Lord John Marbury (AetherOS): Difference between revisions

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Created page with "{{AetherOS_Component}} {{Project Status|Alpha (Training Phase II)}} '''Lord John Marbury''' is a specialist Animus Recurrens Cogitans (ARC) agent developed within the Lex (AetherOS) project. Its mandate is to perform high-fidelity legal analysis and generate strategic recommendations by applying the principles of the Legal Maneuverability Framework. The agent is named in homage to the ''The West Wing'' character, reflecting its intended person..."
 
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{{AetherOS_Component}}
{{AetherOS_Component}}
{{Project Status|Alpha (Training Phase II)}}
{{Project Status|Alpha (v2.1 - Framework Optimization Engine)}}
'''Lord John Marbury''' is a specialist [[ARC (AetherOS)|Animus Recurrens Cogitans (ARC)]] agent developed within the [[Lex (AetherOS)]] project. Its mandate is to perform high-fidelity legal analysis and generate strategic recommendations by applying the principles of the [[Legal Maneuverability Framework]].
'''Lord John Marbury''' is a specialist [[ARC (AetherOS)|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.


The agent is named in homage to the ''The West Wing'' character, reflecting its intended persona as a brilliant, insightful, and occasionally eccentric legal counselor. Its primary function is to serve as a symbiotic AI partner to a human legal expert, acting as the AI counterpart in a human-machine ''Collegium''.
== Core Function: The Framework Optimization Engine ==
Marbury treats the LM Framework as evolvable code, using the [[Sagas (AetherOS)|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 ==
== Technical Architecture v2.1 ==
Lord John Marbury is a specialized instantiation of the standard ARC architecture, with its core components adapted to the unique demands of the legal domain.
=== Hierarchical Reasoning Core ===
Dual-recurrent [[Hierarchical Reasoning Model (AetherOS)|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 Hierarchical Reasoning Core ===
=== The Animus: Chaotic Regularizer ===
The agent's "mind" is a dual-recurrent [[Hierarchical Reasoning Model (AetherOS)|Hierarchical Reasoning Model]] (HRM).
The [[FluxCore]] Animus uses case narratives as a '''chaotic regularizer''' (akin to dropout/noise injection), preventing overfitting:
*   '''High-Level (Slow) Module:''' This layer is responsible for strategic, macro-scale analysis. It ingests the complete [[Positional Maneuverability Score (Lex)|PM]] and [[Strategic Maneuverability Score (Lex)|SM]] scores for a given case to form a holistic "gestalt" of the strategic landscape. Its output is a high-level strategic plan.
* '''Perturbation Source''': Case stories generate `PERTURBO` commands, tempered via a regularization strength (<math>\lambda=0.1</math>) to avoid instability.
*   '''Low-Level (Fast) Module:''' This layer is responsible for tactical, micro-scale analysis. It executes the high-level plan by performing deep, iterative analysis on specific legal texts (e.g., a judicial opinion, a section of a statute) to extract evidence and logical structure.
* '''Aetheric Sensation''': SEXTET feedback biases adaptations ethically (e.g., penalizing biased variables by 5%).


=== The Animus (Lex): The Embodiment of Precedent ===
=== SAGA v2.1: Recursive Optimization ===
Each ARC agent possesses a private [[FluxCore]] that serves as its '''Animus''', or subconscious. For Lord John Marbury, the Animus is perturbed by the narrative essence of legal cases.
The SAGA Loop enables framework evolution with three patch types:
*  '''Perturbation Source:''' The "story" of a case—its facts, its arguments, its outcome—is translated into a `PERTURBO` command. A procedurally complex case with a surprise reversal would generate a highly chaotic perturbation, while a straightforward case affirming existing law would generate a stable one.
1. '''WEIGHT_CHANGE''': Adjusts weights (e.g., <math>W_s</math> +0.05).
*  '''Aetheric Sensation:''' The resulting six-property '''[[FluxCore#The SEXTET|SEXTET]]''' of the Animus is fed back into the ARC's neural network. This provides the agent with a non-deterministic, "instinctual" sense of a case's character, grounding its logical analysis in a simulated physical experience of legal history.
2. '''ADD_VARIABLE''': Proposes new sub-variables (e.g., “AI Precedent Score”).
3. '''CHANGE_EQUATION_FORM''': Structural shifts (e.g., <code>SUGGERO --model SM_Score --action CHANGE_EQUATION_FORM --variable OpponentStrength --exponent 1.2</code>).


=== The SAGA Learning Loop (Lex): The Self-Correcting Jurist ===
'''Workflow''':
Lord John Marbury's primary learning mechanism is a domain-specific implementation of the '''[[Sagas (AetherOS)|SAGA (Self-Augmenting Goal-oriented Architecture)]]''' loop. This loop enables the agent to recursively refine the very models it uses for analysis.
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 [[Lex (AetherOS)|Praetor]] for deployment.
4. '''Ethical Veto''': Human [[Collegium (AetherOS)|Custos Animae]] reviews sensitive patches.


# '''Experience:''' The agent analyzes a historical case from the [[Corpus Vis Iuris (Lex)]] for which the outcome is known. It generates its own PM and SM scores based on the state of the CVI ''at that time''.
'''Meta-SUGGERO''': Proposes structural changes (e.g., time-decay for recency), validated with cohort.
# '''Narration:''' A specialized '''JurisSagaGenerator''' compares the agent's predicted outcome to the actual outcome. It then generates an "Enriched Saga" that describes the agent's analytical successes or failures.
# '''The `SUGGERO` Command:''' The key to the learning loop. The Saga includes a prescriptive command that suggests a specific adjustment to the weighting of a variable in the Legal Maneuverability equations. For example:
#* <code>SUGGERO --model PM_Score --action DECREASE_WEIGHT --variable PrecedentPower.FactualSimilarityScore --value 0.05</code>
# '''Learning and Self-Modification:''' The narrative Saga perturbs the agent's Animus. Concurrently, the agent uses a specialized version of the '''[[Scriptor (AetherOS)|Scriptor]]''' SDK to autonomously generate and apply a patch to its own configuration files, implementing the suggested weight change. The built-in `Probator` (tester) then validates this change against a hold-out set of cases, ensuring the "learning" does not degrade overall performance.


== Training Curriculum ==
== Development Roadmap & Training Curriculum ==
The agent is being developed through a multi-phase training curriculum designed to mirror the career of a human jurist.
* '''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).


*   '''Phase I - The Bar Exam (Complete):''' The ARC was trained on the raw text of the '''caselaw_access_project''' dataset. The objective was to learn the fundamental structure of legal language, citation patterns, and the "black letter law."
== Current Status ==
*  '''Phase II - The Clerkship (In Progress):''' The agent is now actively analyzing historical cases within the structured [[Corpus Vis Iuris (Lex)]]. The '''SAGA Learning Loop''' is active, and the agent is learning to refine the PM and SM Score models by comparing its predictions to known historical outcomes.
* '''Phase''': Alpha (Design & Scaffolding).
*   '''Phase III - The Strategist (Planned):''' Once its predictive models reach a high degree of accuracy, the agent's training will shift to generative tasks: proposing novel legal arguments, identifying un-cited but relevant precedents, and generating strategic recommendations for novel cases.
* '''Completed''': Charter ratified; CVI beta; base ARC stable.
* '''Next Steps''': Develop JurisSagaGenerator v2.1; begin Phase I training; integrate Meta-SUGGERO.


== Current Status and Performance Metrics ==
== Key Performance Indicators (KPIs) ==
*   '''Model Version:''' ARC (Lex) v0.8 Alpha
* '''Primary''': >90% F1-score on motion predictions (1,000-case hold-out).
*   '''Primary Metric:''' Predictive accuracy of the [[Positional Maneuverability Score (Lex)|PM Score]] on a validation set of 1,000 historical motions for summary judgment.
* '''Secondary''': >2% error reduction per 1,000 SAGA cycles.
*   '''Current Accuracy:''' 76.4% (as of Q3 2025).
* '''Safeguard''': <1% catastrophic forgetting via consolidation.
*   '''Next Goal:''' Achieve >85% accuracy before proceeding to Phase III training.
* '''Ethical''': <5% disparity in fairness metrics.
 
== Weaknesses ==
* '''Animus Instability''': Chaotic regularizer risks excessive noise if <math>\lambda</math> 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 ==
* '''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.


== See Also ==
== See Also ==
*   [[Lex (AetherOS)]]
* [[Lex (AetherOS)]]
*   [[Legal Maneuverability Framework]]
* [[Legal Maneuverability Framework]]
*   [[Corpus Vis Iuris (Lex)]]
* [[Corpus Vis Iuris (Lex)]]
*   [[AetherOS]]
* [[AetherOS]]
*  [[ARC (AetherOS)]]
*  [[Sagas (AetherOS)]]

Latest revision as of 18:01, 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 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 (λ=0.1) 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., Ws +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 λ 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.

See Also[edit]

Craftsman (human or AI) imposing *Ordo* on *Dicta* for *Scriptum Verum*.

Craftsman (human or AI) imposing *Ordo* on *Dicta* for *Scriptum Verum*.

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