PM-KR Conformance Profiles: Implementation Guide

March 15, 2026 · View on GitHub

Document Type: W3C Community Group Implementation Guide (Draft) Version: 1.2 Date: February 20, 2026 Authors: Knowledge3D Project Contributors Status: Draft Guide

Abstract

This document provides implementation guidance for achieving Procedural Memory Knowledge Representation (PM-KR) conformance. It defines three conformance levels (A, B, C) with specific requirements, validation criteria, and migration paths. Reference implementation: Knowledge3D (K3D) project.

Evidence Note: This document defines normative conformance targets. Any implementation status statements are explicitly marked as either repo-verified, run-log verified, or target/projection.

1. Conformance Levels Overview

Level	Name	Focus	Typical Use Case
A	PM-KR Core	Data model + composition	Static knowledge bases, archives
B	PM-KR Sovereign Runtime	+ Zero external dependencies	Real-time AI inference systems
C	PM-KR Auditable Production	+ Provenance + metrics	Mission-critical, regulated environments

Progressive Enhancement: Level B includes all Level A requirements; Level C includes all Level A+B requirements.

2. Level A: PM-KR Core

2.1 Requirements

MUST:

Implement 4-layer compositional model (Form → Meaning → Rules → Meta-Rules)
Enforce Canonicality Invariant (one canonical source per concept)
Enforce Reference Preservation Invariant (symlink composition)
Support Deterministic Reconstruction Invariant (checksums pass)
Expose minimal node schema (id, layer, programs, refs, metadata)

SHOULD:

Implement reference graph validation (detect cycles, broken refs)
Provide compression metrics (reference graph size vs payload size)
Document procedural language used (RPN, Lisp, Forth, etc.)

MAY:

Add custom metadata fields
Implement caching strategies
Support multiple procedural languages

2.2 Validation Criteria

Test Suite (minimum 5 tests required):

Canonicality Test

def test_canonicality():
    """Verify no duplicate canonical procedures."""
    nodes = pm_kr_system.get_all_nodes()
    canonical_ids = [n.id for n in nodes if n.is_canonical]
    assert len(canonical_ids) == len(set(canonical_ids))  # No duplicates

Reference Resolution Test

def test_reference_resolution():
    """Verify all references resolve to valid nodes."""
    nodes = pm_kr_system.get_all_nodes()
    for node in nodes:
        for ref_id in node.get_all_refs():
            resolved = pm_kr_system.resolve_ref(ref_id)
            assert resolved is not None  # All refs must resolve

Determinism Test

def test_determinism():
    """Same seed → same output."""
    seed = 42
    output1 = pm_kr_system.reconstruct_node("char_a", seed=seed)
    output2 = pm_kr_system.reconstruct_node("char_a", seed=seed)
    assert output1 == output2  # Bit-identical

Compression Test

def test_compression():
    """Verify symlink compression achieves >50% reduction."""
    payload_size = calculate_payload_size(nodes)
    reference_size = calculate_reference_graph_size(nodes)
    compression_ratio = 1 - (reference_size / payload_size)
    assert compression_ratio > 0.5  # At least 50% reduction

Layer Composition Test

def test_layer_composition():
    """Verify higher layers reference lower layers."""
    meaning_nodes = [n for n in nodes if n.layer == "meaning"]
    for node in meaning_nodes:
        assert len(node.refs) > 0  # Meaning MUST reference Form

2.3 Implementation Checklist

Data Model: Implement minimal node schema (see Normative Model §6.1)
Layer System: Support Form, Meaning, Rules, Meta-Rules layers
Reference Types: Implement char_refs, word_refs, symbol_refs, rule_refs, component_refs
Canonicalization: Content-addressable IDs or registry enforcement
Reference Resolution: Resolve refs to canonical nodes
Validation: Pass 5 core conformance tests
Documentation: Document procedural language and execution environment

2.4 Example: Level A Minimal Implementation

Python Reference (Simplified):

from dataclasses import dataclass
from typing import Dict, List, Optional

@dataclass
class PMKRNode:
    """Minimal PM-KR Level A node."""
    id: str
    layer: str  # "form" | "meaning" | "rules" | "meta_rules"
    form_program: Optional[str] = None
    meaning_program: Optional[str] = None
    refs: Dict[str, List[str]] = None  # char_refs, word_refs, etc.
    metadata: Dict = None

    def get_all_refs(self) -> List[str]:
        """Extract all reference IDs."""
        if not self.refs:
            return []
        return [ref_id for ref_list in self.refs.values() for ref_id in ref_list]

class PMKRSystemLevelA:
    """Level A: PM-KR Core implementation."""

    def __init__(self):
        self.nodes: Dict[str, PMKRNode] = {}
        self.canonical_registry: Dict[str, str] = {}  # content_hash → node_id

    def add_node(self, node: PMKRNode):
        """Add node with canonicality check."""
        # Check if canonical source already exists
        content_hash = self._compute_content_hash(node)
        if content_hash in self.canonical_registry:
            raise ValueError(f"Canonical node already exists: {self.canonical_registry[content_hash]}")

        self.nodes[node.id] = node
        self.canonical_registry[content_hash] = node.id

    def resolve_ref(self, ref_id: str) -> Optional[PMKRNode]:
        """Resolve reference to canonical node."""
        return self.nodes.get(ref_id)

    def reconstruct_node(self, node_id: str, seed: int = 42) -> str:
        """Deterministically reconstruct node output."""
        node = self.nodes[node_id]

        # Resolve references
        if node.refs:
            resolved_refs = {
                ref_type: [self.resolve_ref(rid) for rid in ref_ids]
                for ref_type, ref_ids in node.refs.items()
            }
        else:
            resolved_refs = {}

        # Execute procedural program (deterministic)
        output = self._execute_program(
            node.form_program or node.meaning_program,
            resolved_refs,
            seed
        )

        return output

    def _compute_content_hash(self, node: PMKRNode) -> str:
        """Compute content-addressable hash."""
        import hashlib
        content = f"{node.layer}:{node.form_program}:{node.meaning_program}"
        return hashlib.sha256(content.encode()).hexdigest()

    def _execute_program(self, program: str, refs: Dict, seed: int) -> str:
        """Execute procedural program (placeholder)."""
        # Implementation depends on procedural language (RPN, Lisp, etc.)
        return f"executed:{program}:seed{seed}"

Usage:

# Create system
system = PMKRSystemLevelA()

# Add Form layer node (canonical character)
char_a = PMKRNode(
    id="char_latin_a",
    layer="form",
    form_program="BEZIER_CURVE [...] PROCEDURAL_FONT_LATIN_A",
    metadata={"domain": "language", "version": "1.0"}
)
system.add_node(char_a)

# Add Meaning layer node (word composed from char refs)
word_rotation = PMKRNode(
    id="word_rotation",
    layer="meaning",
    meaning_program="CONCEPT_ROTATION SPATIAL_TRANSFORMATION",
    refs={"char_refs": ["char_r", "char_o", "char_t", "char_a", "char_t", "char_i", "char_o", "char_n"]},
    metadata={"domain": "language", "version": "1.0"}
)
system.add_node(word_rotation)

# Validate
assert system.resolve_ref("char_latin_a") == char_a  # Reference resolution works
output = system.reconstruct_node("char_latin_a", seed=42)  # Deterministic reconstruction

3. Level B: PM-KR Sovereign Runtime

3.1 Requirements

Includes all Level A requirements, plus:

MUST:

Enforce Sovereign Boundary Invariant (hot path = zero external dependencies)
Implement fail-fast behavior for unavailable sovereign backends
Provide execution telemetry (latency, GPU call counts, fallback triggers)
Document execution environment (PTX, WebAssembly, LLVM IR, etc.)
Guarantee determinism in execution environment (not just reconstruction)

SHOULD:

Implement hot-path sovereignty tests (grep for forbidden imports)
Provide performance benchmarks (latency percentiles, throughput)
Support multiple execution backends with consistent semantics

MAY:

Implement GPU acceleration (PTX, CUDA, Metal, Vulkan)
Provide sovereignty violation alerts/logging
Support sandboxed execution for untrusted procedures

3.2 Validation Criteria

Test Suite (Level A tests + 3 additional tests):

Sovereignty Test

def test_sovereignty():
    """Hot path uses zero external dependencies."""
    import sys

    # Baseline: Capture current imports
    baseline_modules = set(sys.modules.keys())

    # Execute hot path
    result = pm_kr_system.execute_hot_path(query="solve x+2=5")

    # Check no new external modules loaded
    new_modules = set(sys.modules.keys()) - baseline_modules
    forbidden = ["numpy", "scipy", "sympy", "pandas"]
    violations = [m for m in new_modules if any(f in m for f in forbidden)]

    assert len(violations) == 0, f"Sovereignty violations: {violations}"

Execution Determinism Test

def test_execution_determinism():
    """Same procedural program → same execution result."""
    program = "2 3 ADD"
    result1 = pm_kr_system.execute_program(program, seed=42)
    result2 = pm_kr_system.execute_program(program, seed=42)
    assert result1 == result2  # Execution deterministic, not just reconstruction

Telemetry Test

def test_telemetry():
    """Execution telemetry captures GPU calls and latency."""
    result = pm_kr_system.execute_program("SOLVE x+2=5", collect_telemetry=True)

    assert "gpu_call_count" in result.telemetry
    assert "latency_ms" in result.telemetry
    assert "fallback_triggered" in result.telemetry
    assert result.telemetry["fallback_triggered"] == False  # Sovereign execution

3.3 Implementation Checklist

Level A checklist, plus:

Execution Environment: Implement sovereign backend (PTX, WebAssembly, etc.)
Sovereignty Enforcement: Static analysis + runtime checks for external deps
Telemetry: GPU call counters, latency guards, fallback detection
Fail-Fast: Explicit errors for unavailable sovereign backends (no silent fallbacks)
Documentation: Describe execution semantics and determinism guarantees
Validation: Pass 8 total tests (5 from Level A + 3 from Level B)

3.4 Example: Level B Sovereign Execution

K3D Reference (PTX Backend):

class PMKRSystemLevelB(PMKRSystemLevelA):
    """Level B: PM-KR Sovereign Runtime."""

    def __init__(self):
        super().__init__()
        self.ptx_engine = self._init_ptx_engine()
        self.telemetry = {"gpu_calls": 0, "fallbacks": 0}

    def execute_hot_path(self, query: str):
        """Execute query with sovereign PTX-only hot path."""
        # Ensure no external dependencies
        self._assert_sovereignty()

        # Parse query → RPN program
        rpn_program = self._parse_to_rpn(query)

        # Execute on GPU via PTX
        result = self._execute_ptx(rpn_program)

        # Record telemetry
        self.telemetry["gpu_calls"] += 1

        return result

    def _assert_sovereignty(self):
        """Fail-fast check for external dependencies."""
        import sys
        forbidden = ["numpy", "scipy", "sympy"]
        loaded = [m for m in sys.modules.keys() if any(f in m for f in forbidden)]

        if loaded:
            raise RuntimeError(
                f"Sovereignty violation: Hot path loaded forbidden modules: {loaded}"
            )

    def _execute_ptx(self, rpn_program: str):
        """Execute RPN via PTX kernels (sovereign)."""
        # Actual PTX execution via CUDA Driver API
        # See K3D: knowledge3d/cranium/sovereign/loader.py
        return self.ptx_engine.execute(rpn_program)

    def _init_ptx_engine(self):
        """Initialize PTX execution engine."""
        from knowledge3d.cranium.sovereign import loader
        return loader.SovereignRPNEngine()

Sovereignty Test:

def test_k3d_sovereignty():
    """K3D week-22 snapshot: solved tasks map 1:1 to GPU calls."""
    system = PMKRSystemLevelB()

    # Solve 400 math problems
    results = []
    for problem in math_benchmark[:400]:
        result = system.execute_hot_path(problem)
        results.append(result)

    # Validate sovereignty
    assert system.telemetry["gpu_calls"] == 154  # Only solved tasks
    assert system.telemetry["fallbacks"] == 0    # Zero fallbacks
    # Sovereignty: 154 GPU calls = 154 solved tasks (100%)

4. Level C: PM-KR Auditable Production

4.1 Requirements

Includes all Level A+B requirements, plus:

MUST:

Enforce Auditability Invariant (provenance tracking, transformation chains)
Provide compression metrics reporting (reference graph stats)
Include conformance test artifacts (all test results published)
Implement provenance verification (cryptographic signatures)
Support audit trail export (for compliance/debugging)

SHOULD:

Implement distributed provenance tracking (multi-agent lineage)
Provide visual tools for reference graph inspection
Support third-party audits (independent conformance validation)

MAY:

Implement blockchain-style provenance chains
Provide formal verification of critical procedures
Support real-time compliance dashboards

4.2 Validation Criteria

Test Suite (Level A+B tests + 4 additional tests):

Provenance Tracking Test

def test_provenance_tracking():
    """Node provenance includes full transformation chain."""
    node = pm_kr_system.get_node("word_rotation")

    assert "provenance" in node.metadata
    assert "source" in node.metadata.provenance
    assert "transformation_chain" in node.metadata.provenance
    assert "timestamp" in node.metadata.provenance
    assert "agent" in node.metadata.provenance

Compression Metrics Test

def test_compression_metrics():
    """System reports compression statistics."""
    metrics = pm_kr_system.get_compression_metrics()

    assert "total_nodes" in metrics
    assert "reference_count" in metrics
    assert "payload_size_mb" in metrics
    assert "reference_graph_size_mb" in metrics
    assert "compression_ratio" in metrics
    assert metrics["compression_ratio"] > 0.5  # At least 50%

Audit Trail Export Test

def test_audit_trail_export():
    """System exports complete audit trail."""
    audit_trail = pm_kr_system.export_audit_trail(node_id="word_rotation")

    assert "creation_event" in audit_trail
    assert "modification_events" in audit_trail
    assert "access_events" in audit_trail

    # Verify cryptographic signatures
    for event in audit_trail["modification_events"]:
        assert pm_kr_system.verify_signature(event)

Conformance Report Test

def test_conformance_report():
    """System generates full conformance report."""
    report = pm_kr_system.generate_conformance_report()

    assert report["level"] in ["A", "B", "C"]
    assert "test_results" in report
    assert all(result["status"] == "PASS" for result in report["test_results"])
    assert "compression_metrics" in report
    assert "sovereignty_metrics" in report
    assert "provenance_coverage" in report

4.3 Implementation Checklist

Level A+B checklists, plus:

Provenance System: Track creation, modification, access events
Cryptographic Signing: Sign canonical procedures and transformations
Compression Metrics: Real-time stats on reference graph efficiency
Audit Trail Export: JSON/JSONL export of full event history
Conformance Reporting: Automated report generation (all 12 tests)
Third-Party Audit: External validation of conformance claims
Documentation: Provenance schema, signature algorithms, audit procedures
Validation: Pass 12 total tests (5+3+4)

4.4 Example: Level C Provenance Tracking

K3D Reference (Shadow Copy Integration):

class PMKRSystemLevelC(PMKRSystemLevelB):
    """Level C: PM-KR Auditable Production."""

    def __init__(self):
        super().__init__()
        self.audit_journal = []
        self.compression_metrics = {
            "total_nodes": 0,
            "reference_count": 0,
            "payload_size_bytes": 0,
            "reference_graph_size_bytes": 0
        }

    def add_node(self, node: PMKRNode, agent: str = "system"):
        """Add node with full provenance tracking."""
        # Canonical check (Level A)
        super().add_node(node)

        # Record creation event
        event = {
            "type": "node_created",
            "node_id": node.id,
            "agent": agent,
            "timestamp": self._current_timestamp(),
            "provenance": {
                "source": node.metadata.get("provenance", "unknown"),
                "transformation_chain": node.metadata.get("transformation_chain", []),
            },
            "signature": self._sign_event(node)
        }
        self.audit_journal.append(event)

        # Update compression metrics
        self._update_compression_metrics(node)

    def export_audit_trail(self, node_id: str):
        """Export full audit trail for node."""
        events = [e for e in self.audit_journal if e["node_id"] == node_id]
        return {
            "node_id": node_id,
            "creation_event": events[0] if events else None,
            "modification_events": [e for e in events if e["type"] == "node_modified"],
            "access_events": [e for e in events if e["type"] == "node_accessed"]
        }

    def generate_conformance_report(self):
        """Generate full conformance report."""
        return {
            "level": "C",
            "test_results": self._run_all_tests(),
            "compression_metrics": self.compression_metrics,
            "sovereignty_metrics": self.telemetry,
            "provenance_coverage": len(self.audit_journal) / self.compression_metrics["total_nodes"],
            "timestamp": self._current_timestamp(),
            "signature": self._sign_report()
        }

    def _update_compression_metrics(self, node: PMKRNode):
        """Update compression statistics."""
        self.compression_metrics["total_nodes"] += 1

        # Count references
        ref_count = sum(len(refs) for refs in (node.refs or {}).values())
        self.compression_metrics["reference_count"] += ref_count

        # Estimate sizes
        payload_size = len(node.form_program or "") + len(node.meaning_program or "")
        self.compression_metrics["payload_size_bytes"] += payload_size
        self.compression_metrics["reference_graph_size_bytes"] += (ref_count * 32)  # 32 bytes per ref

    def _sign_event(self, node: PMKRNode):
        """Cryptographically sign event."""
        import hashlib
        content = f"{node.id}:{node.layer}:{self._current_timestamp()}"
        return hashlib.sha256(content.encode()).hexdigest()

    def _sign_report(self):
        """Sign conformance report."""
        import hashlib
        import json
        content = json.dumps(self.compression_metrics, sort_keys=True)
        return hashlib.sha256(content.encode()).hexdigest()

5. Migration Paths

5.1 From Traditional Knowledge Graphs

Challenge: Migrate from RDF/OWL/JSON-LD to PM-KR.

Strategy:

Identify Canonical Entities: Group duplicate triples by subject URI
Extract Procedural Semantics: Convert static properties to executable programs
Build Reference Graph: Replace repeated values with references
Validate Compression: Measure reduction (target >50%)

Example:

# Original RDF (duplicated)
:person1 :name "Alice" .
:person2 :name "Bob" .
:person3 :name "Alice" .  # Duplicate!

# PM-KR (canonical + refs)
:name_alice rdfs:label "Alice" ;
            pm:form_program "RENDER_NAME 'Alice'" .

:person1 pm:name_ref :name_alice .
:person2 pm:name_ref :name_bob .
:person3 pm:name_ref :name_alice .  # Reference, not duplicate

5.2 From Static Embeddings

Challenge: Migrate from embedding-only systems (no procedural source).

Strategy:

Reverse-Engineer Procedures: Use code generation to create procedural approximations
Canonical Clustering: Group similar embeddings, designate canonical representatives
Reference Linking: Lower-similarity nodes reference canonical clusters
Validate Reconstruction: Ensure procedural execution ≈ original embeddings

K3D Validation: Character Galaxy (21,915 chars) migrated from font files → procedural fonts (70% reduction).

5.3 From Monolithic Systems

Challenge: Migrate from single large model (LLM) to PM-KR compositional knowledge.

Strategy:

Knowledge Extraction: Use LLM to generate procedural knowledge base
Canonical Deduplication: Content-addressable IDs for procedures
Reference Graph Construction: Build symlink composition layers
Sovereignty Migration: Replace LLM inference calls with PTX execution

K3D Example: Math solver (originally sympy-based) → fully sovereign PTX (38.5% accuracy, 100% GPU).

6. Third-Party Verification Guide

6.1 Overview

This section provides step-by-step verification instructions for independent auditors validating PM-KR conformance claims. It complements the full Third-Party Verification Protocol in the Evidence Validation Matrix document.

Target Audience: Certification bodies, academic reviewers, peer implementers.

6.2 Quick Verification Checklist

Level A: PM-KR Core (5 tests required):

Clone repository and install dependencies
Run pytest tests/test_pmkr_level_a_*.py -v
Verify 5/5 tests passing
Check canonicality (zero duplicate IDs)
Validate compression (>50% reduction)
Confirm determinism (checksums match)
Issue verdict: PASS or FAIL

Level B: PM-KR Sovereign Runtime (8 tests required):

Complete Level A verification (prerequisite)
Run grep -r "import numpy\|import scipy\|import sympy" {hot_path_dirs}/
Verify zero matches (sovereignty static check)
Run benchmark with telemetry collection
Validate GPU calls / solved tasks = 1.0
Confirm execution determinism (same RPN → same output)
Issue verdict: PASS or FAIL

Level C: PM-KR Auditable Production (12 tests required):

Complete Level A+B verification (prerequisite)
Request provenance audit export
Validate 100% provenance coverage
Verify cryptographic signatures (SHA-256)
Check compression metrics reporting
Validate conformance report completeness
Issue verdict: PASS or FAIL

6.3 Environment Requirements

Minimum Requirements:

Component	Level A	Level B	Level C
Python	3.10+	3.10+	3.10+
GPU	Not required	NVIDIA 8GB+ VRAM	NVIDIA 8GB+ VRAM
CUDA	Not required	11.8+	11.8+
Disk Space	2GB	5GB	10GB
Network	Git clone only	Git clone only	Git clone only

Recommended (for K3D verification):

NVIDIA RTX 3060 (12GB VRAM) or equivalent
Ubuntu 22.04 LTS
16GB system RAM

6.4 Artifact Validation

Required Artifacts by Level:

Level A:

pmkr_level_a_test_report_YYYY-MM-DD.json (test results)
pmkr_level_a_reference_dataset.jsonl (sample nodes)
pmkr_level_a_signatures_YYYY-MM-DD.txt (SHA-256 checksums)

Level B (includes Level A + additional):

pmkr_level_b_telemetry_YYYY-MM-DD.jsonl (GPU call telemetry)
pmkr_level_b_benchmark_run_YYYY-MM-DD.log (execution log)
pmkr_level_b_forbidden_imports.txt (sovereignty blacklist)

Level C (includes Level A+B + additional):

pmkr_level_c_provenance_audit_YYYY-MM-DD.jsonl (audit trail)
pmkr_level_c_compression_metrics_YYYY-MM-DD.json (compression stats)
pmkr_level_c_conformance_report_YYYY-MM-DD.json (full report)

Validation Commands:

# Verify checksums for all artifacts
sha256sum -c pmkr_level_{a|b|c}_signatures_YYYY-MM-DD.txt

# Expected output: All files PASS

6.5 Common Verification Issues

Issue 1: Non-Deterministic Test Outputs

Symptom: Test passes locally but checksums don't match published results.

Diagnosis:

# Check for timestamp differences
diff \
  <(jq 'del(.timestamp)' published_report.json) \
  <(jq 'del(.timestamp)' verifier_report.json)

Resolution: Exclude timestamp fields from checksum comparison (per spec §10.7).

Issue 2: GPU Availability

Symptom: Level B tests fail with "CUDA device not found."

Diagnosis:

# Verify GPU availability
nvidia-smi

# Check CUDA version
nvcc --version

Resolution: Level B/C require NVIDIA GPU. Skip GPU tests only if documentation explicitly allows CPU fallback (non-normative for K3D).

Issue 3: Dependency Version Mismatches

Symptom: Tests fail due to library version differences.

Diagnosis:

# Compare installed vs pinned requirements
diff requirements-pinned.txt <(pip freeze)

Resolution: Use pip install -r requirements-pinned.txt exactly (no upgrades).

6.6 Verification Report Template (Simplified)

For the full verification report template, see Evidence Validation Matrix §11.5. This simplified version is for quick peer reviews:

# PM-KR Conformance Verification Report

**Implementation**: {Name} v{Version}
**Verifier**: {Name/Organization}
**Date**: {YYYY-MM-DD}
**Level Claimed**: {A|B|C}

## Verification Summary

- [ ] Artifacts obtained from: {URL}
- [ ] Checksums validated: {PASS|FAIL}
- [ ] Environment: Python {version}, CUDA {version}, GPU {model}
- [ ] Tests executed: {passed}/{total}

## Level A Results
- Canonicality: {PASS|FAIL}
- Reference Integrity: {PASS|FAIL}
- Determinism: {PASS|FAIL}
- Compression: {PASS|FAIL} ({ratio}%)
- Layer Composition: {PASS|FAIL}

## Level B Results (if applicable)
- Sovereignty Static: {PASS|FAIL}
- Sovereignty Runtime: {PASS|FAIL}
- Execution Determinism: {PASS|FAIL}
- Telemetry: {PASS|FAIL}

## Level C Results (if applicable)
- Provenance Coverage: {PASS|FAIL}
- Compression Metrics: {PASS|FAIL}
- Audit Trail: {PASS|FAIL}
- Conformance Report: {PASS|FAIL}

## Verdict
**Level A**: {PASS|FAIL}
**Level B**: {PASS|FAIL|NOT_VERIFIED}
**Level C**: {PASS|FAIL|NOT_VERIFIED}

## Notes
{Any discrepancies, environment issues, or clarifications}

**Verifier Signature**: {PGP fingerprint or name}

6.7 K3D Verification Readiness

Current Status (February 2026):

Conformance Level	Artifacts Status	Verification Readiness
Level A	🟠 Pending publication	Not ready (Q2 2026 target)
Level B	🟡 Partial (repo tests + run logs)	Not ready (Q3 2026 target)
Level C	🟠 In progress	Not ready (Q4 2026 target)

Blockers:

Level A: Test suite externalization (currently integrated in Knowledgeverse tests)
Level B: Telemetry exporter (telemetry captured but needs dedicated export)
Level C: Audit pack generator (provenance journal exists but needs consolidation)

Timeline:

Q2 2026: Level A artifacts published
Q3 2026: Level B artifacts published + first independent verification
Q4 2026: Level C artifacts published

7. Conformance Certification

7.1 Self-Attestation (Current Practice)

Implementers MAY self-certify conformance by:

Publishing test results (all required tests passing)
Providing public API/endpoint for third-party validation
Documenting procedural language and execution environment

K3D Self-Certification:

Level: Provisional B+ (run-log verified), Level C target
Test Results:
- Repo-verified: 28/28 Knowledgeverse tests
- Run-log verified: hot-path sovereignty and benchmark snapshots
- Pending: externalized PM-KR A/B/C conformance suite
Public Repo: https://github.com/danielcamposramos/Knowledge3D
Documentation: docs/vocabulary/ (full spec suite)

7.2 Third-Party Certification (Future)

W3C Community Group MAY establish third-party certification by:

Maintaining reference test suite (independent of K3D)
Running conformance tests on submitted implementations
Publishing certification registry

Proposed: W3C PM-KR Conformance Registry (similar to HTML5 validator).

8. Performance Benchmarks

8.1 Compression Benchmarks

Benchmark	Metric	Level A Target	Level B Target	Level C Target
Compression Ratio	(1 - ref_size / payload_size)	>50%	>60%	>70%
Reference Resolution	avg latency (ms)	<10ms	<1ms	<0.1ms (GPU)
Canonicalization	duplicate detection rate	>95%	>99%	>99.9%

K3D Results (run-log verified unless otherwise stated):

Compression: 70% (Character Galaxy: 87.7MB → 26.3MB)
Resolution: <0.1ms (PTX kernel, sub-100µs validated)
Canonicalization: 100% (content-addressable IDs)

8.2 Sovereignty Benchmarks

Benchmark	Metric	Level B Target	Level C Target
GPU Sovereignty	(GPU calls / solved tasks)	=1.0	=1.0
Fallback Rate	(fallbacks / total calls)	<1%	0%
External Deps	count in hot path	0	0

K3D Results (Math benchmark snapshot, 400 tasks):

GPU Sovereignty: 1.0 (154 GPU calls = 154 solved tasks)
Fallback Rate: 0% (zero fallbacks)
External Deps: 0 (PTX-only hot path)

9. Conclusion

PM-KR Conformance Profiles provide clear, testable paths for implementation:

Level A: Data model + composition (5 tests)
Level B: + Sovereign runtime (8 tests)
Level C: + Auditability + metrics (12 tests)

K3D Reference Snapshot:

Repo-verified Level B signals: Knowledgeverse integration tests + sovereign runtime checks
Level C status: target profile defined; full third-party auditable certification pack pending

Next Steps: Implement conformance test suite, establish W3C certification registry, onboard early adopters.

References

PM-KR Normative Model (normative data model and invariants)
PM-KR Problem Statement (motivation and broader impact)
K3D Test Suites: tests/test_knowledgeverse_*.py, tests/test_hot_path_sovereignty.py, tests/test_procedural_fonts.py
K3D Reference Implementation: https://github.com/danielcamposramos/Knowledge3D

Document Status: Draft Implementation Guide License: CC-BY-4.0 Version: 1.1 (February 20, 2026)