Building Information Digital Twins for Adaptive Artificial Intelligence
Our core offering is the design and implementation of custom Information Digital Twins (IDTs) that enable AI systems to measure and optimize their own information throughput with the environment in real-time.
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Each IDT is tailored to a specific domain and operational requirements, incorporating our expertise in state-action space modeling and information-optimized discretization strategies. We guide the entire implementation process—from initial system assessment through architecture design, integration planning, and performance verification.
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From Theory to Application
Entanglement Learning (EL) is a pioneering framework aimed at redefining autonomy in AI through our Information Digital Twin (IDT). While EL is not yet a commercial product, it offers a robust set of foundational methodologies, algorithms, and architectural blueprints ready for enabling IDTs' implementation across domains such as computer vision, robotics, and control systems.
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We are actively seeking partners to co-develop and mature EL into operational systems—accelerating its path from conceptual framework to deployable, product-ready solutions (IDT-enabled systems). Our focus remains on advancing the core IDT architecture—not on developing domain-specific technologies, infrastructure, or commercialization.
We leave those layers to our partners, who bring the expertise to apply EL in real-world settings. These collaborations will help validate and extend the framework across diverse applications, while preserving its foundational goal: enable real-time adaptive behavior by maximizing information throughput between agents and their environments.
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We also seek research partners exploring EL’s theoretical foundations, including its connections to dynamical systems, information theory, and complex physical processes.
Partnership Opportunities
We are currently focused on advancing Entanglement Learning (EL) through a targeted set of five active use cases spanning computer vision, control systems, robotics, and decision-making. Our priority is to collaborate with partners who can contribute:
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Domain-specific data
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Operational infrastructure
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Applied expertise
These collaborations will help implement and validate EL in real-world systems in the form of IDTs, with a shared goal of demonstrating EL's capacity for autonomous adaptation and information alignment under dynamic conditions.
Modes of Engagement
We offer practical, applied collaboration models that focus on implementation and shared outcomes:
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Use Case Partnerships
Apply EL-i.e., develop IDTs, in one of your active domains using your data and infrastructure, supported by our methods and architecture. -
Joint System Development
Integrate the Information Digital Twin (IDT) into existing AI agents to enable adaptive performance monitoring and self-alignment. -
Validation Pilots
Test IDT-enabled agents in your environment, with co-designed evaluation metrics and technical support.
What we Offer
We provide specialized expertise in the two most critical aspects of implementing Entanglement Learning (EL) as domain-specific Information digital Twins (IDTs):
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Domain-specific systems-environment interaction modeling,
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Information-optimized discretization strategies, and
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Systems-environment information throughput optimization methods.
We enable our partners to define the state and action spaces relevant to their agents/systems which to be enabled by IDTs, identify which variables to monitor, and model the complete interaction cycle that captures the most informative relationships between agent and environment. This ensures that the IDT implementations reflect real system dynamics and operational constraints.
Our most distinctive capability lies in crafting custom discretization schemes that transform continuous variables into information-rich probability distributions. These methods are optimized for sensitivity to misalignment while maintaining computational efficiency, allowing entanglement metrics to detect degradation early—well before visible performance loss.
We also support the design of hierarchical, multi-modal IDT architectures, where multiple IDTs monitor different subsystems and feed into a higher-level coordinator. This structure enables localized adaptation with system-wide coherence—making EL scalable across complex, multi-modal, real-world applications.
Additional EL Application Domains
Healthcare Monitoring Systems
Current State: Healthcare AI typically employs rigid thresholds or population-based models that struggle to account for individual patient variability and gradual physiological changes.
Key Challenges: Patient-specific baselines that evolve over time; critical need for high precision with minimal false alarms; severe consequences for undetected distribution shifts.
IDT Implementation: A healthcare IDT would establish patient-specific information baselines and continuously optimize the mutual predictability between physiological signals and diagnostic assessments, enabling systems to autonomously adapt to individual baseline changes while maintaining clinical reliability across diverse patient populations.
Financial Trading Systems
Current State: Algorithmic trading systems employ predefined strategies optimized for specific market regimes, requiring human intervention to detect and adapt to fundamental market dynamics shifts.
Key Challenges: Unpredictable regime changes without clear boundaries; adversarial market behaviors; high-dimensional correlation structures that evolve rapidly.
IDT Implementation: A financial IDT would monitor information throughput between market signals and trading outcomes, detecting subtle changes in information relationships that precede major strategy failures and adaptively adjusting model parameters to maintain performance through volatile market transitions without manual reconfiguration.
Autonomous Supply Chains
Current State: Current supply chain optimization employs static demand forecasting and inventory models that require manual reconfiguration when faced with significant disruptions or pattern shifts.
Key Challenges: Complex interdependencies between manufacturing, logistics, and consumer demand; seasonal and trend-based distribution shifts; high-dimensional optimization constraints.
IDT Implementation: A supply chain IDT would measure information throughput across multi-echelon inventory systems, detecting misalignments between forecasting models and emerging demand patterns to guide targeted parameter updates that maintain operational efficiency during transitions without requiring complete model rebuilding.
Smart Grid Management
Current State: Power management systems rely on historical pattern recognition and static optimization models that struggle to maintain stability under increasing renewable energy variability.
Key Challenges: Non-stationary load and generation patterns; cascading effects across interconnected systems; critical need for continuous reliability despite infrastructure changes.
IDT Implementation: A grid-focused IDT would maintain an information-theoretic model of energy flow relationships, continuously measuring mutual predictability across the network to detect emerging instabilities before traditional indicators, enabling preemptive rebalancing through targeted control parameter adjustments that maintain system-wide coherence.
Physical Systems Analysis and Prediction
Current State: Complex systems like the double pendulum are traditionally modeled with differential equations that become unstable in chaotic phases, requiring expert knowledge and high-cost simulations to analyze their behavior.
Key Challenges: Unpredictability during chaotic transitions; difficulty maintaining control across dynamic regimes; inability to detect structure in apparent randomness; absence of universal metrics for model-system alignment.
IDT Implementation: A physics-focused IDT shifts the approach from state prediction to information tracking. By monitoring entanglement between energy distributions and transitions, the IDT uncovers persistent information patterns even in chaotic motion, enabling adaptive responses that maintain coherent energy relationships rather than precise state trajectories.
