Genmount WorldModel-OS：可审计AI推理的世界模型操作系统架构

Genmount WorldModel-OS is an OS-level platform for world model-based AI systems, designed to enable auditable agent reasoning. It addresses the black-box problem of traditional LLMs by providing infrastructure with key components like gateway design, discrete anchor state space, append-only audit trails, and four-level rollback contracts, supporting transparency and accountability in critical decision scenarios.

Traditional large language model reasoning processes are often black boxes, making it hard to trace and verify intermediate steps. This opacity poses serious obstacles in high-risk fields like finance, medical care, and law, where AI agents are increasingly used for key decisions. Auditability has thus become a critical consideration in AI system design.

World model refers to a system's internal representation of environmental states. AI agents with world models can predict future states, simulate action consequences, and make informed decisions. Genmount WorldModel-OS takes world model as its core abstraction, providing a structured environment representation that follows design principles to ensure the agent's reasoning process is understandable and auditable by external systems.

Gateway: The only entry/exit point for agent-external interactions. It records all external inputs (queries, sensor data, API responses) and outputs (decisions, actions), providing a unified audit point and enabling security policies (input validation, access control). It also converts external data into the world model's internal semantic representation.

Discrete Anchor State Space: Maps complex continuous state spaces to discrete, named anchor states with clear semantics and boundaries. Agent reasoning is represented as transitions between these states (e.g., "market analysis" → "risk assessment" in finance), simplifying state tracking and audit. Designed by domain experts following field-driven principles.

Append-only Audit Trail: Immutable logs where records (input/output, state changes, reasoning steps, decision references) are never modified/deleted. Ensures audit evidence reliability, with structured storage for machine parsing and encrypted signatures for integrity/non-repudiation.

Four-level Rollback Contract: Provides granular rollback capabilities for real-time error correction: 1) Operation-level (undo last interaction), 2) State-level (return to previous anchor state), 3) Session-level (reset user session), 4) System-level (restore to known good config). Rollback actions are logged for full history.

Design Principles: 1) Safety first (all decisions prioritize security), 2) Least privilege (agents get minimal necessary permissions), 3) Transparent & auditable (key operations are recordable/verifiable).

Application Scenarios: Finance (compliance audit for auto trading), medical (transparency for clinical decision support), legal (ensuring AI decisions align with procedural justice). It also provides a reference for AI safety research.

Genmount's code release is pending §11 approval, reflecting the importance of compliance in high-risk AI deployment. The team maintains a governance repository (Genmount-WorldModel-OS-Governance) containing defensive preprints, threat models, and governance frameworks—adopting a "technology + governance" dual approach as a best practice for responsible AI development.

Genmount WorldModel-OS offers a valuable reference architecture for auditable AI systems. By combining gateway control, discrete state spaces, immutable audit trails, and granular rollback, it balances AI agent capabilities with auditability. As AI expands in critical domains, such architectures will become increasingly essential for building trustworthy AI systems.