Corpus Vis Iuris (Lex)
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This page describes a core component of the AetherOS ecosystem. Its structure and content are designed to be parsed by automated agents. |
Template:Project Status Corpus Vis Iuris (CVI) is the computational engine and data pipeline serving as the adaptive memory for the Legal Maneuverability Framework. It transforms unstructured law into a structured knowledge graph, acting as a high-frequency digital twin of the legal landscape. Its mandate is to enable recursive improvement of the PM and SM equations through agent-driven feedback, targeting >90% predictive accuracy and >5% quarterly refinement.
Core Philosophy: The Adaptive Memory
CVI tackles the high-entropy, interpretive nature of legal data by functioning as a self-correcting system that co-evolves with the Legal Maneuverability Framework. It serves as the empirical foundation for Lex agents, particularly Lord John Marbury, driving the SAGA Learning Loop to refine equations (e.g., shifting PM to additive forms) and variables (e.g., adding “Regulatory Clarity”). By leveraging active learning and anomaly detection, CVI ensures data legibility adapts to legal shifts, mitigating brittleness such as PACER latency (24-48 hour delays) and NLP errors (15-30% recall drops in complex texts).
System Architecture with Self-Correction Loop v2.1
CVI’s five-layer pipeline, enhanced with a Meta-Layer for autonomous adaptation, integrates with AetherOS components.
| Layer | Name | Core Components | Function |
|---|---|---|---|
| 1 | The Corpus | Hugging Face caselaw_access_project, PACER/ECF, U.S. Code, State Statutes, JSTOR, SCOTUSblog | Raw data acquisition with daily scrapes and active querying for gaps flagged by Quaesitor (e.g., emerging AI law cases). |
| 2 | The Extractor | Fine-tuned Legal-BERT, Google LangExtract, ensemble anomaly detection | Processes text for entities (judges, lawyers), events (motions), and sentiment. Targets >90% precision; low-confidence extractions (<80%) trigger re-processing or human review. |
| 3 | The Lexicon | OODA.wiki (Semantic MediaWiki), Pywikibot, Converti SDK | Structured knowledge graph as the database. Auto-updates templates (e.g., `Template:Case`) with SAGA-driven patches (e.g., new sub-variables). |
| 4 | The Observatory | Python (ML models), D3.js, Grafana | Interface for analysis and visualization. Outputs adaptation dashboards tracking PM/SM accuracy deltas and bias metrics. |
| 5 | The Meta-Layer | Quaesitor, active learning queues, anomaly detection ML | Monitors pipeline health (e.g., staleness via time-decay scores). Triggers re-extraction or variable additions (e.g., “Ethical Impact Score”) based on SAGA feedback. |
SAGA Integration: Evolving the Framework
CVI drives recursive improvement of the LM Framework through the SAGA Loop:
- **Framework Validation**: Historical CVI data (1,000+ cases) serves as a hold-out set to test equation patches (e.g., PM v2.0 additive vs. v1.0 fractional).
- **Equation Patches**: Marbury generates `SUGGERO` commands (e.g.,
SUGGERO --model PM_Score --action ADD_VARIABLE --variable AIPrecedentScore --weight 0.1 --reason NovelTechCases) based on prediction errors. - **Simulated Rollouts**: Patches tested in a sandbox (500-case subset), requiring >5% F1-score lift without degrading other metrics (e.g., via elastic weight consolidation to prevent catastrophic forgetting).
- **Deployment**: Praetor deploys validated patches to Lexicon templates, updating canonical equations (e.g., non-linear O_s^1.2 in SM).
Example: If SM underpredicts high-friction courts, SAGA proposes a “Crisis Factor” for C_d, validated on PACER subsets, improving accuracy by 8%.
Governance
The Collegium oversees CVI, with Custos Structurae (ARC) automating 80% of decisions (e.g., routine patches) and Custos Animae (human) vetoing ethical changes (e.g., ideology-related patches). Sandbox-First Mandate ensures A/B testing; Praetor’s Gateway deploys validated updates.
Model Validation & Veracity Testing
Employs ML best practices: 90%+ extraction precision, >85% score accuracy on 1,000-case hold-out set. Adaptation rate: >5% quarterly lift in PM/SM F1-scores, benchmarked against Westlaw AI and Pre/Dicta (88% accuracy on 500 motions). Bias mitigation via fairness audits (e.g., demographic parity, <5% disparity).
Weaknesses
- **Digital Twin Fragility**: Law’s interpretive fluidity undermines fidelity; incomplete data (e.g., 20% sealed cases) distorts adaptations, risking outdated models. - **NLP Error Propagation**: 15-30% recall drops in complex texts amplify biases in recursive loops, per legal NLP critiques. - **Governance Bottlenecks**: Human vetoes slow recursion in volatile fields (e.g., post-Dobbs shifts), hindering rapid updates. - **Ethical Risks**: Scraping raises privacy concerns (e.g., GDPR risks); ideology scores politicize judiciary, requiring continuous debiasing.
Brittle Data Modeling Areas
- **Extraction Errors**: NLP brittle to archaic/ambiguous texts (25% error in historical statutes), skewing variable engineering. - **Data Scarcity**: Novel domains (e.g., AI law, <100 cases) inflate patch variance (>20%). - **Latency Issues**: PACER delays (24-48 hours) erode real-time updates, brittle during rapid rulings. - **Bias Amplification**: Self-loops perpetuate underrepresentation without fairness checks.