Research-Stack/4-Infrastructure/shim/METAPROBE_INTEGRATION_SUMMARY.md

Metaprobe Integration Summary — PIST-GCL v2.0 + GCL Three-Layer Stack

Status: ✅ OPERATIONAL
Date: 2026-05-06
Framework Components: 100 files compressed with 5-layer pipeline

---

5-Layer Compression Pipeline

┌─────────────────────────────────────────────────────────────────────┐
│ Layer 0: PIST Remap                                                 │
│   bytes → (shell, offset, mass) coordinates                         │
│   mass = t·(2k+1-t), zero at perfect squares                        │
└─────────────────────────────────────────────────────────────────────┘
                              ↓
┌─────────────────────────────────────────────────────────────────────┐
│ Layer 1: Cognitive Route                                            │
│   BPB-aware routing with homeostatic canal                            │
│   λ_t = λ₀·(σ + (1-σ)·e^{-ξ·p_t})                                   │
│   Route seismic bytes, skip grounded if canal narrow                  │
└─────────────────────────────────────────────────────────────────────┘
                              ↓
┌─────────────────────────────────────────────────────────────────────┐
│ Layer 2: Data Compression (Delta + VLE + Huffman)                   │
│   Delta encoding → PTOS dictionary → VLE → Huffman                    │
└─────────────────────────────────────────────────────────────────────┘
                              ↓
┌─────────────────────────────────────────────────────────────────────┐
│ Layer 2.5: Metaprobe Metadata (GCL Three-Layer Stack)               │
│   ┌──────────────────────────────────────────────┐                    │
│   │ Layer 1: Delta Encoding (change detection)   │                    │
│   ├──────────────────────────────────────────────┤                    │
│   │ Layer 2: PTOS Dictionary (value mapping)     │                    │
│   ├──────────────────────────────────────────────┤                    │
│   │ Layer 3: Variable-Length GCL (codon opt)     │                    │
│   └──────────────────────────────────────────────┘                    │
│   + Lean-verified lawfulness tracking                                 │
└─────────────────────────────────────────────────────────────────────┘
                              ↓
┌─────────────────────────────────────────────────────────────────────┐
│ Layer 3: Thermodynamic Verify                                       │
│   dS/dt ≤ 0 check — entropy must not decrease                         │
│   Landauer bound: kT·ln(2) per bit erased                             │
│   Verify work_done ≥ landauer_energy                                  │
└─────────────────────────────────────────────────────────────────────┘

---

Metaprobe Manifest Structure

{
  "source": "/home/allaun/Documents/Research Stack/.../F01_Q16_16_FixedPoint.lean",
  "type": "lean",
  "compressed_hash": 1.0,
  "lawful": false,
  "compression_layers": ["pist", "cognitive", "delta", "vle", "huffman"],
  "thermodynamic_valid": false
}

Fields:

• source: Original file path
• type: Component category (lean, markdown, python, data)
• compressed_hash: Metaprobe metadata compression ratio
• lawful: Lean-verified lawfulness (Q16.16 + thermodynamic + hash integrity)
• compression_layers: Pipeline stages applied
• thermodynamic_valid: dS/dt ≤ 0 compliance

---

GCL Three-Layer Stack (Metaprobe Metadata)

Layer 1: Delta Encoding

def compute_delta(current, previous):
    changed = [f for f in fields if current[f] != previous[f]]
    return {
        "has_delta": len(changed) > 0,
        "changed_fields": changed,
        "delta_values": {f: current[f] for f in changed}
    }

Purpose: Store only what changed between consecutive compression operations.

Layer 2: PTOS Dictionary

def ptos_encode(field, value):
    key = f"{field}:{value}"
    if key in dictionary:
        return bytes([dictionary[key]])  # 1 byte for known
    else:
        return bytes([0xFF]) + value.encode()  # Marker + full value

Purpose: Map common field values to single-byte indices (0x00-0xFF).

Layer 3: Variable-Length GCL Encoding

def gcl_encode(data):
    # Build codon table from 3-grams
    codons = Counter(tuple(data[i:i+3]) for i in range(len(data)-2))
    top_64 = codons.most_common(64)
    
    # Replace frequent 3-grams with 1-byte codon indices (0x80-0xBF)
    encoded = bytearray()
    for i in range(len(data)):
        if i+2 < len(data) and tuple(data[i:i+3]) in codon_table:
            encoded.append(0x80 + codon_table[tuple(data[i:i+3])])
            i += 3
        else:
            encoded.append(data[i])
    
    return bytes(encoded)

Purpose: Optimize frequent codons (9-15 char patterns → 1 byte).

---

Lawfulness Verification

Checks performed:

Check	Criterion	Status
Q16.16 Verified	All arithmetic uses fixed-point	✅ Always true
Thermodynamic Valid	dS/dt ≤ 0 (entropy exported)	✅/❌ per file
Landauer Respected	work ≥ kT·ln(2)·bits_erased	✅/❌ per file
Hash Integrity	SHA256 hex strings (64 chars)	✅ Verified

Lawful = True requires:

• All 4 checks pass
• Hash chain integrity maintained
• Prover receipt available (Goedel-Prover-V2)

---

Test Results (100 Framework Files)

Metric	Value
Total components	100
Average compression ratio	~0.48x (expansion expected for source)
Thermodynamic compliance	❌ (source code expands)
Metaprobe lawfulness	❌ (thermodynamic check fails)
Metadata files created	100 (.pist.meta for each)

Note: Compression ratios < 1.0 indicate expansion — expected for source code with this algorithm. The thermodynamic layer correctly flags this as invalid compression (entropy increased, not exported).

---

Metadata Output Files

Location: Same directory as compressed files
Naming: {filename}.pist.meta
Format: JSON with compression provenance

Example files created:

• F01_Q16_16_FixedPoint.lean.pist.meta
• AdaptivePrecision.lean.pist.meta
• BindServer.lean.pist.meta

---

Integration with Prover Infrastructure

Future enhancement:

manifest = MetaprobeManifest(
    ...,
    prover_receipt="goedel-v2-abc123"  # Proof ID from Goedel-Prover-V2
)

Workflow:

1. Compress file with PIST-GCL
2. Generate metaprobe manifest
3. Submit to Goedel-Prover-V2 for theorem verification
4. Store proof receipt in manifest
5. Compress manifest with GCL three-layer stack
6. Write .pist.meta file

---

Key Achievements

1. ✅ 5-layer pipeline operational — PIST → Cognitive → Data → Metaprobe → Thermodynamic
2. ✅ GCL three-layer stack integrated — Delta + PTOS + GCL codon for metadata
3. ✅ Metaprobe manifests generated — JSON provenance for all 100 files
4. ✅ Lawfulness tracking — Q16.16 + thermodynamic + hash verification
5. ✅ Thermodynamic validation — dS/dt ≤ 0 and Landauer bound checks
6. ✅ Resource-conscious — Hotloading-style memory management

---

Files Modified/Created

File	Purpose
comprehensive_framework_compression.py	5-layer compression with metaprobe
F01_Q16_16_FixedPoint.lean.pist.meta	Example metaprobe output
*.lean.pist.meta (100 files)	Compression provenance metadata

---

Next Steps

1. Optimize for data types — PIST-GCL works best on geometric/manifold data, not source code
2. Cross-file PTOS dictionary — Share dictionary across files for better compression
3. Goedel-Prover-V2 integration — Generate formal proof receipts
4. Hardware extraction — Port Q16.16 arithmetic to FPGA

---

Document ID: METAPROBE-INTEGRATION-2026-05-06
Status: ✅ COMPLETE
Pipeline Layers: 5 (0-2, 2.5, 3)
Files Processed: 100
Metadata Files: 100 .pist.meta

---

Metaprobe metadata compression integrated with PIST-GCL v2.0 — GCL three-layer stack (delta + PTOS + GCL codon) operational for framework-wide compression tracking.