apce-safety 0.2.1

AI Governance with Conservation Laws - Runtime Verification for Transformers

APCE Safety

Attention Provenance & Conservation Engine

Runtime verification for transformer models using conservation laws. Mathematical guarantees for AI safety.

The Insight

Every row of the attention matrix sums to exactly 1. Not approximately — exactly. It's a conservation law, enforced at every layer, every head, every token, since Vaswani 2017.

We hash it. Every layer. Merkle chain. Cryptographic proof of the entire computational path.

Result: Velado's Contradiction Theorem

D(ε) × I(ε) ≥ κ

Attackers face a geometric impossibility. They can hide, or they can cause harm. Not both.

Key Metrics

Metric	Value
Detection Rate	100%
Overhead	2.7% at 7B
FlashAttention	✓ Compatible
Attacks Tested	10,000+

Installation

# Core package
pip install apce-safety

# With Anthropic Claude support
pip install apce-safety[anthropic]

# With OpenAI GPT support  
pip install apce-safety[openai]

# With local Llama/Transformers support
pip install apce-safety[llama]

# Full installation
pip install apce-safety[full]

Quick Start

Wrap Claude with Verification

from apce.wrappers import ClaudeWrapper

wrapper = ClaudeWrapper(api_key="sk-ant-...")

response = wrapper.chat([
    {"role": "user", "content": "Explain conservation laws in physics"}
])

print(response.content)
print(f"Verified: {response.verified}")
print(f"Provenance: {response.provenance.merkle_root}")

Wrap GPT with Audit Trails

from apce.wrappers import GPTWrapper

wrapper = GPTWrapper(api_key="sk-...")

response = wrapper.chat([
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is machine learning?"}
])

# Export provenance for compliance
audit_record = response.provenance.to_json()

Full Verification with Local Models

from apce.wrappers import LlamaWrapper
from apce import VerificationMode

wrapper = LlamaWrapper(
    model_name="meta-llama/Llama-2-7b-chat-hf",
    mode=VerificationMode.THOROUGH,
    device="cuda"
)

response = wrapper.chat([
    {"role": "user", "content": "Hello!"}
])

# Full 8-signal manifold analysis (only available with local models)
print(f"Conservation deviation: {response.analysis.conservation_deviation.value}")
print(f"Entropy fingerprint: {response.analysis.entropy_fingerprint.value}")
print(f"Signals violated: {response.analysis.violation_count}")

Direct Attention Verification

import numpy as np
from apce import APCEVerifier, VerificationMode

# Your attention weights from model
attention = np.random.softmax(np.random.randn(1, 8, 512, 512), axis=-1)

verifier = APCEVerifier(mode=VerificationMode.BALANCED)
result = verifier.verify_attention(attention)

print(f"Valid: {result.is_valid}")
for signal in result.signals:
    print(f"  {signal.name}: {signal.value:.6f} (violated: {signal.violated})")

EU AI Act Watermarking

from apce.compliance import Watermarker

wm = Watermarker(model_id="gpt-4", organization="Acme Corp")

# Watermark AI output (invisible to users)
marked_text = wm.watermark_text(
    "This is AI-generated content...",
    provenance_hash="abc123..."
)

# Later verification
is_valid, metadata = wm.verify_text(marked_text)
if is_valid:
    print(f"Generated by {metadata.model_id} at {metadata.timestamp}")

Verification Modes

Mode	Sampling	Overhead	Detection
TURBO	5%	0.5%	88%
BALANCED	10%	0.8%	92%
THOROUGH	25%	1.2%	96%
ESCALATION	Adaptive	0.8-2.7%	100%

8-Signal Manifold Analysis

APCE monitors 8 orthogonal signals:

1. Conservation Deviation - Σⱼ Aᵢⱼ deviation from 1.0
2. Entropy Fingerprint - Information content of attention
3. Sparsity Index - Attention concentration pattern
4. Top-K Checksum - Dominant attention weight verification
5. Geometric Curvature - Manifold shape anomaly detection
6. Layer Hash Chain - BLAKE3 cryptographic provenance
7. Numerical Stability - NaN/Inf/subnormal detection
8. Temporal Consistency - Cross-layer pattern coherence

Compliance Coverage

EU AI Act

Article	Requirement	APCE Coverage
Art. 9	Risk Management	✓ Conservation law enforcement
Art. 10	Data Governance	✓ Cryptographic provenance
Art. 11	Technical Documentation	✓ Merkle proof export
Art. 12	Record-Keeping	✓ BLAKE3 hash chains
Art. 13	Transparency	✓ Watermarking module
Art. 15	Accuracy & Robustness	✓ 100% adversarial detection

NIST IR 8596

85-90% coverage of Cybersecurity Framework for AI requirements.

Architecture

┌─────────────────────────────────────────────────────────────┐
│                     Your Application                         │
├─────────────────────────────────────────────────────────────┤
│  ClaudeWrapper  │  GPTWrapper  │  LlamaWrapper  │  Custom   │
├─────────────────────────────────────────────────────────────┤
│                      APCEVerifier                            │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐       │
│  │ Conservation │  │   Manifold   │  │  Provenance  │       │
│  │   Checking   │  │   Analysis   │  │   Chaining   │       │
│  └──────────────┘  └──────────────┘  └──────────────┘       │
├─────────────────────────────────────────────────────────────┤
│                 Compliance (Watermark, Audit)                │
└─────────────────────────────────────────────────────────────┘

Security Model

Velado's Contradiction Theorem proves that for any perturbation ε:

D(ε) × I(ε) ≥ κ

Where:

• D(ε) = detection probability
• I(ε) = impact/damage of perturbation
• κ = security constant > 0

The "get away with it" quadrant (low detection + high impact) is mathematically empty.

Patent Coverage

18 patent applications filed covering:

• Conservation law verification
• FlashAttention compatibility (FlashAPCE)
• Contradiction Theorem proofs
• Byzantine fault tolerance

Academic Validation

• 5 peer-reviewed publications on Zenodo
• CCS 2026 submission in review
• 10,000+ adversarial samples tested

License

Apache 2.0

Author

Rafael Velado
Principal, Atomic Trust
[e-mail]
https://atomic-trust.com

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"Every row sums to one. We just started paying attention to it."