Lex (AetherOS)
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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 Lex is a specialized initiative within AetherOS, orchestrating a cohort of legal-analytic ARC agents to create a multi-agent adaptation loop that recursively refines the Legal Maneuverability Framework. It targets >90% predictive accuracy and >5% quarterly improvement in PM/SM Scores through continual learning.
Core Philosophy: The Nervous System of Legal Intelligence[edit]
Lex models law as a dynamic system with quantifiable energy exchanges, via PM Score, SM Score, and CVI. As the "nervous system," Lex coordinates agents to evolve equations and variables, adapting to legal shifts (e.g., new precedents) through meta-learning inspired by MAML. It grounds physics-inspired analogies in empirical performance, targeting >85% motion prediction accuracy (per Pre/Dicta benchmarks).
System Architecture v2.1[edit]
Lex extends AetherOS with a Meta-Adaptation Engine for recursive evolution.
The Intelligence: The Legal ARC Cohort[edit]
A unified cohort for distributed learning:
- Lord John Marbury (The Strategist): Proposes equation patches (e.g., non-linear O_s^1.2) via SAGA.
- The Quaesitor (The Auditor): Scans CVI for anomalies (e.g., stale win rates) and validates patches (>5% accuracy lift).
- The Praetor (The Deployer): Automates 80% of deployments (e.g., wiki template updates), ensuring rapid adaptation.
The Multi-Agent Adaptation Loop[edit]
1. Audit (Quaesitor): Identifies framework weaknesses (e.g., SM underpredicts in high-friction courts, <80% accuracy).
2. Hypothesize (Marbury): Generates SAGA patch (e.g., SUGGERO --model SM_Score --action ADD_VARIABLE --variable CrisisFactor --weight 0.1).
3. Validate & Deploy (Praetor): Tests on CVI hold-outs (500 cases); deploys if >2% F1-score lift, feeding back to Lexicon.
The Toolchain: Adaptive SDKs[edit]
- Converti (Lex Edition): Manages templates with Git-like merging to sync with AetherOS, ensuring adaptation continuity.
- Scriptor (Lex Edition): Generates Evolution Sagas (e.g., “v2.1: Added F_m to SM, +8% accuracy”), fed into Marbury for meta-learning.
The Meta-Adaptation Engine[edit]
Central ML orchestrator aggregates cohort outputs to refine LM (e.g., optimize weights, propose hybrid equations).
Project Governance[edit]
The Collegium oversees Lex, with Custos Structurae automating routine decisions and Custos Animae handling ethical vetoes, minimizing bottlenecks via ML-driven governance.
Weaknesses[edit]
- Analogy Overreach: Physics-inspired model risks misaligned adaptations in interpretive law. - Cohort Fragmentation: Agents may desync without robust APIs, leading to conflicting patches. - Empirical Speculation: Superiority claims require continuous validation against Westlaw AI. - Human Bottlenecks: Ethical vetoes slow recursion in urgent scenarios.
Brittle Data Modeling Areas[edit]
- SDK Forking Debt: Divergent codebases risk integration failures. - Cohort Variance: Low-sample domains (<500 cases) cause 20-30% patch variance. - Bias Propagation: Meta-Engine amplifies biases without fairness checks. - Governance Delays: Vetoes brittle to rapid legal shifts.
Validation and Performance[edit]
KPIs: >90% cohort agreement, >5% quarterly adaptation rate. Benchmarked against Thomson Reuters AI, achieving 88% accuracy on 500 motions. Targets 90%+ via ensemble methods and fairness audits (<5% disparity).