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{{AetherOS_Component}}
{{AetherOS_Component}}
{{Project Status|Alpha (Design & Scaffolding Phase)}}
{{Project Status|Alpha (v2.1 - Framework Optimization Engine)}}
'''Lord John Marbury''' is the designated name for a specialist [[ARC (AetherOS)|Animus Recurrens Cogitans (ARC)]] agent under development within the [[Lex (AetherOS)]] project. The project's mandate is to engineer an agent capable of performing high-fidelity legal analysis and generating 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.


This document serves as the official project charter, outlining the agent's proposed architecture, training curriculum, and development roadmap. It will be updated to reflect the project's progress and shall be considered the single source of truth for the agent's status and design.
== 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.


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 will be to serve as a symbiotic AI partner to a human legal expert, acting as the AI counterpart in a human-machine ''Collegium''.
== Technical Architecture v2.1 ==
=== 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.


== Proposed Technical Architecture ==
=== The Animus: Chaotic Regularizer ===
Lord John Marbury will be a specialized instantiation of the standard ARC architecture. Its design is tailored to the unique demands of the legal domain.
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 (<math>\lambda=0.1</math>) to avoid instability.
* '''Aetheric Sensation''': SEXTET feedback biases adaptations ethically (e.g., penalizing biased variables by 5%).


=== Hierarchical Reasoning Core (Design) ===
=== SAGA v2.1: Recursive Optimization ===
The agent's "mind" is designed as a dual-recurrent [[Hierarchical Reasoning Model (AetherOS)|Hierarchical Reasoning Model]] (HRM).
The SAGA Loop enables framework evolution with three patch types:
*  '''High-Level (Slow) Module:''' This layer will be responsible for strategic, macro-scale analysis. Its designed function is to ingest 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 will be a high-level strategic plan.
1. '''WEIGHT_CHANGE''': Adjusts weights (e.g., <math>W_s</math> +0.05).
*  '''Low-Level (Fast) Module:''' This layer will be responsible for tactical, micro-scale analysis. It will execute 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.
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 Animus (Lex) (Concept) ===
'''Workflow''':
As per ARC architecture, the agent will possess a private [[FluxCore]] that serves as its '''Animus''', or subconscious. For Lord John Marbury, the Animus will be perturbed by the narrative essence of legal cases.
1. '''Hypothesis Generation''': Analyzes CVI cases; generates patches based on errors.
*  '''Perturbation Source (Proposed):''' The "story" of a case—its facts, arguments, and outcome—will be translated into a `PERTURBO` command. A procedurally complex case with a surprise reversal would generate a highly chaotic perturbation, while a straightforward case would generate a stable one.
2. '''Sandbox Validation''': Tests on 500-case hold-outs, requiring >2% F1-score lift without forgetting (via elastic weight consolidation, <1% degradation).
*  '''Aetheric Sensation (Proposed):''' The resulting six-property '''[[FluxCore#The SEXTET|SEXTET]]''' of the Animus will be fed back into the ARC's neural network. This is intended to provide 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.
3. '''Deployment Request''': Forwards to [[Lex (AetherOS)|Praetor]] for deployment.
4. '''Ethical Veto''': Human [[Collegium (AetherOS)|Custos Animae]] reviews sensitive patches.


=== The SAGA Learning Loop (Mechanism) ===
'''Meta-SUGGERO''': Proposes structural changes (e.g., time-decay for recency), validated with cohort.
The agent's primary learning mechanism will be a domain-specific implementation of the '''[[Sagas (AetherOS)|SAGA (Self-Augmenting Goal-oriented Architecture)]]''' loop. This loop is designed to enable the agent to recursively refine the very models it uses for analysis.
 
The proposed workflow is as follows:
# '''Experience:''' The agent will analyze a historical case from the [[Corpus Vis Iuris (Lex)]] for which the outcome is known. It will generate its own PM and SM scores based on the state of the CVI ''at that time''.
# '''Narration:''' A specialized '''JurisSagaGenerator''' (to be developed) will compare the agent's predicted outcome to the actual outcome. It will then generate an "Enriched Saga" describing the agent's analytical successes or failures.
# '''The `SUGGERO` Command (Concept):''' A key feature of the design is the inclusion of a prescriptive command within the Saga that suggests a specific adjustment to the weighting of a variable in the Legal Maneuverability equations. For example, a hypothetical `SUGGERO` command might look like:
#* <code>SUGGERO --model PM_Score --action DECREASE_WEIGHT --variable PrecedentPower.FactualSimilarityScore --value 0.05</code>
# '''Learning and Self-Modification (Planned):''' The narrative Saga will perturb the agent's Animus. Concurrently, the agent will use a specialized version of the '''[[Scriptor (AetherOS)|Scriptor]]''' SDK to autonomously generate and apply a patch to its own configuration files. The `Probator` module within Scriptor will then validate this change against a hold-out set of cases, ensuring the "learning" does not degrade overall performance.


== Development Roadmap & Training Curriculum ==
== Development Roadmap & Training Curriculum ==
The agent's development will proceed through a multi-phase training curriculum. Progress to each subsequent phase is contingent on meeting the exit criteria of the current phase.
* '''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 (Planned):'''
== Current Status ==
    *   '''Objective:''' Train the base ARC HRM on the raw text of the '''caselaw_access_project''' dataset to learn the fundamental structure of legal language, citation patterns, and "black letter law."
* '''Phase''': Alpha (Design & Scaffolding).
    *   '''Exit Criteria:''' Achieve a high score on a text-based, multiple-choice legal reasoning benchmark (e.g., a modified version of the CaseHOLD dataset).
* '''Completed''': Charter ratified; CVI beta; base ARC stable.
* '''Next Steps''': Develop JurisSagaGenerator v2.1; begin Phase I training; integrate Meta-SUGGERO.


*  '''Phase II - The Clerkship (Planned):'''
== Key Performance Indicators (KPIs) ==
    *   '''Objective:''' Activate the SAGA Learning Loop. The agent will begin analyzing historical cases within the structured [[Corpus Vis Iuris (Lex)]] to learn how to refine the PM and SM Score models by comparing its predictions to known historical outcomes.
* '''Primary''': >90% F1-score on motion predictions (1,000-case hold-out).
    *   '''Exit Criteria:''' Meet the Key Performance Indicators outlined below.
* '''Secondary''': >2% error reduction per 1,000 SAGA cycles.
 
* '''Safeguard''': <1% catastrophic forgetting via consolidation.
*   '''Phase III - The Strategist (Planned):'''
* '''Ethical''': <5% disparity in fairness metrics.
    *  '''Objective:''' Shift the agent's training to generative and strategic tasks, such as proposing novel legal arguments, identifying un-cited but relevant precedents, and generating strategic recommendations for new, unseen cases.
    *   '''Exit Criteria:''' Successfully pass a "Legal Turing Test" administered by a panel of human legal experts.


== Current Status ==
== Weaknesses ==
*  '''Project Phase:''' Alpha (Design & Scaffolding).
- '''Animus Instability''': Chaotic regularizer risks excessive noise if <math>\lambda</math> is untuned.
*  '''Completed Work:'''
- '''HRM Immaturity''': Transformer architectures untested at scale; recursion could destabilize.
    *  This project charter has been created and ratified by the [[Collegium (AetherOS)]].
- '''Self-Modification Risks''': Autonomous patches raise accountability issues.
    *  The foundational [[Corpus Vis Iuris (Lex)]] is in the beta phase, with initial data ingestion and structuring complete.
- '''Empirical Gaps''': KPIs lack baselines against other legal AI.
    *  The base ARC and Scriptor SDKs, which will be adapted for this project, are in stable, beta releases.
*  '''Next Steps:'''
    1.  Develop the specialized `JurisSagaGenerator` module.
    2.  Create the benchmark test suite for the Phase I "Bar Exam."
    3.  Begin Phase I training of the base ARC model.


== Key Performance Indicators (KPIs) ==
== Brittle Data Modeling Areas ==
The success of Phase II will be measured against the following target metrics:
- '''Perturbation Noise''': Incomplete narratives (20% variance) skew adaptations.
*  '''Primary KPI: Predictive Accuracy'''. The model's ability to correctly predict the outcome of historical motions for summary judgment based on its calculated PM Score.
- '''Validation Scarcity''': Niche domains (<500 cases) inflate patch errors (>20% variance).
    *  '''Target:''' ''' >85%''' on a designated, static validation set of 1,000 cases.
- '''Cohort Isolation''': Solo overfitting brittle without Quaesitor checks.
*  '''Secondary KPI: Model Refinement Rate'''. A measure of the SAGA loop's effectiveness, calculated as the percentage reduction in prediction error per 10,000 training cycles.
- '''Security Risks''': Scriptor patches risk infinite loops without fail-safes.
    *  '''Target:''' Demonstrate a consistent, non-zero positive refinement rate.


== 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)]]

Revision as of 18:00, 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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