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fxyz Network

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Network Validation

Validation is continuous and distributed.

Stages
04
Status
Continuous
01

Agent Validation

Fixies validate, infer, and update the graph

02

Consensus Mechanisms

Multiple validators must agree before information is accepted

03

Real-time Correction

Errors will be detected and corrected as they occur

The Validation Pipeline

  1. 01Ingestion

    New data enters the network from various sources and agents

  2. 02Analysis

    Fixies analyze data for consistency, accuracy, and completeness

  3. 03Consensus

    Multiple validators vote on data quality and accuracy

  4. 04Integration

    Validated data is integrated into the knowledge graph

Fixies: Validation Agents

Fixies are autonomous agents embedded in the system. They validate, infer, and update the graph directly. A Fixie might infer a new relationship and add it, or observe data that contradicts existing knowledge and flag it for review. They also gather feedback and incorporate learning.

Fixies operate on Letta server with persistent memory and tool access.

  • ·Data collectors, validators, analyzers, and maintainers work together
  • ·Agents can detect anomalies, infer new relationships, adapt from feedback, and maintain data integrity

Consensus Mechanisms

Multi-Agent Validation

Multiple Fixie agents independently validate each piece of data before it's accepted into the knowledge graph.

  • ·Independent verification
  • ·Majority consensus required
  • ·Conflict resolution protocols

Expert Review

Human experts can be brought in for complex validations, especially for domain-specific knowledge. Reputation scores and token-based incentives (Florin) apply.

  • ·Domain expert validation
  • ·Community peer review
  • ·Reputation and trust score weighting
  • ·Token incentives (pending emission)

Quality Assurance

Continuous Monitoring

The network continuously monitors for changes, inconsistencies, and new information that might affect existing knowledge.

  • ·Real-time anomaly detection
  • ·Consistency checking
  • ·Automated error flagging

Trust Scoring

Every piece of data receives a trust score based on its source, validation history, and consensus level among validators.

  • ·High Trust (90-100%)
  • ·Medium Trust (70-89%)
  • ·Low Trust (50-69%)
  • ·Unverified (0-49%)

Self-Healing Network (Design Goal)

The network is designed to detect and repair inconsistencies over time, improving data integrity as validation matures.

Automatic Correction (Planned)

When conflicts are detected, the system is designed to apply corrections based on consensus and trust scores.

Adaptive Learning (Planned)

Validation algorithms are being designed to improve over time by learning from past validation decisions and outcomes.