The Information Digital Twin (IDT)

The Information Digital Twin (IDT) anchors Entanglement Learning (EL), empowering intelligent systems to self-evaluate and adapt by maximizing information throughput—defined as the quantified coherence between an agent internal processes and the environment.

Operating as a parallel, self-referential layer, the IDT monitors statistical dependencies across the agent's inputs, actions, and outcomes without altering the agent’s core logic. By tracking how effectively the agent channels information through its interaction loop, it detects environmental coherence declines and generates precise adaptation signals.

This reframes adaptation as an emergent property of optimized information flow rather than an engineered function.

The IDT thus drives autonomy by sustaining the agent’s entanglement with its environment—its primary informational goal. Non-invasive and modular, it integrates seamlessly into diverse AI architectures, from neural networks to reinforcement learners, paving the way for structurally independent, general-purpose agents.

Information Digital Twin (IDT) Architecture

The Information Digital Twin (IDT) operates as a parallel feedback layer that complements an agent’s primary architecture without altering its task-specific components. It connects to three key interfaces:

Observations: Receives the same sensory or input data as the agent.
Internal Model: Accesses intermediate representations or decision parameters.
Actions and Outcomes: Monitors the agent’s outputs and the resulting environmental responses.

By discretizing these components into probability distributions, the IDT continuously computes information-theoretic metrics—specifically entanglement measures—capturing the mutual predictability across the agent-environment interaction loop.

Rather than influencing decision logic directly, the IDT generates information gradients: precise signals indicating where statistical dependencies are weakening. These gradients are then translated into targeted parameter updates within the agent’s internal model, enabling real-time alignment without interfering with the agent’s functional pipeline.

Input Processing-This module continuously tracks the enabled system, collecting three key data streams: system inputs, control actions taken, and the results that follow, I.e., next state. It organizes this data for probability calculations, using a sliding window of recent information to keep analysis real-time.
Entanglement Metric Calculations-Using the collected data, this module calculates three essential metrics: entanglement (𝜓) to measure predictability, asymmetry (𝛬𝜓) to spot performance imbalances, and memory (𝜇𝜓) to check model accuracy. These metrics help evaluate system's information throughput dynamics.
Baseline Definition-This module sets reference values for the entangelement metrics when the system performs at its best. It studies normal patterns across various conditions to create adaptive thresholds, separating real issues from typical variations. Over time, it refines these benchmarks with more data, catching problems early.
Misalignment Detection-This component compares current metrics to the baseline, flagging significant deviations. It assesses the size and speed of changes, using flexible thresholds to decide if further investigation is needed, while ignoring normal fluctuations.
Alignment Restoration Analysis-When information throughput declines, this module pinpoints the cause by analyzing shifts in entanglement, asymmetry, and memory. It determines if the problem comes from model errors, optimization flaws, or external changes, then suggests fixes based on short- and long-term trends.
Control Signal Generation-Based on the findings, this module provides two outputs: local adjustments (like adjusting agent's prediction horizons or constraints), or detailed alerts for major problems needing system-wide updates. It prioritizes actions by urgency and impact.

Entropy and Entanglement Changes Along System-Environment Interactions

Notional EL metrics from our EL for Model Predictive Controller, MPC, use case, illustrating entanglement dynamics and information flow across different operational phases.

IHM

Beyond Exploration and Exploitation: The "Seek" Strategy

In Entanglement Learning (EL), the Information Digital Twin (IDT) introduces a third behavioral mode—SEEK—extending beyond the classical exploration–exploitation duality. Unlike reactive strategies driven by external rewards or uncertainty, the seek strategy is initiated by the IDT to actively maximize information throughput between the agent and its environment.

The IDT continuously monitors entanglement metrics, detecting coherence loss and generating information gradients that guide the agent toward states of higher mutual predictability. This intrinsic process drives autonomous reconfiguration of internal models or environmental engagement—without the need for external prompts.

Through this mechanism, the IDT operationalizes the seek strategy, enabling adaptation as a byproduct of sustained information alignment. Embedded within EL’s triadic framework (explore, exploit, seek), the IDT serves as the architectural anchor of autonomous intelligence.

IDT Deployment Flexibility

The IDT is designed as a modular overlay architecture, enabling broad deployment configurations with minimal integration overhead. Its core strength lies in its non-invasive structure and operational decoupling from the primary agent, allowing it to be positioned in multiple system contexts:

1. Embedded Mode

In this configuration, the IDT runs locally on the same hardware stack as the agent, directly interfacing with its data structures (e.g., internal state vectors, output activations). This mode supports:

Low-latency adaptation signals, ideal for control and robotics,
Tight integration with internal model checkpoints and planning routines.

2. Edge-Co-Processor Mode

Here, the IDT is deployed on a dedicated co-processor (e.g., TPU/FPGA/NPU), streaming relevant internal and environmental variables for independent analysis. Benefits include:

Workload isolation between agent execution and meta-evaluation,
Accelerated computation of entropic models and gradients,
Minimal disruption to the agent’s real-time operations.

3. Remote or Cloud-Hosted Mode

For large-scale or distributed systems, the IDT can operate as a remote service:

Streaming observation–action–outcome tuples,
Performing centralized entanglement analysis,
Broadcasting adaptation signals back to local agents.

This configuration supports fleet-level coordination, comparative diagnostics across agents, and long-term monitoring of alignment degradation trends.