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 (Design & Scaffolding Phase)}}
'''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 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]].


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''.
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.


== Technical Architecture ==
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''.
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.


=== The Hierarchical Reasoning Core ===
== Proposed Technical Architecture ==
The agent's "mind" is a dual-recurrent [[Hierarchical Reasoning Model (AetherOS)|Hierarchical Reasoning Model]] (HRM).
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.
*  '''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.
*  '''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.


=== The Animus (Lex): The Embodiment of Precedent ===
=== Hierarchical Reasoning Core (Design) ===
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 agent's "mind" is designed as a dual-recurrent [[Hierarchical Reasoning Model (AetherOS)|Hierarchical Reasoning Model]] (HRM).
*  '''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.
*  '''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.
*  '''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.
*  '''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.


=== The SAGA Learning Loop (Lex): The Self-Correcting Jurist ===
=== The Animus (Lex) (Concept) ===
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.
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.
*  '''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.
*  '''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.


# '''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''.
=== The SAGA Learning Loop (Mechanism) ===
# '''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 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 `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:
 
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>
#* <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.
# '''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 ==
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 - The Bar Exam (Planned):'''
    *  '''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."
    *  '''Exit Criteria:''' Achieve a high score on a text-based, multiple-choice legal reasoning benchmark (e.g., a modified version of the CaseHOLD dataset).
 
*  '''Phase II - The Clerkship (Planned):'''
    *  '''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.
    *  '''Exit Criteria:''' Meet the Key Performance Indicators outlined below.


== Training Curriculum ==
*  '''Phase III - The Strategist (Planned):'''
The agent is being developed through a multi-phase training curriculum designed to mirror the career of a human jurist.
    *  '''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.


*  '''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.
*  '''Project 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 Work:'''
    *  This project charter has been created and ratified by the [[Collegium (AetherOS)]].
    *  The foundational [[Corpus Vis Iuris (Lex)]] is in the beta phase, with initial data ingestion and structuring complete.
    *  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.


== Current Status and Performance Metrics ==
== Key Performance Indicators (KPIs) ==
*  '''Model Version:''' ARC (Lex) v0.8 Alpha
The success of Phase II will be measured against the following target metrics:
*  '''Primary Metric:''' Predictive accuracy of the [[Positional Maneuverability Score (Lex)|PM Score]] on a validation set of 1,000 historical motions for summary judgment.
*  '''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.
*  '''Current Accuracy:''' 76.4% (as of Q3 2025).
    *  '''Target:''' ''' >85%''' on a designated, static validation set of 1,000 cases.
*  '''Next Goal:''' Achieve >85% accuracy before proceeding to Phase III training.
*  '''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.
    *  '''Target:''' Demonstrate a consistent, non-zero positive refinement rate.


== See Also ==
== See Also ==

Revision as of 17:20, 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 the designated name for a specialist 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.

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.

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.

Proposed Technical Architecture

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.

Hierarchical Reasoning Core (Design)

The agent's "mind" is designed as a dual-recurrent Hierarchical Reasoning Model (HRM).

  • High-Level (Slow) Module: This layer will be responsible for strategic, macro-scale analysis. Its designed function is to ingest the complete PM and SM scores for a given case to form a holistic "gestalt" of the strategic landscape. Its output will be a high-level strategic plan.
  • 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.

The Animus (Lex) (Concept)

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.

  • 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.
  • Aetheric Sensation (Proposed): The resulting six-property 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.

The SAGA Learning Loop (Mechanism)

The agent's primary learning mechanism will be a domain-specific implementation of the 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:

  1. 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.
  2. 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.
  3. 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:
    • SUGGERO --model PM_Score --action DECREASE_WEIGHT --variable PrecedentPower.FactualSimilarityScore --value 0.05
  4. 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 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

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 - The Bar Exam (Planned):
   *   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."
   *   Exit Criteria: Achieve a high score on a text-based, multiple-choice legal reasoning benchmark (e.g., a modified version of the CaseHOLD dataset).
  • Phase II - The Clerkship (Planned):
   *   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.
   *   Exit Criteria: Meet the Key Performance Indicators outlined below.
  • Phase III - The Strategist (Planned):
   *   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

  • Project Phase: Alpha (Design & Scaffolding).
  • Completed Work:
   *   This project charter has been created and ratified by the Collegium (AetherOS).
   *   The foundational Corpus Vis Iuris (Lex) is in the beta phase, with initial data ingestion and structuring complete.
   *   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)

The success of Phase II will be measured against the following target metrics:

  • 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.
   *   Target:  >85% on a designated, static validation set of 1,000 cases.
  • 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.
   *   Target: Demonstrate a consistent, non-zero positive refinement rate.

See Also

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