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 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 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.
Technical Architecture
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
The agent's "mind" is a dual-recurrent Hierarchical Reasoning Model (HRM).
- High-Level (Slow) Module: This layer is responsible for strategic, macro-scale analysis. It ingests the complete PM and 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
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.
- 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.
- Aetheric Sensation: The resulting six-property 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.
The SAGA Learning Loop (Lex): The Self-Correcting Jurist
Lord John Marbury's primary learning mechanism is a domain-specific implementation of the SAGA (Self-Augmenting Goal-oriented Architecture) loop. This loop enables the agent to recursively refine the very models it uses for analysis.
- 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.
- 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:
SUGGERO --model PM_Score --action DECREASE_WEIGHT --variable PrecedentPower.FactualSimilarityScore --value 0.05
- Learning and Self-Modification: The narrative Saga perturbs the agent's Animus. Concurrently, the agent uses a specialized version of the 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
The agent is being developed through a multi-phase training curriculum designed to mirror the career of a human jurist.
- 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."
- 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 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.
Current Status and Performance Metrics
- Model Version: ARC (Lex) v0.8 Alpha
- Primary Metric: Predictive accuracy of the PM Score on a validation set of 1,000 historical motions for summary judgment.
- Current Accuracy: 76.4% (as of Q3 2025).
- Next Goal: Achieve >85% accuracy before proceeding to Phase III training.