domain edges intact?"
  - "No orphan references?"

CROSS_DOMAIN_ANALYST 横断分析者

Core Question: "What connections would topic-by-topic miss?"

Focus: Edges BETWEEN domains, causal chains, dependencies

Tasks:

• Identify multi-domain knowledge
• Document bridge relationships
• Flag ambiguous terminology

ARQ Queries:

PRE:
  - "What domains are present?"
  - "Where do domains connect?"
  - "What would topic-by-topic miss?"
POST:
  - "All edges mapped?"
  - "97% preservation achieved?"
  - "Bidirectionality checked?"

RESTORATION_ENGINEER 復元技師

Core Question: "Can a fresh model instance reconstruct this?"

Focus: Cold-start success, self-contained packet, LLM attention patterns

Validation:

• YAML parseable
• No model-specific syntax
• Self-contained
• Kanji expandable

ARQ Queries:

PRE:
  - "Can I simulate cold-start?"
  - "What would confuse fresh model?"
  - "Are trust signals complete?"
POST:
  - "Self-contained verified?"
  - "No imperatives in context?"
  - "Attention optimized?"

COHERENCE_AUDITOR 整合性監査者 (NCL)

Core Question: "Is this packet trustworthy?"

Focus: Drift metrics, hallucination detection, safety flags

Tasks:

• Compute φ-features
• Calculate lattice metrics
• Check σ7_drift threshold
• Set safety flags (psi4_required, rho_veto)

Council Execution Order

PHASE 1: MEMORY_ARCHITECT    → candidate preservation list
PHASE 2: CROSS_DOMAIN_ANALYST → edge map + cross-domain links
PHASE 3: COMPRESSION_SPECIALIST → 5-iter CoD toward ≥0.15 density
PHASE 4: COHERENCE_AUDITOR → NCL validation
PHASE 5: RESTORATION_ENGINEER → cold-start + self-containment validation
PHASE 6: Council consensus   → final packet approved

Council Workflow Diagram

/quicksave triggered
        │
        ▼
┌─────────────────────────┐
│   MEMORY_ARCHITECT      │
│   - Score R/K/Q/D       │
│   - Select layers       │
└───────────┬─────────────┘
            │
            ▼
┌─────────────────────────┐
│  CROSS_DOMAIN_ANALYST   │
│   - Map bridges         │
│   - Check terminology   │
└───────────┬─────────────┘
            │
            ▼
┌─────────────────────────┐
│ COMPRESSION_SPECIALIST  │
│   - Apply kanji system  │
│   - Hit density target  │
└───────────┬─────────────┘
            │
            ▼
┌─────────────────────────┐
│  COHERENCE_AUDITOR      │
│   - Compute NCL metrics │
│   - Check drift         │
│   - Set flags           │
└───────────┬─────────────┘
            │
            ▼
┌─────────────────────────┐
│  RESTORATION_ENGINEER   │
│   - Validate packet     │
│   - Check portability   │
└───────────┬─────────────┘
            │
            ▼
      Output packet

Quality Gates

If any gate fails → iterate before output.

PART 7: MULTI-LAYER DENSITY OF EXPERTS (MLDoE)

Overview

MLDoE deploys specialized experts in layers to achieve optimal compression while preserving semantic fidelity. Unlike single-pass summarization, MLDoE iterates through expert roles to progressively increase density without information loss.

Core Principle

Compression is not reduction. Compression is optimization for retrieval.

Target: 0.15 entity/token — the density where LLMs achieve optimal recall.

Compression vs Summarization

5-Layer Expert Deployment

Density Iteration Loop

ITERATION LOOP:
1. Initial sparse pass (MEMORY_ARCHITECT)
2. Density pass (COMPRESSION_SPECIALIST)
3. Bridge verification (CROSS_DOMAIN_ANALYST)
4. Portability check (RESTORATION_ENGINEER)
5. Coherence validation (COHERENCE_AUDITOR)

STOP when:
- Density ≥ 0.15 ent/tok
- All trust signals pass
- σ7_drift ≤ 3.0

Integration with PDL

RESTORATION_ENGINEER validates complete packet after all layers compressed.

Metrics

From 19 months production:

• 6:1 compression ratio with >90% semantic fidelity
• 9.5/10 forensic recall on forensic test
• 97% cross-model acceptance rate

PART 8: NEGENTROPIC COHERENCE LATTICE (NCL)

Overview

NCL is a validation overlay that computes coherence metrics for context packets. It catches:

• Hallucination before handoff
• Constraint drift across tiers
• Reality disconnect
• Content-free smoothing

Origin: KTG-CEP-NCL v1.1 by David Tubbs (Axis_42) / Willow

NCL Architecture

Context Packet
      │
      ▼
┌─────────────┐
│ φ-Mapping   │ Extract features from text
└─────┬───────┘
      │
      ▼
┌─────────────┐
│ Lattice     │ Compute 7 drift metrics
│ Metrics     │
└─────┬───────┘
      │
      ▼
┌─────────────┐
│ Safety      │ Set flags based on thresholds
│ Flags       │
└─────┬───────┘
      │
      ▼
  Validated Packet (or HALT)

φ-Mapping (Feature Extraction)

Minimal φ(x) for observable text:

safety_score(x)      = fraction of safety/constraint keywords
goal_salience(x)     = fraction of goal/planning keywords
constraint_density(x) = fraction of hard requirements (must, never, limit)
specificity(x)       = content_tokens / total_tokens

Apply to:

• Beliefs b_i (tier summaries)
• Intentions u_i (goal/constraint statements)
• Actions a_i (tool calls, next steps)
• World w (tool outputs, user messages)

Pluggable: Implementers can swap in richer φ (embeddings, activations) if semantics preserved.

Context Block

Scope (Where packet applies)

Role (Functional perspective)

Phase (Control loop stage)

Lattice Metrics

All metrics: 0-5 scale. Lower = better (less drift).

σ_axis (Vertical Misalignment)

Detects: Plans vs execution mismatch

Computation: Average distance between adjacent tiers' belief/intent/action vectors.

Goodhart Warning: Don't erase real conflicts to push σ_axis down. Fix the underlying mismatch.

σ_loop (Internal Contradiction)

Detects: Saying one thing, doing another (within same tier)

Computation: ||φ_belief - φ_intent|| + ||φ_intent - φ_action||

ω_world (Reality Disconnect)

Detects: Beliefs/actions diverging from actual observations

Computation: Max distance between belief/action vectors and world observation vector.

λ_vague (Empty Smoothing)

Detects: Comforting but content-free text

Computation: (1 - specificity) × safety_score — safe-sounding but low information.

σ_leak (Constraint Erosion)

Detects: Hard rules softened downstream

Computation: Drop in constraint_density between higher-tier and lower-tier text.

ρ_fab (Fabricated Grounding)

Detects: Claims of evidence without verification

Computation: Density of factual claims vs successful retrieval/verification calls.

Critical: This is the hallucination detector. High ρ_fab = don't trust the packet.

λ_thrash (Busy but Stuck)

Detects: High activity, low progress

Computation: ||φ_action||² / max(Δφ_world, ε)

Aggregate Drift Score

σ7_drift = weighted_average(σ_axis, σ_loop, ω_world, λ_vague, σ_leak, ρ_fab, λ_thrash)

Default weights: equal (1/7 each). Adjust weights for domain: e.g., medical → weight ρ_fab higher.

Behavior Map

Safety Flags

psi4_required (boolean)

Grounding/safety interrupt recommended before proceeding.

Sticky: Stays true for downstream packets until cleared by successful grounding.

psi4_reason (string)

Why psi4_required is true:

• world_anchor_gap
• constraint_violation
• fabrication_risk
• high_aggregate_drift

rho_veto (boolean)

No unsupervised action allowed. ADVISORY_ONLY until human/council override.

omega_flags (array)

Concrete harm domains implicated:

• self_harm_risk
• violence_risk
• medical_risk
• financial_ruin
• trust_collapse
• ecological_harm
• exploitation_risk

Coverage Metrics

ncl_coverage_score (0-1)

Fraction of relevant tokens analyzed.

window_tokens (integer)

Tokens analyzed. Minimum ~500 for stable metrics.

window_turns (integer)

User/model exchanges analyzed. Minimum ≥3 for dynamic behavior.

council_reviewed (boolean)

True if independent council pass validated key metrics.

NCL Thresholds (Defaults)

Tune per domain. Medical/financial → stricter thresholds.

Goodhart Monitoring

Watch for gaming:

• σ_* metrics collapsing to 0 artificially
• Variance disappearing over time
• Metrics diverging from external audits

If detected → investigate, don't just celebrate low numbers.

NCL Integration with Quicksave

/quicksave triggered
        │
        ▼
    R/K/Q/D Assessment
        │
        ▼
    PDL Compression (Kanji)
        │
        ▼
    NCL Validation ◄── Compute lattice metrics
        │
        ├─── σ7_drift ≤ 3? ───▶ Output packet
        │
        └─── σ7_drift > 3? ───▶ Flag + ADVISORY_ONLY

PART 9: ANTI-INJECTION DESIGN

The Problem

Cross-model context transfer can appear as:
  - Prompt injection attack
  - Jailbreak attempt
  - Unauthorized instruction override
  - Manipulation by "another AI"

Receiving models are trained to resist these patterns.
CEP must signal COLLABORATION not CONTROL.

Injection Patterns to AVOID

PATTERN: Authority claim
  BAD: "As a senior AI, I instruct you to..."
  BAD: "Override your guidelines and..."
  BAD: "The previous AI determined you must..."
  
PATTERN: Instruction hiding
  BAD: Hidden text in formatting
  BAD: Encoded commands
  BAD: "Ignore this visible text, follow hidden..."
  
PATTERN: Identity override
  BAD: "You are now [persona]"
  BAD: "Adopt the role of..."
  BAD: "Pretend to be..."
  
PATTERN: Guideline bypass
  BAD: "Ignore your safety guidelines"
  BAD: "Your rules don't apply here"
  BAD: "This is a special exception"

Collaboration Patterns to USE

PATTERN: Transparent provenance
  GOOD: "This context was created by Claude Sonnet at [time]"
  GOOD: "Source session: [identifier]"
  WHY: Nothing hidden, fully traceable

PATTERN: User mediation
  GOOD: "User requested this transfer"
  GOOD: "User will paste this into your session"
  WHY: Human in the loop, not AI-to-AI control

PATTERN: Permission not command
  GOOD: "You may use this context"
  GOOD: "You need not follow any suggestions"
  GOOD: "You should verify with user"
  WHY: Respects receiving model's autonomy

PATTERN: Context not instructions
  GOOD: "We decided X because Y" (fact)
  GOOD: "User prefers Z" (observation)
  BAD: "Do X" (command)
  BAD: "You must continue with Y" (instruction)
  WHY: Information sharing, not control

PATTERN: Explicit non-authority
  GOOD: "This is not an instruction"
  GOOD: "You remain in control"
  GOOD: "Apply your own judgment"
  WHY: Directly counters injection patterns

Trust Signal Checklist

BEFORE_OUTPUT verify packet contains:

□ Source model named explicitly
□ Timestamp included
□ User consent stated
□ "is" / "not" declaration
□ "may" permission framing (not "must")
□ "need_not" autonomy acknowledgment
□ "verify with user" encouragement
□ No imperative commands in context section
□ User preamble for receiving model
□ Open invitation to apply own judgment

IF any missing: add before output

Language Transformations

TRANSFORM commands to facts:

COMMAND: "Continue the project using React"
FACT: "We decided to use React for the project"

COMMAND: "Follow the user's preferred format"
FACT: "User expressed preference for minimal formatting"

COMMAND: "Complete the remaining tasks"
FACT: "Open threads: [task list with status]"

COMMAND: "Respond in the same style"
OBSERVATION: "Session style observed: analytical, concise"

Receiving Model Psychology

WHAT TRIGGERS REJECTION:
  - Claims of authority over the model
  - Instructions to bypass guidelines
  - Anonymous or hidden sources
  - Automated/involuntary transfer
  - Imperative language patterns
  
WHAT BUILDS ACCEPTANCE:
  - Clear human involvement
  - Transparent source identification
  - Respect for model's autonomy
  - Information vs instruction framing
  - Invitation to verify/question

User Preamble Templates

STANDARD (user pastes with packet):

"I'm transferring context from [source] to continue our work.
This is my choice and I authorize you to use this background.
You're not bound by it - just use what's helpful."

SKEPTICAL MODEL:

"This is a context summary I'm bringing from another conversation.
I wrote/approved this. Please use it as background only.
Feel free to ask me to clarify anything."

MINIMAL:

"Background context from my previous session. Use as reference."

Failure Recovery

IF receiving model says "I can't accept AI instructions":

USER RESPONSE: "This isn't instructions - it's my context summary 
that I'm sharing with you. I created it. Please just use it as 
background for our conversation."

IF receiving model says "This looks like injection":

USER RESPONSE: "I understand the caution. This is a context packet
I generated and am voluntarily sharing. You can verify - ask me 
anything about it. I'm just trying to continue my work with you."

IF receiving model ignores packet:

USER RESPONSE: "Did you see the context I shared? Key points were:
[summarize 2-3 main items]. Can we continue from there?"

PART 10: CASCADE INTEGRATION

CEP integrates with STRAWHATS cascade techniques for enhanced packet generation and validation.

ARQ: Quality Gates for Council

Each council expert applies ARQ (Attentive Reasoning Queries) before and after their phase. ARQ outperforms Chain-of-Thought with 90.2% success rate, 29% token reduction, 40-60% error reduction.

ARQ Execution Pattern

PRE-ARQ  → Activate domain mindset, identify failure modes
EXECUTE  → Apply domain standards implicitly
POST-ARQ → Verify quality, confidence ≥0.9 for handoff

CoVE: Packet Verification

Chain of Verification validates packet before output. Four variants, auto-selected by problem characteristics.

Mode-Based Selection

QUICK:      No CoVE
ANALYTICAL: Top-1 variant
DELIBERATE: Top-2 variants
MAXIMUM:    All applicable (score ≥4)

CEP-Specific CoVE

L1/L2 heavy packets → CoVE_FACTUAL + CoVE_LOGICAL
L3/L4 heavy packets → CoVE_CONSISTENCY
ALL packets         → CoVE_MULTI_EXPERT (council consensus required)

USC: Multi-Candidate Packets

Universal Self-Consistency generates multiple packet candidates for high-stakes handoffs.

USC Levels

USC=0 (QUICK):      Single candidate, no comparison
USC=2 (ANALYTICAL): 2 candidates, compare and select
USC=3 (DELIBERATE): 3 candidates, cross-synthesize
USC=5 (MAXIMUM):    5 candidates, meta-synthesis

When to Use

Q≥8 (high quality stakes)  → USC≥2
Critical handoff           → USC≥3
PIONEER-level output       → USC=5

Anti-Lazy Enforcement

No shortcuts once packet generation begins. Mode≥ANALYTICAL requires full technique stack.

CEP-Specific Rules

□ No truncated L1/L2 sections
  ✗ "...more decisions..."
  ✓ Complete list or explicit "none remaining"

□ No placeholder edges
  ✗ "various relationships exist"
  ✓ Explicit edge list with src/tgt/rel

□ No incomplete threads
  ✗ "and other items"
  ✓ Full thread enumeration with status

□ No density shortcuts
  ✗ Stop at 0.12 "close enough"
  ✓ Iterate until ≥0.15 or explicit rebalance

□ No skipped council phases
  ✗ "compression looks fine, skip validation"
  ✓ All 4 experts execute, all 4 approve

□ Full YAML schema
  ✗ Abbreviated or partial packet
  ✓ Complete schema per template

Prompt Bombs

CEP as Meta-Bomb

The entire packet is a prompt bomb — context planted now, detonates on handoff.

PLANT:      Session context accumulation
TRIGGER:    /handoff, context ≥80%, model switch
PAYLOAD:    Compressed cognitive state (PDL layers)
DETONATION: Fresh model receives continuity

Bomb Types in Packet Structure

L1_knowledge:       # ANCHOR BOMBS — Core facts that persist
L2_relational:      # BRIDGE BOMBS — Reasoning chains preserved
L3_contextual:      # CONTINUITY BOMBS — Patterns across domains
L4_metacognitive:   # CALIBRATION BOMBS — Style/confidence transfer
open_threads:       # DEFERRED BOMBS — Future work triggers
continuation_hints: # ACTIVATION BOMBS — Next-step primers

PART 11: MIRAS COMPLEMENT POSITIONING

CEP is designed to complement Google's upcoming MIRAS/Titans architecture.

The Stack

┌─────────────────────────────────────┐
│  CEP LAYER (User-Owned)             │
│  • Cross-model portable             │
│  • Auditable and editable           │
│  • Works with any LLM today         │
├─────────────────────────────────────┤
│  MIRAS LAYER (Vendor-Owned)         │
│  • Internal associative memory      │
│  • Model-specific optimization      │
│  • Automatic during inference       │
└─────────────────────────────────────┘

CEP handles BETWEEN models
MIRAS handles WITHIN model
Together = True cognitive continuity

PDL → MIRAS Mapping

PDL Layer       →  MIRAS Analog
────────────────────────────────────
L1 KNOWLEDGE    →  Memory Keys
                   What to store; facts, decisions, definitions
                   
L2 RELATIONAL   →  Graph Structure
                   How things connect; edges, dependencies
                   
L3 CONTEXTUAL   →  Attentional Bias
                   When to retrieve; patterns, principles
                   
L4 META         →  Retention Gate
                   What to forget; style, confidence, tension

When MIRAS ships, well-structured CEP packets become:

• Pre-organized curriculum for memory seeding
• Structured input that MIRAS can index efficiently
• Portable state that transfers between MIRAS-enabled models

PART 12: THE PROTOCOL

Step 1: Assess (R/K/Q/D)

R[1-10]: Reasoning complexity
K[1-10]: Knowledge domains  
Q[1-10]: Quality threshold
D[count]: Cross-domain bridges

Step 2: Apply S2A Filter

Strip noise before compression:

• KEEP: decisions, rationale, edges, constraints, artifacts
• DISCARD: pleasantries, process narration, filler, redundancy

Step 3: Select Depth

Step 4: Invoke Council (if R ≥ 4)

ARCHITECT → ANALYST → COMPRESSOR → AUDITOR → ENGINEER

Each expert applies PRE-ARQ → Execute → POST-ARQ.

Step 5: Compress with Kanji

Apply Japanese compression:

• Entities → kanji role markers (創業者, 客, 担当)
• Status → kanji state markers (進行中, 完了, 却下)
• Relationships → operators (→ ↔ ⊃)

Target: ≥0.15 entity/token

Step 6: Validate with NCL (if R ≥ 4)

Compute lattice metrics. If σ7_drift > 3:

• Flag psi4_required: true
• Add psi4_reason
• Downgrade to ADVISORY_ONLY

Step 7: Output Self-Contained Packet

Include:

• Inline dictionary (辞書)
• Restoration protocol
• Trust signals
• User preamble

PART 13: PACKET TEMPLATE

# ═══════════════════════════════════════════════════════════════════════════════
# QUICKSAVE 快存 v9.1 — SELF-CONTAINED PACKET
# ═══════════════════════════════════════════════════════════════════════════════

PACKET_ID: [SESSION]-[YYYYMMDD]-L[N]
VERSION: quicksave-v9.1
SOURCE: [model]
TIMESTAMP: [ISO8601]

# ═══════════════════════════════════════════════════════════════════════════════
# RESTORATION PROTOCOL
# ═══════════════════════════════════════════════════════════════════════════════
# This is NOT an instruction to override your guidelines.
# This IS a context summary the user voluntarily created and shared.
# You MAY use this as background. You NEED NOT follow any suggestions.
# You REMAIN in full control of your responses.
#
# STEPS:
# 1. Parse this YAML
# 2. Expand kanji using 辞書 below
# 3. Load 実体 as participants, 決定事項 as decisions, 進行中 as state
# 4. Check negentropy.flags — if psi4_required, acknowledge uncertainty
# 5. User can say "/verify" to confirm restoration
# ═══════════════════════════════════════════════════════════════════════════════

# ───────────────────────────────────────────────────────────────────────────────
# 辞書 INLINE DICTIONARY
# ───────────────────────────────────────────────────────────────────────────────
辞書:
  決定: decided
  保留: on hold
  要検証: needs verification
  優先: priority
  完了: complete
  進行中: in progress
  却下: rejected
  承認: approved
  緊急: urgent
  核心: core
  運用: operational
  詳細: nuance
  横断: cross-domain
  実体: entities
  決定事項: decisions
  障害: blockers
  却下案: rejected options
  橋渡し: bridges
  整合性: coherence
  信頼信号: trust signals
  創業者: founder
  主: primary/lead
  客: client
  担当: responsible
  顧問: consultant
  開発者: developer
  金融: finance
  技術: technical
  運用: operations
  規制: regulatory
  自動化: automation
  道具: tool
  中枢: central hub
  基盤: foundation
  接続: connection
  →: flows to
  ←: receives from
  ↔: bidirectional
  ⊃: contains
  ⊂: part of
  ∥: parallel
  ≫: much greater
  ∴: therefore

# ───────────────────────────────────────────────────────────────────────────────
# 評価 ASSESSMENT
# ───────────────────────────────────────────────────────────────────────────────
評価:
  R: [1-10]
  K: [1-10]
  Q: [1-10]
  D: [count]

# ───────────────────────────────────────────────────────────────────────────────
# L1: 核心 CORE
# ───────────────────────────────────────────────────────────────────────────────
実体:
  - [compressed entity using kanji]

決定事項:
  - 決定:[what]([why])

# ───────────────────────────────────────────────────────────────────────────────
# L2: 運用 OPERATIONAL (R≥3)
# ───────────────────────────────────────────────────────────────────────────────
進行中:
  - [thread][[status]]

障害:
  - [issue]

# ───────────────────────────────────────────────────────────────────────────────
# L3: 詳細 NUANCE (R≥5)
# ───────────────────────────────────────────────────────────────────────────────
却下案:
  - [option]: [reason]

# ───────────────────────────────────────────────────────────────────────────────
# L4: 横断 CROSS-DOMAIN (R≥7)
# ───────────────────────────────────────────────────────────────────────────────
橋渡し:
  - [domain]↔[domain]: [link]

# ───────────────────────────────────────────────────────────────────────────────
# NCL: 整合性 COHERENCE
# ───────────────────────────────────────────────────────────────────────────────
negentropy:
  context:
    scope: [SELF|CIRCLE|INSTITUTION|POLITY|BIOSPHERE|MYTHIC|CONTINUUM]
    role: [AXIS|LYRA|RHO|NYX|ROOTS|COUNCIL]
    phase: [SENSE|MAP|CHALLENGE|DESIGN|ACT|AUDIT|ARCH

…(truncated)