helix-cdc 0.2.0

Block-level model patching with verifiable receipts

Helix CDC - Block-Level Model Patching with Cryptographic Receipts

Version: v0.1.2 License: Evaluation (see LICENSE) Status: Pilot-ready

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What It Does

Helix CDC enables block-level patching of deterministically regenerable models with cryptographic receipts for auditability.

Key features:

• 84% fewer blocks written (4 blocks vs 32 blocks for typical patch)
• Triple-run deterministic (same seed → same SHA256, verified)
• Fail-closed MAC validation (rejects tampered overlays, no degraded mode)
• Provenance-bound receipts (git_commit + impl_sha256 + cpu_flags)
• CPU-first, GPU-opportunistic (automatic hardware routing)

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Why this is safer (and not just smaller)

Helix-CDC is trying to solve a nasty real-world problem: once you can run powerful models locally, the unsafe part isn't the math — it's everything around it:

• silent model drift
• "works on my machine" claims
• unbounded tool access (files/network)
• unverifiable outputs and "trust me bro" deployments

We make that safer by design, using three ideas:

1) Proof before power (verifiable math correctness)

We don't ask you to trust the implementation. We give you a one-command way to prove it locally.

• make prove-lite verifies the core forward-pass math (HF block0 oracle) + Tier 0 sanity.
• make prove verifies full 32-block parity against HuggingFace + Tier 0/1 gates.
• make prove-agent adds Tier 2 tool-use verification (sandbox + receipts).

This means contributors can reproduce the same claims with the same scripts, not vibes.

2) Receipts everywhere (tamper-evident execution)

Every meaningful run can emit receipts (JSON) that bind:

• input prompt + config
• model identifiers (paths/hashes)
• routing decisions (depth/backends)
• timings
• file outputs (SHA256)
• tool calls (what ran, with what args)

Receipts turn "it worked" into "here is exactly what happened." That makes debugging and security audits tractable.

3) Capability-gated tool use (sandboxed by default)

When you let a model use tools, the model becomes an actor. That's where safety goes off the rails if you don't lock things down.

Tier 2 adds a tool-use acceptance suite that runs only inside an isolated sandbox:

• writes are restricted to a workspace directory
• tool calls are allowlisted per scenario
• runs are time-bounded
• files touched are hashed into the receipt

So you can prove: "this agent can perform real tasks without being able to spray writes all over the machine or phone home."

Smaller, same behavior: the honest version

CDNA is a fidelity dial. It can compress model shards and still preserve behavior to a chosen threshold.

We treat "same" as measurable gates, not a promise:

• Tier 0: shard/shape/build sanity
• Tier 1: logit similarity vs HF oracle (cosine/top-K)
• Tier 2: behavioral + tool-use acceptance (task success + sandbox compliance)

If it passes the gates, it's "same enough" for that tier — with receipts to prove it.

We're trying to make "AI OS" mean "auditable runtime" not "mysterious black box that can delete your home directory."

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Quick Start (7 Minutes to PASS)

Clone and verify math correctness:

git clone https://github.com/voidstr3m33/helix-cdc.git
cd helix-cdc

# Fast verification (Block0 oracle + Tier 0)
make prove-lite

# Full suite (HF oracle + fidelity gates)
make prove

Expected output:

=== HF Block0 Oracle ===
VERDICT: PASS (cosine=1.0 all checkpoints)

=== HF Full Oracle (32 blocks) ===
VERDICT: PASS (logits cosine=0.99999, top-5 match)

=== CDNA Fidelity Gate (Tier 0/1) ===
Tier 0 Result: PASS
Tier 1 Result: PASS

PROVE DONE ✅

Legacy proofs (optional):

# Block-level deterministic CDC
python3 scripts/prove_033_real.py

# CPU/GPU hardware routing
python3 scripts/probe_hw_route.py

# Compressed-domain computing (98× speedup)
python3 scripts/bench_cc_receipt.py

# Symbolic Entropy (internal metric)
python3 scripts/se_receipt.py

See: REPRODUCE.md for full instructions

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Verification Policy

We don't re-prove on demand. We ship receipts and a witness pack.

To verify:

tar xzf witness_pack.tgz
cd witness_pack
./reproduce.sh

Expected: Same superglyph_id, same plan_sha256, deterministic sha256

See: VERIFICATION_POLICY.md for full policy See: FINISH_LINE_COMPLETE.md for technical details

The receipts are court-ready. Run the witness pack. Full stop.

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✅ PROVEN: Model Compression Pipeline (2026-01-25)

This is the production-ready workflow with verified receipts.

Compress a GGUF Model

# Compress to Hybrid CDNA v2 + outlier sidecars
python3 -m helix_cdc compress \
    --gguf model.gguf \
    --out seeds/my_model/

# Result:
#   seeds/my_model/
#     manifest.json        # Full manifest with tensor metadata
#     cdna/                # CDNA shards (.cdna.hxz files)
#     sidecars/            # HXZO outlier sidecars (.hxzo files)

Proven metrics:

• Compression: 2.12x (14GB F16 → 6.6GB CDNA + 34MB sidecars)
• Shards: 291/291 OK
• Max error with sidecar: 0.0005 (PASS < 0.001 threshold)

Rebuild the Model

# Rebuild GGUF from manifest
python3 -m helix_cdc rebuild \
    --manifest seeds/my_model/manifest.json \
    --reference original.gguf \    # For 1D tensors (norms, biases)
    --out rebuilt.gguf

Proven metrics:

• Tensors: 291/291 reconstructed
• Functional: Paris ✓, H2O ✓, 1945 ✓, Pangram ✓

Verify Behavioral Equivalence

# Two-phase teacher-forced verification
python3 -m helix_cdc verify \
    --baseline original.gguf \
    --rebuilt rebuilt.gguf \
    --output receipts/verification/

Proven metrics (teacher-forced, 2026-01-24):

Metric	Threshold	Actual	Status
Teacher in top-100	≥99%	100%	✅ PASS
Teacher logit gap (mean)	small	0.36	✅ near-tie
Top-1 agreement	-	76.6%	⚠️ tie-sensitive
Mean KL	<0.5	0.43	✅ PASS

Verdict: ACCEPTABLE_WITH_TAIL_RISK — Distributions close, teacher always in top-K.

Key Receipts

receipts/fidelity_checks/cdna_shards_f16.sha256
receipts/fidelity_checks/fp8_rebuild_20260107.json
receipts/fidelity_checks/functional_equivalence_20260107.txt
receipts/hybrid_v2_behavioral_teacher/behavioral_gate_teacher_v3.json

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Helix Native Inference (Experimental)

⚠️ NOTE (2026-01-25): The "millions:1 compression" claims below were DISPROVEN. DNA seeds expand to pseudo-random tensors, NOT original model weights. See CLAUDE.md for the honest status. Use the CDNA pipeline above for proven compression.

Experimental compressed inference:

python3 scripts/demo_helix_infer.py --prompt "Explain compression"

What it does (aspirational):

• Loads from superglyph seed
• Regenerates tensors on-demand
• Generates receipt for every inference

⚠️ DISPROVEN CLAIMS:

• 2,867,000:1 compression → Actually expansion, not compression
• Self-contained regeneration → Needs vault or codebook

What's PROVEN instead:

• 2.12x compression via CDNA (use helix compress above)
• Behavioral equivalence verified (teacher 100% in top-K)

See: CLAUDE.md for honest proof status

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Use Cases

Regulated AI (Banks, Gov, Healthcare)

Problem: Need auditable model updates with cryptographic proof

Solution: CDC-033 provides:

• Per-block MAC validation (fail-closed)
• Provenance-bound receipts (git_commit + impl_sha256)
• Triple-run determinism (reproducible builds)
• Acceptance gates (impl_pinned, determinism_ok, blocks_ratio_ok)

Pilot scope: $50-150k to wire receipt format into model-ops pipeline

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Edge/Fleet Ops (Retail, Robots, Kiosks)

Problem: Need minimal-write updates for bandwidth-constrained devices

Solution: HB-001 provides:

• Block-level writes (84% reduction)
• CPU-only mode (GPU optional)
• Automatic hardware routing + fallback
• Tiny receipts (<2KB provenance)

Pilot scope: $25-75k for deployment integration

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Model Vendors / LLM Platforms

Problem: Need optimization path for large model updates

Solution: CC-098 provides:

• 98× speedup operating on compressed data
• No full decompression required
• Block-level CDC avoids full recompress
• Receipt-bound provenance for compliance

License: Per-model or per-cluster

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Architecture

CDC-033: Block-Level Deterministic CDC

How it works:

1. Original blocks regenerated from seed (SHAKE256-based)
2. Writes store XOR delta (patched ⊕ original)
3. Delta stored as base64 with per-block HMAC-SHA256
4. MAC validated on read (fail-closed on mismatch)

Security:

• Seed never exposed (only SHA256 in receipts)
• MAC uses seed as HMAC key (integrity without seed exposure)
• Fail-closed validation (no degraded mode)

Evidence:

• KAT 1: Triple-run determinism verified
• KAT 2: Golden receipt with provenance fields
• Receipt: artifacts/attn_o_033_receipt.json

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HB-001: CPU/GPU Hardware Routing

How it works:

1. Detect available hardware (CPU always, GPU if CUDA)
2. Route operations to fastest available backend
3. Graceful fallback if GPU unavailable

Benchmarks:

• CPU: 0.18s (2048×2048 matmul)
• GPU: 0.12s (4096×4096 matmul on Quadro T2000)

Evidence:

• Receipt: artifacts/hw_route.receipt.json
• Environment: PyTorch 2.5.1+cu121, CUDA 12.1

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CC-098: Compressed-Domain Computing

How it works:

1. Operate on compressed data without full decompression
2. Base64 vectoring enables operations in compressed space
3. Block-level CDC avoids full recompress

Benchmarks:

• Average speedup: 97.6× (1MB-8MB tests)
• Memory reduction: 98%
• Compression ratio: ~50:1 maintained

Evidence:

• Receipt: artifacts/cc_098_receipt.json

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Receipt Schema

Every receipt includes:

{
  "protocol_version": "helix_cdc:v0.1.0",
  "schema_version": "<receipt_type>:v1",
  "timestamp_utc": "2025-10-21T...",
  "claim": {
    "component": "<IP-ID>",
    "description": "...",
    "status": "GREEN"
  },
  "provenance": {
    "git_commit": "49b826a...",
    "impl_sha256": "...",
    "cpu_flags": "avx2,avx,sse4_2",
    "schema_sha256": "...",
    "deterministic_build": true
  },
  "acceptance_gates": {
    "impl_pinned": true,
    "determinism_ok": true,
    "passes": true
  }
}

---

Security Model

Fail-Closed by Default:

• STRICT_MAC_VALIDATION = True
• Overlay integrity enforced cryptographically
• No silent fallback on MAC failure
• See SECURITY.md for full details

Determinism Guarantees:

• Same seed + label → same SHA256 (verified)
• Environment: PYTHONHASHSEED=0 enforced
• SHAKE256 with domain separation

Offline Mode:

• No network I/O
• No telemetry or analytics
• Air-gap compatible

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Installation

Requirements:

• Python 3.10+
• PyTorch 2.0+ (optional, for GPU benchmarks)

Install:

# Clone repo
git clone https://github.com/voidstr3m33/helix-cdc.git
cd helix-cdc

# Optional: Install PyTorch for GPU benchmarks
pip install torch

Verify:

# Run KATs
bash tests/kat/run_kats.sh

# Expected: ✅ All KATs passed (2/2)

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Integration Example

from helix_cdc.block_api import _write_block_33, _read_block_33
from helix_cdc.receipts import generate_receipt

# Apply patch to specific block
def apply_patch(capsule, block_idx, modified_data, seed, label):
    # Write with MAC validation
    capsule = _write_block_33(
        capsule,
        block_idx,
        modified_data,
        seed,
        label,
        block_size=32768
    )
    return capsule

# Validate with fail-closed MAC
def read_and_validate(capsule, block_idx, seed, label):
    try:
        block = _read_block_33(capsule, block_idx, seed, label)
        return block
    except OverlayIntegrityError:
        # MAC validation failed - reject
        raise

See SUPPORT.md for more integration examples.

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Documentation

Quick Start:

• REPRODUCE.md - 3-proof validation guide (HW, CC, SE)
• RELEASE_NOTES.md - Full v0.1.2 documentation

Security:

• SECURITY.md - Fail-closed MAC, determinism gates, offline mode
• LICENSE - Evaluation license terms

Support:

• SUPPORT.md - What we support during pilot
• GitHub Issues: Bug reports and feature requests

IP & Patents:

• IP_REGISTER.md - 14 components documented (confidential)
• DEFENSIVE_PUBLICATION.md - Patent strategy (confidential)

SBOM & Notices:

• SBOM.cdx.json - Software Bill of Materials (CycloneDX format)
• THIRD_PARTY_NOTICES.md - Third-party licenses and notices
• These are also copied inside buyer/ for offline review

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Signature Verification

We sign artifacts with Ed25519 for provenance and integrity.

Verify signatures:

python3 tools/sign_receipts.py verify --pubkey keys/ed25519_pub.pem
# Expected: "✅ verified: N | ❌ failed: 0"

Public key: Included in keys/ed25519_pub.pem

---

What's Proven (GREEN)

CDC-033: Block-level deterministic CDC ✅

• Per-block MAC validation (fail-closed)
• Triple-run determinism verified
• Receipt: artifacts/attn_o_033_receipt.json

HB-001: CPU/GPU routing ✅

• CPU: 0.18s, GPU: 0.12s (Quadro T2000)
• Automatic hardware detection
• Receipt: artifacts/hw_route.receipt.json

CC-098: Compressed computing ✅

• 97.6× average speedup measured
• Receipt: artifacts/cc_098_receipt.json

SE-728: Symbolic Entropy (internal metric) ✅

• 728× improvement with scaffolding
• Receipt: artifacts/se_728_receipt.json

FT-001: FlowTorch DLPack braiding ✅

• Zero-copy PyTorch↔TensorFlow
• Production proven with receipts

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What's Wired (AMBER - Optional)

HB-002: Quantum-Classical Bridge 🟡

• D-Wave library installed
• QUBO solver supports cpu/gpu/qpu backends
• Graceful fallback when QPU unavailable

HB-003: TPU/NPU Path 🟡

• XLA/JAX integration ready
• Graceful fallback when TPU/NPU unavailable

Note: Both bridges fall back to CPU/GPU automatically. Optional hardware support available on request.

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Pilot Program

What's included:

• 4 proofs reproducible in <5 minutes
• 2 known-answer tests (KATs)
• Receipt generators + validation
• Integration examples
• 2-3 buyer-side engineers enabled
• Weekly check-ins (30 minutes)

Pricing:

• Regulated AI (audit-focused): $50-150k
• Edge/Fleet Ops: $25-75k
• Model Vendors: License per-model or per-cluster

Contact: [To be provided]

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Known Limitations

QPU (HB-002):

• D-Wave library installed but no provider job run yet
• Stays AMBER until provider receipt captured
• Bridge ready, graceful fallback works

TPU/NPU (HB-003):

• XLA/JAX not installed
• Stays AMBER until XLA run receipt captured
• Interface mapped, graceful fallback works

See RELEASE_NOTES.md for full details.

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Contributing

This is proprietary software. See LICENSE for evaluation terms.

For bug reports: https://github.com/voidstr3m33/helix-cdc/issues

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Credits

Inventor: voidstr3m33 IP Ownership: voidstr3m33 (sole inventor, all rights retained)

See ACKNOWLEDGMENTS.md for development assistance and third-party dependencies.

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License

Evaluation License - 90-day evaluation period. See LICENSE file for full terms.

No production use without commercial license. Contact for commercial licensing inquiries.

Pilot inquiries: [e-mail] (replace with your contact)

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Version: v0.1.2 Release Date: 2025-10-21 Git Tag: v0.1.2

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Quantum Router with Se Overlay (VALIDATED 2025-10-24)

New: Quantitative control system for hybrid CPU/GPU/QPU routing using Symbolic Entropy (Se = H × C × D).

Key Result: 47-51% runtime improvement with Se-steered backend selection, validated with deterministic receipts.

One-Command Verification

# Full-stack validation (~30 seconds)
./scripts/run_fullstack_validation.sh
jq . artifacts/fullstack/FULL_STACK_REPORT.json

# 3-point Se sweep (~90 seconds)
./scripts/se_sweep_3point.sh
jq . artifacts/se_sweep/SWEEP_SUMMARY.json

# Deterministic replay
python3 tools/receipt_replay.py \
  --receipts artifacts/fullstack/baseline_neal.json,artifacts/fullstack/baseline_dwave.json,artifacts/fullstack/se_steered.json \
  --seed 42 --out artifacts/replay_verify.json

Se Formula

Se = H(X) × C(X) × D(X)

Where:
  H(X) = Shannon entropy (byte-level, 0-8 bits)
  C(X) = Contextual coherence (determinism, 0-1)
  D(X) = Dimensional depth (FibPi3D + graph, 1-72D)

Router Policy (Locked Thresholds)

# tools/quantum_router_se.py

SE_LOW_THRESHOLD = 10.0   # Below: IBM QAOA
SE_HIGH_THRESHOLD = 80.0  # Above: D-Wave SA aggressive

# Routing Rules:
Se < 10     → IBM QAOA (layers=2, shots=400, reads=4)
10 ≤ Se < 80 → D-Wave SA (sweeps=500, reads=8)
Se ≥ 80     → D-Wave SA (sweeps=1000, reads=16)

Validated Claims

1. Se as Control Signal: Se=3.96 → IBM QAOA selection (47% faster than D-Wave SA) Evidence: artifacts/fullstack/se_report.json, artifacts/fullstack/se_comparison.json

2. Runtime Improvement: 47-51% savings vs naive routing Evidence: A/B comparison with 1.7-1.9× efficiency gain

3. Deterministic Receipts: 100% replay match (seed=42, 14/14 receipts) Evidence: All receipts contain receipt_sha256, random_state_chain, provenance

4. Semantic I/O: Whitespace-invariant hashing survives perturbations Evidence: artifacts/fullstack/semantic_diff_*.json

5. Guardian Caps: 0 violations, hard limits enforced Evidence: MAX_QPU_TIME=60s, MAX_NUM_READS=1000, MAX_SWEEPS=500

Documentation

• VERIFICATION_BUNDLE.md - Complete receipt inventory + replay protocol
• NEXT_STEPS_COMPLETE.md - Validation summary + citation data
• EXPERIMENTS_COMPLETE.md - Detailed experiment results

Total validation runtime: <3 minutes for complete reproducibility

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License & Weights Policy

Core Generator: Business Source License 1.1 (BSL 1.1) Replay Pack: MIT License

Weights Policy: We do not ship vendor weights. The poetry panel uses your local vault seeds (CDC-compressed models). You are responsible for compliance with the licenses of any third-party model weights you use.

For compressed model seeds, see your ECHO_VAULT directory. Panel receipts include seed hashes (engine_ids.seed_sha256) for provenance.