Corpus Vis Iuris (Lex): Difference between revisions
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== SAGA Integration: Evolving the Framework == | == SAGA Integration: Evolving the Framework == | ||
CVI drives recursive improvement of the LM Framework through the SAGA Loop: | 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''': [[Lord John Marbury (AetherOS)|Marbury]] generates `SUGGERO` commands (e.g., <code>SUGGERO --model PM_Score --action ADD_VARIABLE --variable AIPrecedentScore --weight 0.1 --reason NovelTechCases</code>) 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''': [[Lex (AetherOS)|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%. | '''Example''': If SM underpredicts high-friction courts, SAGA proposes a “Crisis Factor” for C_d, validated on PACER subsets, improving accuracy by 8%. | ||
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== Weaknesses == | == 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Law’s interpretive fluidity undermines fidelity; incomplete data (e.g., 20% sealed cases) distorts adaptations, risking outdated models.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 == | == 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. | ||
== See Also == | == See Also == | ||