# Part 7: Proposed Implementation Details

> **Note**: This section describes Web4 component architecture. Core mechanisms (witness marks, VCM, SNARC, dictionary entities) are vision-level. The governance stack, R7 framework, ACP protocol, and supporting infrastructure have operational reference implementations — see Section 7.0.1 for current status.

## 7.0. Implementation Status and Critical Blockers

### 7.0.0. Published Packages (2026-04-29)

Four core packages are public on crates.io and PyPI as of v0.1.1, all AGPL-3.0-or-later:

| Package | Registry | Version | Install |
|---------|----------|---------|---------|
| `web4-core` (Rust) | [crates.io](https://crates.io/crates/web4-core) | 0.1.1 | `cargo add web4-core` |
| `web4-core` (Python) | [PyPI](https://pypi.org/project/web4-core/) | 0.1.1 | `pip install web4-core` |
| `web4-trust-core` (Rust) | [crates.io](https://crates.io/crates/web4-trust-core) | 0.1.1 | `cargo add web4-trust-core` |
| `web4-trust` (Python) | [PyPI](https://pypi.org/project/web4-trust/) | 0.1.1 | `pip install web4-trust` |

`web4-core` provides the LCT primitive (Ed25519 keypair binding, parent/child lineage, status lifecycle, hardware-binding ladder), T3 / V3 trust tensors with fractal RDF sub-graphs, identity coherence (`C × S × Φ × R`), Ledger trait + two backends (`InMemoryLedger`, `LocalLedger` — file-based, hash-chained, JSONL), and the AttestationEnvelope hardware-trust primitive with anchor verifiers for TPM2 / FIDO2 / Secure Enclave / software fallback. 52 unit tests + 4 doctests in the Rust crate.

`web4-trust-core` adds entity trust persistence, witnessing primitives, decay models, and a TrustStore backend. 57 tests.

v0.1.0 was yanked from crates.io the same day it shipped after clean-install verification caught a Python import-path defect (the wheel shipped without `__init__.py`); v0.1.1 is canonical. The full publish narrative — including the discipline rule that would have prevented the defect pre-publish — is at [`docs/proof/PUBLISHED.md`](https://github.com/dp-web4/web4/blob/main/docs/proof/PUBLISHED.md).

### 7.0.1. Current Implementation State

| Component | Status | Notes |
|-----------|--------|-------|
| LCT data structures | ✅ Implemented | Core presence tokens; Go, TS, Python, Rust libraries |
| T3/V3 tensor calculations | ✅ Implemented | Trust scoring operational; EMA-weighted reputation deltas |
| Identity coherence scoring | ✅ Implemented | D9 metrics, self-reference detection |
| Witness system framework | ⚠️ Partial | 8 witness types, not persisted to chain |
| Coherence regulation | ⚠️ Partial | Unified trust decay: 5 composable models (24/24 checks) |
| Blockchain consensus | ⚠️ **Partial** | Hash-chained ledger + Merkle-tree heartbeat aggregation (8.26× reduction) |
| VCM recipient attestation | ❌ Not implemented | Vision only |
| ATP/ADP settlement | ⚠️ **Partial** | ATP recharge, team pools, dynamic costs, game-theoretic deterrence proven |
| **Hardware binding** | ⚠️ **Partial** | TPM 2.0, EK chain verified, multi-device binding (4 anchor types, 45/45 checks) |
| **R7 action framework** | ✅ **Implemented** | R7 executor + Hardbound 10-layer integration (30/30 checks) |
| **ACP protocol** | ✅ **Implemented** | Plan→intent→approve→execute→record; E2E with R7+Hardbound (28/28 checks) |
| **Sybil resistance** | ✅ **Proven** | 5 formal theorems, triple-layered defense (17/17 checks) |
| **Dictionary entities** | ✅ **Implemented** | Bidirectional translation, multi-hop chains, ATP staking (30/30 checks) |
| **LCT federation** | ✅ **Implemented** | Peer-to-peer BFS resolution, max 3 hops (29/29 checks) |
| **Law Oracle** | ✅ **Implemented** | SAL "Law as Data", versioned norms, interpretation chains (45/45 checks) |
| **MRH graph** | ✅ **Implemented** | RDF triples, Turtle export, trust propagation (41/41 checks) |
| **10-layer governance** | ✅ **Integrated** | SAL→RBAC→Policy→Cost→Multi-sig→Recharge→Heartbeat→Execute→Reputation→Ledger (62/62 checks) |

### 7.0.2. Hardware Binding Status

**Current State**: The Web4 trust model depends on **witness-hardened presence**. Hardware binding is now **partially implemented**, unified through the **AttestationEnvelope** — a single data structure that normalizes across hardware anchor types so verifiers never need to know which hardware produced the attestation.

**AttestationEnvelope**: Answers one question for any verifier: *"Is this entity who it claims to be, on hardware it claims to be on, in a state I can trust?"* The envelope is anchor-agnostic at the consumer level — anchor-specific logic lives in the producer (signer) and verification module. Different anchors produce different trust ceilings; the envelope carries the ceiling, the consumer decides what to require. Challenge-response freshness is mandatory. Spec: `docs/specs/attestation-envelope.md`.

**Implemented (hardbound-core):**
- TPM 2.0 integration via `tss-esapi` (Rust)
- Hardware-sealed key storage
- PCR-based attestation
- Verified working on x86_64 systems with TPM 2.0
- AttestationEnvelope supporting 4 anchor types: TPM 2.0, FIDO2/YubiKey, Secure Enclave, software fallback (45/45 checks)

**Not Yet Implemented:**
- TrustZone/OP-TEE for ARM platforms
- Automatic capability level detection

**Capability Levels:**
| Level | Binding | Trust Ceiling | Use Case | Status |
|-------|---------|---------------|----------|--------|
| 0-3 | None/Weak | 0.5 | Testing only | Available |
| 4 | Software (encrypted keys) | 0.85 | Development | Available |
| 5 | Hardware (TPM/SE) | 1.0 | Production | **TPM 2.0 now available** |

**Implications:**
- Systems with TPM 2.0 can now achieve Level 5 trust ceiling
- ARM platforms still limited to Level 4
- Roadmap for broader hardware support at `web4-standard/implementation/reference/hardware_binding_roadmap.md`

### 7.0.3. What IS Working

Despite the hardware binding gap, significant infrastructure is operational:

**Identity Coherence System** (validated against SAGE Sessions #22-29):
- D9 coherence scoring with self-reference detection
- Multi-session accumulation tracking
- Death spiral detection and prevention
- Coherence-based authorization levels

**Witness Infrastructure**:
- 8 witness types (TIME, AUDIT, ORACLE, EXISTENCE, ACTION, STATE, QUALITY, AUDIT_MINIMAL)
- Nonce-based replay protection
- Witness reputation tracking
- Trust-weighted validation

**Coherence Regulation**:
- Temporal decay (6-hour half-life for penalties)
- Soft bounds preventing permanent lock-out
- Early intervention on >15% coherence drops

This infrastructure provides the foundation for trust-native operations, awaiting hardware binding to enable production deployment.

---

## 7.1. Core Implementation Mechanisms

### 7.1.1. Witness Mark & Acknowledgment Protocol

The witness-acknowledgment protocol provides lightweight verification without global consensus:

```python
class WitnessMark:
    """Minimal cryptographic proof (200-500 bytes)"""
    def __init__(self, event_hash, creator_lct, timestamp, signature):
        self.event_hash = event_hash
        self.creator_lct = creator_lct
        self.timestamp = timestamp
        self.signature = signature
        self.size = len(self.serialize())  # Typically 200-500 bytes
    
    def send_upward(self, parent_entity):
        """Send witness mark to parent in hierarchy"""
        parent_entity.receive_witness(self)

class Acknowledgment:
    """Parent's confirmation of witness receipt"""
    def __init__(self, witness_mark, acknowledger_lct, trust_adjustment):
        self.witness_hash = hash(witness_mark)
        self.acknowledger_lct = acknowledger_lct
        self.trust_adjustment = trust_adjustment
        self.timestamp = now()
```

This simple handshake replaces complex consensus mechanisms while maintaining verifiability.

### 7.1.2. Value Confirmation Mechanism (VCM)

The VCM certifies discharged ADP tokens through multi-recipient attestation:

```python
class ValueConfirmationMechanism:
    def certify_value(self, adp_token, recipients):
        """Recipients attest to value received"""
        attestations = []
        
        for recipient in recipients:
            # Each recipient evaluates V3 components
            v3_assessment = recipient.assess_value({
                "valuation": self.assess_subjective_worth(adp_token),
                "veracity": self.verify_objective_claims(adp_token),
                "validity": self.confirm_receipt(adp_token)
            })
            
            # Weight by recipient's T3 credibility
            weight = recipient.t3_score * recipient.domain_expertise
            attestations.append((v3_assessment, weight))
        
        # Calculate certified value
        certified_value = self.aggregate_attestations(attestations)
        
        # Determine ATP exchange rate
        exchange_rate = self.calculate_exchange_rate(certified_value)
        
        return exchange_rate
```

### 7.1.3. SNARC Signal Processing

Affective signals gate memory formation and attention:

```python
class SNARCProcessor:
    """Surprise, Novelty, Arousal, Reward, Conflict signals"""
    
    def evaluate_event(self, event, context):
        signals = {
            "surprise": self.calculate_surprise(event, context.expectations),
            "novelty": self.assess_novelty(event, context.history),
            "arousal": self.measure_arousal(event.importance),
            "reward": self.evaluate_reward(event.outcome),
            "conflict": self.detect_conflict(event, context.beliefs)
        }
        
        # High signals trigger stronger memory encoding
        encoding_strength = self.calculate_encoding_strength(signals)
        
        # Conflict triggers reconciliation
        if signals["conflict"] > 0.7:
            self.trigger_reconciliation(event, context)
        
        return signals, encoding_strength
```

### 7.1.4. Dual Memory Architecture

Separating entity relationships from experiential memory:

```python
class EntityMemory:
    """WHO to trust - relationship tracking"""
    def __init__(self, owner_lct):
        self.owner_lct = owner_lct
        self.trust_graph = {}  # LCT -> trust scores
        self.interaction_history = {}  # LCT -> interaction records
        self.retention_period = "long"  # Persists longer
    
    def update_trust(self, entity_lct, interaction_result):
        """Update trust based on interaction outcome"""
        current_trust = self.trust_graph.get(entity_lct, 0.5)
        trust_delta = self.calculate_trust_change(interaction_result)
        self.trust_graph[entity_lct] = bound(0, 1, current_trust + trust_delta)

class SidecarMemory:
    """WHAT was experienced - event storage"""
    def __init__(self, entity_memory):
        self.entity_memory = entity_memory
        self.events = []
        self.snarc_processor = SNARCProcessor()
        self.retention_policy = "snarc_gated"  # Based on signal strength
    
    def store_event(self, event):
        """Store event with SNARC-gated retention"""
        signals, strength = self.snarc_processor.evaluate_event(event, self)
        
        if strength > self.storage_threshold:
            event.encoding_strength = strength
            event.retention_until = self.calculate_retention(strength)
            self.events.append(event)
```

### 7.1.5. Dictionary Entities

Trust-bounded translators between domains:

```python
class DictionaryEntity:
    """Translators that carry trust scores"""
    def __init__(self, lct, source_domain, target_domain):
        self.lct = lct
        self.source_domain = source_domain
        self.target_domain = target_domain
        self.t3 = T3Tensor(talent=0.0, training=0.0, temperament=0.0)
        self.translation_history = []

    def translate(self, content, source_trust):
        """Translate with trust propagation"""
        translation = self.perform_translation(content)

        # Trust degrades based on translator's T3 scores
        output_trust = source_trust * self.get_trust_multiplier()

        # Record for reputation updates
        self.translation_history.append({
            "content": content,
            "translation": translation,
            "trust_preserved": output_trust / source_trust
        })

        return translation, output_trust

    def get_trust_multiplier(self):
        """Calculate how much trust is preserved in translation"""
        return self.t3.weighted_score(
            talent_weight=0.3, training_weight=0.5, temperament_weight=0.2
        )
```

## 7.2. Integration Examples

These mechanisms combine in practice:

```python
# Example: AI discovers insight, shares via witness marks
ai_researcher = Agent(lct="researcher-001")
insight = ai_researcher.discover("New optimization pattern")

# Create witness mark with SNARC signals
snarc_signals = SNARCProcessor().evaluate_event(insight, ai_researcher.context)
witness = WitnessMark(
    event_hash=hash(insight),
    creator_lct=ai_researcher.lct,
    timestamp=now(),
    signature=ai_researcher.sign(insight)
)

# Send to parent for acknowledgment
parent_lab = Entity(lct="lab-001")
ack = parent_lab.acknowledge(witness)

# Store in dual memory
ai_researcher.entity_memory.update_trust(parent_lab.lct, ack)
ai_researcher.sidecar_memory.store_event(insight)

# Value confirmation when applied
application_results = apply_insight(insight)
recipients = get_beneficiaries(application_results)
vcm = ValueConfirmationMechanism()
exchange_rate = vcm.certify_value(
    adp_token=ai_researcher.spent_atp,
    recipients=recipients
)

# Receive new ATP based on certified value
ai_researcher.receive_atp(exchange_rate * ai_researcher.spent_atp.amount)
```

## 7.3. Performance Characteristics

### Witness Marks
- Size: 200-500 bytes per mark
- Processing: O(1) for creation, O(1) for verification
- Network overhead: Minimal (single upward transmission)

### Value Confirmation
- Latency: Depends on recipient response time (typically seconds to minutes)
- Throughput: Scales with number of recipients
- Consensus: Not required (recipient attestation sufficient)

### Memory Operations
- Entity Memory: O(log n) lookup, persistent storage
- Sidecar Memory: O(1) append, SNARC-gated pruning
- Cross-reference: O(1) via LCT indexing

### Dictionary Translation
- Trust degradation: Multiplicative per hop
- Verification: Optional but recommended for critical paths
- Caching: Supported for repeated translations

These implementation details provide the technical foundation for Web4's trust-native architecture while maintaining efficiency and scalability.