A Historical and Motivational Introduction

The dream of Artificial General Intelligence (AGI)—a machine capable of matching human cognitive flexibility—has driven computer science since the Dartmouth Workshop in 1956. For decades, this pursuit was divided: the Symbolic AI tradition focused on formal rules and logic, often failing to interface with the messy, continuous real world; simultaneously, the Connectionist (Deep Learning) tradition excelled at perception and pattern recognition but lacked intrinsic causality and high-level reasoning. The advent of powerful Large Language Models (LLMs) like Gemini, with their vast store of codified human knowledge, reignited the AGI debate but highlighted a persistent gap: how does a text-based brain effectively govern a body in the physical world?

This work directly tackles that gap. Inspired by the architectural pillars proposed by influential thinkers such as Yann LeCun, the system presented here demonstrates true modularity. It transcends the limitations of monolithic LLMs by integrating Vision Joint-Embedding Predictive Architecture (V-JEPA) for real-world sensing, a Predictive Latent Dynamics Model (PLDM) for internal causal simulation, and the advanced reasoning of Gemini 3 Pro for operational oversight. By combining these specialized modules, the architecture aligns with the five core AGI pillars, resulting in a unified, agentic system capable of coherent action in a complex environment such as autonomous flight operations. This integration represents a critical evolutionary leap from abstract knowledge processing toward embodied, causal, and safe decision-making.

The AGI Architecture: Aligning Code with Conceptual Pillars

The successful refactoring of the code showcases the integration of an LLM (Gemini 3) with a perception system (V-JEPA) and a dynamics model (PLDM) to conceptually demonstrate the Five AGI Pillars for an autonomous flight agent. The entire notebook structure—from data ingestion and model training to the final Gemini assessment—is designed to address these fundamental requirements of next-generation AI.

1. World Models that Predict and Reason About Real Situations

Pillar Alignment: The system explicitly uses a Latent Dynamics Predictor (the "World Model") to learn the causal relationships of aircraft states in a hidden, compact space. Code Implementation:

• The LatentDynamicsPredictor takes the current latent state ($\mathbf{z}_t$) and a conceptual action ($\mathbf{a}_t$) to predict the next latent state ($\mathbf{z}_{t+1}$).


• This World Model is conceptually trained on real ADS-B flight telemetry data (Latitude, Longitude, Altitude, Speed), which, after being projected into the latent space, enables the model to predict how the aircraft's physical state will change in response to its controls (actions).

2. Autonomous Learning that Discovers Causal Structure

Pillar Alignment: The system moves beyond memorizing patterns by building a predictive model that understands cause-and-effect ($\text{Action} \to \text{Next State}$) in the latent space. Code Implementation:

• The model explicitly predicts the next state from the current state and a discrete action (a change in speed/altitude). This forced link creates a causal graph, unlike traditional pattern-matching models.


• The use of the Joint Embedding Predictive Architecture (JEPA) loss ensures the learned latent space is stable and information-rich, which is essential for discovering robust causal relationships. Crucially, the use of a loss function inspired by LEJEPA's regularization ensures this latent space prevents representational collapse, reinforcing stability and predictive power.

3. Energy-based or Modular Systems that Reason, Plan, and Act Coherently

Pillar Alignment: The system is inherently modular, separating Perception (V-JEPA for feature extraction), High-Level Reasoning (Gemini LLM for operational assessment), and Causal Planning (Latent Dynamics Predictor). Code Implementation:

• The final code execution demonstrates coherence: V-JEPA's output $\to$ Classifier's output $\to$ Gemini's operational assessment (e.g., "Runway occupied, initiate ground handling"). This modular flow enables reasoning about the observed state before taking action.


• Planning (Conceptual): The theoretical planning loop (MPPI-inspired in the overall system design) uses a cost function to guide the agent, serving as a conceptual energy function that drives the plan toward the lowest "energy" (cost) state.

4. Embodied Sentience and Salience

Pillar Alignment: The agent is embodied through its visual input (V-JEPA processing a video from an assumed aircraft perspective) and its reliance on physical state data (ADS-B telemetry). It focuses on what matters—the operational context. Code Implementation:

• Embodiment: The classifier explicitly links visual evidence (V-JEPA features from a camera) to a physical operational status ("airplane landing").


• Salience: The input to the Gemini LLM includes the Classification Confidence (e.g., 1.00), forcing the LLM to ground its reasoning in the system's certainty and focus its output on the highest-confidence visual state.

5. Cognitive World Models and Evolutionary Learning Modules

Pillar Alignment: This describes a hybrid system, demonstrated here by combining the mathematically rigorous Cognitive World Model (the latent state predictor) with a Symbolic Reasoning system (the Gemini LLM). Code Implementation:

• Common-sense Reasoning: The Gemini LLM provides high-level, common-sense reasoning ("Runway occupied, prepare for taxiing") based on the low-level sensory input.


• Analog-Digital Integration: The Classifier acts as the direct translation layer between the continuous, analog perception space (V-JEPA feature vector) and the symbolic, digital planning space (Gemini's text response and the discrete $\mathbf{a}_t$ actions).

Conclusion: A Unified Leap Toward Agentic AGI

The architecture demonstrated by integrating V-JEPA, the Predictive Latent Dynamics Model, and Gemini 3 Pro's advanced reasoning represents a pivotal shift from narrow AI utility to the design of truly agentic AGI systems. The success of this modular approach validates the need to combine specialized components: V-JEPA for what is seen, PLDM for what will happen, and Gemini for what should be done.

By separating these cognitive functions—perception, internal modelling, and high-level command—the system gains robustness, transparency, and, crucially, causal intelligence. This framework provides a robust foundation for building self-supervised, self-correcting agents capable of safely navigating the complexities of the real world, from flight control to complex industrial automation. The core challenge of AGI is not just generating language or classifying images, but orchestrating these functions coherently under real-world constraints. This project offers a compelling solution, establishing a modular paradigm that will define the next generation of autonomous intelligence.

By FRANK MORALES

Keywords: Agentic AI, Generative AI, Predictive Analytics

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