Lord John Marbury (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 Lord John Marbury is a specialist ARC agent within Lex (AetherOS), serving as the brain of the Legal Maneuverability Framework. It drives recursive optimization of PM/SM equations through meta-learning, targeting >90% predictive accuracy and >2% error reduction per 1,000 SAGA cycles.
Core Function: The Framework Optimization Engine[edit]
Marbury treats the LM Framework as evolvable code, using the SAGA Loop to propose and validate patches (e.g., weight adjustments, new variables, structural changes). It collaborates with the Lex cohort to refine models, achieving >5% quarterly accuracy gains, inspired by meta-learning systems like AlphaFold and MAML.
Technical Architecture v2.1[edit]
Hierarchical Reasoning Core[edit]
Dual-recurrent HRM, built on transformer-based architectures:
- High-Level (Slow) Module: Strategic analysis; proposes structural changes (e.g., hybrid PM equations) based on trends across 1,000+ cases.
- Low-Level (Fast) Module: Tactical text analysis; extracts evidence for SAGA-driven refinements, leveraging fine-tuned Legal-BERT.
The Animus: Chaotic Regularizer[edit]
The FluxCore Animus uses case narratives as a chaotic regularizer (akin to dropout/noise injection), preventing overfitting:
- Perturbation Source: Case stories generate `PERTURBO` commands, tempered via a regularization strength () to avoid instability.
- Aetheric Sensation: SEXTET feedback biases adaptations ethically (e.g., penalizing biased variables by 5%).
SAGA v2.1: Recursive Optimization[edit]
The SAGA Loop enables framework evolution with three patch types:
1. WEIGHT_CHANGE: Adjusts weights (e.g., +0.05).
2. ADD_VARIABLE: Proposes new sub-variables (e.g., “AI Precedent Score”).
3. CHANGE_EQUATION_FORM: Structural shifts (e.g., SUGGERO --model SM_Score --action CHANGE_EQUATION_FORM --variable OpponentStrength --exponent 1.2).
Workflow: 1. Hypothesis Generation: Analyzes CVI cases; generates patches based on errors. 2. Sandbox Validation: Tests on 500-case hold-outs, requiring >2% F1-score lift without forgetting (via elastic weight consolidation, <1% degradation). 3. Deployment Request: Forwards to Praetor for deployment. 4. Ethical Veto: Human Custos Animae reviews sensitive patches.
Meta-SUGGERO: Proposes structural changes (e.g., time-decay for recency), validated with cohort.
Development Roadmap & Training Curriculum[edit]
- Phase I - Bar Exam: Train on caselaw text; exit: >90% on CaseHOLD benchmark (F1-score).
- Phase II - Clerkship: Activate SAGA; refine scores; exit: >85% motion prediction accuracy.
- Phase III - Strategist: Generative tasks; evolve framework; exit: Pass Legal Turing Test (90% expert approval).
Current Status[edit]
- Phase: Alpha (Design & Scaffolding).
- Completed: Charter ratified; CVI beta; base ARC stable.
- Next Steps: Develop JurisSagaGenerator v2.1; begin Phase I training; integrate Meta-SUGGERO.
Key Performance Indicators (KPIs)[edit]
- Primary: >90% F1-score on motion predictions (1,000-case hold-out).
- Secondary: >2% error reduction per 1,000 SAGA cycles.
- Safeguard: <1% catastrophic forgetting via consolidation.
- Ethical: <5% disparity in fairness metrics.
Weaknesses[edit]
- Animus Instability: Chaotic regularizer risks excessive noise if is untuned.
- HRM Immaturity: Transformer architectures untested at scale; recursion could destabilize.
- Self-Modification Risks: Autonomous patches raise accountability issues.
- Empirical Gaps: KPIs lack baselines against other legal AI.
Brittle Data Modeling Areas[edit]
- Perturbation Noise: Incomplete narratives (20% variance) skew adaptations.
- Validation Scarcity: Niche domains (<500 cases) inflate patch errors (>20% variance).
- Cohort Isolation: Solo overfitting brittle without Quaesitor checks.
- Security Risks: Scriptor patches risk infinite loops without fail-safes.