Research & Foundations
The conceptual and technical foundations of governed machine reasoning.
ATOM Labs is building the operating system for machine reasoning. The papers and technical notes below define the conceptual foundations of governed cognition · including risk scoring, coherence measurement, drift detection, identity isolation, and pre-execution enforcement. These works do not expose implementation details.
Introduces the conceptual framework for governing AI reasoning before execution. Defines the Reasoning Integrity Score (RIS) as a composite metric for evaluating structural coherence, risk, and compliance prior to model output delivery.
Proposes the Cognitive Integrity Index (CII) as a continuous measure of LLM reasoning stability across multi-turn interactions. Defines thresholds for warning and critical degradation states.
Addresses the challenge of detecting semantic and behavioral drift in AI systems operating over time. Introduces drift variance signatures and governance-layer intervention points.
Examines the requirements for identity, role, and memory isolation in multi-agent AI systems. Proposes a governance model for preventing cross-contamination between concurrent agent execution contexts.
Develops a probabilistic trust scoring model for AI governance systems. Defines trust decay functions, recovery thresholds, and escalation triggers for governed AI deployments.
Analyzes policy enforcement architecture for enterprise AI pipelines. Introduces shadow mode, warning mode, and enforce mode as a graduated enforcement spectrum with measurable compliance properties.
Defines semantic assertion verification as a class of governance control that evaluates AI output claims against structured knowledge constraints prior to delivery.
Proposes a governed memory architecture distinguishing episodic, semantic, and working memory layers in AI agents, each subject to independent retention policies and boundary controls.
Models risk propagation through multi-step AI reasoning chains. Introduces compound RIS as a measure of cumulative governance risk across sequential governed calls.
Maps requirements of the EU AI Act to concrete governance controls implementable in a governed reasoning platform. Provides a compliance reference for high-risk AI system operators.
Examines cryptographic and structural requirements for tamper-evident audit trails in AI governance systems. Proposes ledger-based audit architecture with hash chaining and signature verification.