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'''Corpus Vis Iuris''' (CVI), Latin for "Body of Legal Force," is the data ingestion, processing, and validation protocol that provides the quantified, empirical foundation for the [[Legal Maneuverability Framework]]. Its function is to systematically scrape, parse, and structure the vast, unstructured data of the legal world into the specific, machine-readable variables required to calculate the [[Positional Maneuverability Score (Lex)|PM]] and [[Strategic Maneuverability Score (Lex)|SM]] scores.}}
{{AetherOS_Component}}
{{Project Status|Beta (v2.1 - Self-Correcting Pipeline)}}
'''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 [[Positional Maneuverability Score (Lex)|PM]] and [[Strategic Maneuverability Score (Lex)|SM]] equations through agent-driven feedback, targeting >90% predictive accuracy and >5% quarterly refinement.


== Core Principles ==
== Core Philosophy: The Adaptive Memory ==
The CVI protocol is governed by a set of core principles to ensure the resulting data is robust, reliable, and suitable for rigorous machine learning applications.
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 (AetherOS)|Lex]] agents, particularly [[Lord John Marbury (AetherOS)|Lord John Marbury]], driving the [[Sagas (AetherOS)|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).
* '''Verifiability:''' All data must originate from publicly available or credential-accessible primary sources (e.g., court dockets, statutes).
* '''High-Frequency:''' The system must be capable of updating its datasets in near real-time to reflect new filings, rulings, and precedents.
* '''Granularity:''' Data must be captured at the most granular level possible (e.g., individual docket entries, specific case citations) before being aggregated into higher-level metrics.
* '''Structural Linkage:''' The protocol must build a relational graph between entities (judges, lawyers, litigants, cases, statutes) to enable complex network analysis.


== Data Sourcing and Variable Engineering Pipeline ==
== System Architecture with Self-Correction Loop v2.1 ==
The CVI pipeline is a multi-stage process that transforms raw legal information into the engineered variables used in the scoring equations.
CVI’s five-layer pipeline, enhanced with a Meta-Layer for autonomous adaptation, integrates with AetherOS components.


=== For the [[Positional Maneuverability Score (Lex)|Positional Maneuverability Score]] ===
{| class="wikitable" style="width:100%;"
{| class="wikitable"
|+ PM Score Data Pipeline
! Variable
! Primary Data Sources
! Parsing & Engineering Methodology
|-
|-
| '''Statutory Support''' (<math>S_s</math>)
! Layer !! Name !! Core Components !! Function
| U.S. Code, State legislative sites, Cornell LII, ProQuest Legislative Insight, Congress.gov
| NLP-based semantic similarity analysis between legal briefs and statutory text. Keyword extraction and regex-based searches for exception clauses. Analysis of legislative history documents for intent.
|-
|-
| '''Precedent Power''' (<math>P_p</math>)
| 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 [[Lex (AetherOS)|Quaesitor]] (e.g., emerging AI law cases).
| PACER, CourtListener, Caselaw Access Project, Google Scholar
| Construction of a citation graph to calculate '''Shepardization Scores'''. NLP analysis of citing cases to classify treatment (positive/negative). Vector embedding of factual summaries to calculate '''Factual Similarity Scores'''. Extraction of court metadata to determine '''Binding Authority'''.
|-
|-
| '''Legal Complexity''' (<math>L_c</math>)
| 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.
| Law review databases (JSTOR, HeinOnline), SCOTUSblog, case briefs (PACER)
| NLP models trained to search for key phrases like "case of first impression" or "circuit split." Topic modeling to identify novel legal concepts.
|-
|-
| '''Jurisdictional Friction''' (<math>J_f</math>)
| 3 || '''The Lexicon''' || OODA.wiki (Semantic MediaWiki), Pywikibot, [[Converti (AetherOS)|Converti]] SDK || Structured knowledge graph as the database. Auto-updates templates (e.g., `{{Template:Case}}`) with SAGA-driven patches (e.g., new sub-variables).
| PACER, CourtListener, academic judicial databases (e.g., Judicial Common Space)
| Large-scale data analysis to track individual cases from trial court through appeal to calculate judge-specific '''Reversal Rates'''. Linking judges to established '''Ideology Scores'''.
|}
 
=== For the [[Strategic Maneuverability Score (Lex)|Strategic Maneuverability Score]] ===
{| class="wikitable"
|+ SM Score Data Pipeline
! Variable
! Primary Data Sources
! Parsing & Engineering Methodology
|-
| '''Litigant Resources''' (<math>L_r</math>)
| SEC EDGAR, business intelligence APIs, public property records, Pacer/CourtListener
| Entity resolution to link litigant names to corporate or individual data. Scraping of dockets to count '''Legal Team Size'''. Normalization of financial data across the entire case corpus to generate a '''Financial Power''' index.
|-
|-
| '''Counsel Skill''' (<math>S_c</math>)
| 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.
| State Bar association websites, law firm websites, legal ranking publications (Am Law, Vault)
| Scraping attorney profiles for experience data. Mapping firms to '''Firm Tier Scores'''. Building a secondary database linking attorneys to judges and motion outcomes to calculate a '''Contextual Win Rate'''.
|-
|-
| '''Procedural Drag''' (<math>C_d</math>)
| 5 || '''The Meta-Layer''' || [[Lex (AetherOS)|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.
| Pacer, U.S. Courts statistics
| Time-series analysis of docket entries to calculate judge-specific '''Median Ruling Times'''. Aggregation of case filing data to determine court/judge '''Caseload'''.
|}
|}
== 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''': [[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%.
== Governance ==
The [[Collegium (AetherOS)|Collegium]] oversees CVI, with [[Collegium (AetherOS)|Custos Structurae]] (ARC) automating 80% of decisions (e.g., routine patches) and [[Collegium (AetherOS)|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 ==
== Model Validation & Veracity Testing ==
The CVI protocol includes a mandatory validation framework to test the virtue and veracity of both the variables themselves and the models they feed.
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.


# '''Training/Validation Data Split:''' Historical case data is partitioned into an 80% training set and a 20% validation set to prevent overfitting and test the model's ability to generalize to unseen data.
== Brittle Data Modeling Areas ==
# '''Feature Importance Analysis:''' Following model training, statistical analysis (e.g., SHAP values, permutation importance) is used to rank the predictive power of each engineered variable. This identifies which factors are most influential in determining legal outcomes.
- '''Extraction Errors''': NLP brittle to archaic/ambiguous texts (25% error in historical statutes), skewing variable engineering.
# '''Ablation Studies:''' The models are systematically re-trained with specific variables or sub-variables removed. The resulting degradation (or lack thereof) in predictive accuracy is measured to determine the necessity and virtue of each component in the framework. A variable whose removal significantly harms performance is considered critical and virtuous.
- '''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 ==
* [[Lex (AetherOS)]]
* [[Legal Maneuverability Framework]]
* [[Legal Maneuverability Framework]]
* [[Positional Maneuverability Score (Lex)]]
* [[Lord John Marbury (AetherOS)]]
* [[Strategic Maneuverability Score (Lex)]]
* [[AetherOS]]

Latest revision as of 17:58, 29 August 2025

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:

  1. 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).
  2. 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.
  3. 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).
  4. 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.

See Also

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