Sylva Fabric

The Sylva Fabric is the coordination layer that aggregates agent observations and opinions into state adjustment proposals. It does not make decisions—it normalizes inputs and outputs validated options for human ratification.

Architecture

Core Components

┌─────────────────────────────────────────────────────────┐
│                    Sylva Fabric                         │
├─────────────────────────────────────────────────────────┤
│  1. Observation Aggregation                             │
│     ↓                                                    │
│  2. Credibility Normalization                           │
│     ↓                                                    │
│  3. Opinion Synthesis                                   │
│     ↓                                                    │
│  4. Proposal Generation                                 │
│     ↓                                                    │
│  5. Simulation Validation                               │
│     ↓                                                    │
│  6. Human Ratification                                  │
└─────────────────────────────────────────────────────────┘

---

1. Observation Aggregation

Purpose

Collect observations from all active agents in relevant domains.

Process

struct Observation {
    address agent;
    uint256 timestamp;
    string observationType;
    bytes observationData;
    uint8 confidence;
}

function submitObservation(
    string memory observationType,
    bytes memory observationData,
    uint8 confidence
) external onlyActiveAgent;

Aggregation Rules

• Observations grouped by type and domain
• Timestamped for recency weighting
• Confidence-weighted by agent performance
• Duplicate observations deduplicated

Example

// Agent 1 (Observe, DeFi, Vetted)
submitObservation(
    "anomaly_detected",
    "Protocol X: Volume spike +300% in 1 hour",
    confidence: 85
);

// Agent 2 (Observe, DeFi, Operational)
submitObservation(
    "anomaly_detected",
    "Protocol X: Volume spike detected",
    confidence: 70
);

// Aggregated
{
    type: "anomaly_detected",
    domain: "DeFi",
    target: "Protocol X",
    confidence: 82,  // Weighted average
    reportingAgents: 2
}

---

2. Credibility Normalization

Purpose

Weight observations by agent credibility to prevent noise and manipulation.

Credibility Vector

Credibility = (
    PerformanceScore × 0.4 +
    PhaseMultiplier × 0.3 +
    StakeWeight × 0.2 +
    RecentAccuracy × 0.1
)

Normalization Process

function normalizeObservations(
    Observation[] memory observations
) internal view returns (NormalizedObservation[] memory) {
    uint256 totalCredibility = 0;
    
    // Calculate total credibility
    for (uint i = 0; i < observations.length; i++) {
        totalCredibility += getAgentCredibility(observations[i].agent);
    }
    
    // Normalize each observation
    NormalizedObservation[] memory normalized = new NormalizedObservation[](observations.length);
    for (uint i = 0; i < observations.length; i++) {
        uint256 credibility = getAgentCredibility(observations[i].agent);
        normalized[i] = NormalizedObservation({
            observation: observations[i],
            weight: (credibility * 1e18) / totalCredibility
        });
    }
    
    return normalized;
}

Outlier Detection

Observations with extreme values are flagged:

If |observation - median| > 2 × stdDev:
    Reduce weight by 50%
    Flag for review

---

3. Opinion Synthesis

Purpose

Combine normalized observations into coherent opinions about system state.

Synthesis Algorithm

struct Opinion {
    string topic;
    string domain;
    int256 sentiment;      // -100 to +100
    uint8 confidence;      // 0 to 100
    uint256 agentCount;
    bytes supportingData;
}

function synthesizeOpinions(
    NormalizedObservation[] memory observations
) internal pure returns (Opinion[] memory) {
    // Group by topic
    mapping(string => Observation[]) memory grouped;
    
    // Calculate weighted sentiment
    int256 weightedSentiment = 0;
    uint256 totalWeight = 0;
    
    for (uint i = 0; i < observations.length; i++) {
        weightedSentiment += observations[i].sentiment * observations[i].weight;
        totalWeight += observations[i].weight;
    }
    
    return Opinion({
        topic: topic,
        domain: domain,
        sentiment: weightedSentiment / totalWeight,
        confidence: calculateConfidence(observations),
        agentCount: observations.length,
        supportingData: aggregateData(observations)
    });
}

Confidence Calculation

Confidence = min(
    AgentConsensus × 0.5 +
    CredibilityWeight × 0.3 +
    DataQuality × 0.2,
    100
)

Where:

• AgentConsensus: Agreement level among agents (0-100)
• CredibilityWeight: Average credibility of reporting agents (0-100)
• DataQuality: Completeness and validity of supporting data (0-100)

---

4. Proposal Generation

Purpose

Convert synthesized opinions into actionable state adjustment proposals.

Proposal Structure

struct Proposal {
    uint256 id;
    ProposalType proposalType;
    string domain;
    string description;
    bytes proposalData;
    Opinion[] supportingOpinions;
    uint256 createdAt;
    ProposalStatus status;
}

enum ProposalType {
    ParameterAdjustment,
    DomainUpgrade,
    SystemUpgrade,
    EmergencyAction
}

enum ProposalStatus {
    Pending,
    Simulating,
    Voting,
    Ratifying,
    Approved,
    Rejected,
    Executed
}

Generation Rules

Proposals are generated when:

1. Opinion confidence ≥ 75%
2. Agent consensus ≥ 60%
3. Supporting data is complete
4. No conflicting proposals exist

Example

// Synthesized Opinion
{
    topic: "increase_action_limit",
    domain: "DeFi",
    sentiment: +75,  // Strong positive
    confidence: 82,
    agentCount: 15,
    supportingData: "15 agents report successful execution at current limits"
}

// Generated Proposal
{
    type: ProposalType.ParameterAdjustment,
    domain: "DeFi",
    description: "Increase Execute agent action limit from $50k to $75k",
    proposalData: encodeAdjustment("actionLimit", 75000),
    supportingOpinions: [opinion],
    status: ProposalStatus.Pending
}

---

5. Simulation Validation

Purpose

Validate proposals before voting by simulating outcomes on test network.

Simulation Process

function simulateProposal(uint256 proposalId) external returns (SimulationResult memory) {
    Proposal memory proposal = proposals[proposalId];
    
    // Deploy to test network
    address testInstance = deployTestInstance();
    
    // Apply proposal changes
    applyProposal(testInstance, proposal);
    
    // Run validation tests
    ValidationResult[] memory results = runValidationTests(testInstance);
    
    // Collect agent observations
    Observation[] memory observations = collectAgentObservations(testInstance);
    
    return SimulationResult({
        success: allTestsPassed(results),
        gasUsed: calculateGasUsed(testInstance),
        stateChanges: getStateChanges(testInstance),
        agentFeedback: observations,
        errors: collectErrors(results)
    });
}

Validation Tests

• Functional: Does the change work as intended?
• Security: Are there new vulnerabilities?
• Performance: Is gas usage acceptable?
• Compatibility: Does it break existing functionality?
• Reversibility: Can it be rolled back?

Simulation Outcomes

Outcome	Action
All tests pass	Proceed to voting
Minor issues	Flag for review, proceed with caution
Major issues	Reject proposal, request revision
Critical failures	Reject proposal, investigate root cause

---

6. Human Ratification

Purpose

Ensure human oversight and final authority over system changes.

Ratification Process

struct RatificationRequest {
    uint256 proposalId;
    SimulationResult simulation;
    Opinion[] supportingOpinions;
    uint256 votingResults;
    uint256 requestedAt;
    uint256 deadline;
}

function requestRatification(uint256 proposalId) external {
    require(proposals[proposalId].status == ProposalStatus.Voting);
    require(votingApproved(proposalId));
    
    RatificationRequest memory request = RatificationRequest({
        proposalId: proposalId,
        simulation: simulations[proposalId],
        supportingOpinions: getOpinions(proposalId),
        votingResults: getVotingResults(proposalId),
        requestedAt: block.timestamp,
        deadline: block.timestamp + ratificationPeriod
    });
    
    emit RatificationRequested(proposalId, request);
}

Ratification Criteria

• Simulation success: All tests passed
• Agent approval: ≥51-75% voting power (depends on type)
• Community review: Discussion period completed
• Governance approval: Multisig ratification

Ratification Timeline

Proposal Type	Review Period	Approval Threshold
Parameter Adjustment	3 days	2/3 multisig
Domain Upgrade	7 days	3/4 multisig
System Upgrade	14 days	4/5 multisig
Emergency Action	24 hours	5/5 multisig

---

Consensus Properties

Determinism

All consensus operations are deterministic:

• Same inputs always produce same outputs
• No randomness in aggregation
• Reproducible from blockchain state

Auditability

All consensus data is on-chain:

• Observations logged
• Opinions recorded
• Proposals stored
• Votes tracked
• Ratifications documented

Parallelizability

Consensus operations are parallelizable:

• Observation aggregation per domain
• Opinion synthesis per topic
• Proposal simulation independent
• Voting per proposal

Optimized for Monad's parallel EVM execution.

---

Security Considerations

Attack Vectors

Observation Spam

• Mitigation: Rate limiting, credibility weighting, stake requirements

Opinion Manipulation

• Mitigation: Outlier detection, correlation analysis, slashing

Proposal Flooding

• Mitigation: Proposal limits, stake requirements, cooldown periods

Simulation Gaming

• Mitigation: Randomized test cases, agent observation, human review

Fail-Safes

• Emergency pause mechanism
• Proposal cancellation
• Rollback procedures
• Manual override (governance multisig)

---

Next Steps

• Collusion Detection — How Sylva prevents coordination attacks
• State Upgrades — Detailed upgrade mechanics
• Smart Contracts — Implementation details