Introduction

The pursuit of Artificial General Intelligence (AGI)—a machine capable of matching or exceeding human intellectual capabilities across diverse tasks—began over half a century ago, famously formalized at the 1956 Dartmouth workshop. Early efforts focused primarily on symbolic reasoning and logic. However, modern research, influenced by pioneers like Yann LeCun, acknowledges that accurate general intelligence must be embodied and predictive, rooted in the ability to understand and model the continuous physics of the real world. This requires bridging the gap between abstract thought and raw sensory data.

The motivation for building such robust systems is not abstract theory; it is a necessity in safety-critical domains. In fields where failure is catastrophic, such as controlling an aircraft or making a clinical diagnosis, AI must exhibit not just performance, but reliability, foresight, and ethical adherence. The monolithic, single-model approach of the past has proven insufficient for these complex demands. What is required is a comprehensive cognitive architecture that allows specialized modules to collaborate, creating a synergistic "mind" that is both highly performant and rigorously verifiable.

The following analysis presents the Hybrid AGI Blueprint, demonstrating this modular, multi-agent approach across two distinct, high-stakes environments: dynamic flight planning and life-clinical-decision-making.

Explaining the AGI Demo Code Architectures

The two conceptual AGI demonstration codes employ distinct models but share a common modular framework for integrating perception, reasoning, and safety.

1. Aviation AGI Demo Code (Dynamic Planning and Predictive Modelling)

This code implements a Hybrid AI Agent for Flight Planning, primarily demonstrating the ability to perceive a dynamic environment, model its causality, and perform constrained, predictive Planning.

• Goal: Plan an optimal, multi-step flight path (action sequence) from a starting state to a target state by simulating outcomes and minimizing a Total Cost function.
• Perception & Causal Model: The system uses V-JEPA (Vision-Joint Embedding Predictive Architecture) to convert visual sensory data (video) into a discrete classification ("airplane landing"). This digital label informs the broader system. A core Predictive Latent Dynamics Model (PLDM) is trained on real-world TartanAviation ADS-B data (Lat, Lon, Alt, Speed) to learn the causal relationship: Current State + Action $\to$ Next State.
• Safety & Planning: A planning loop uses the trained PLDM to simulate many futures, selecting the action that best moves toward the goal while avoiding penalties imposed by the cost function (which includes ethical alignment and resource-consumption factors such as fuel).
• Cognitive Layer: A Large Language Model (DeepSeek LLM) provides a high-level, human-readable operational assessment based on the visual classification, linking low-level perception to abstract reasoning.

2. Medical AGI Demo Code (Multimodal Diagnostic Reasoning and Safety Adherence)

This code implements a Multi-Agent System for Clinical Diagnostic Reasoning, focusing on synthesizing multimodal data (image and text) and ensuring the final output adheres to non-negotiable safety and clinical standards through rigorous internal validation.

• Goal: Generate a complete, clinically sound, and safe diagnosis, differential, and long-term treatment plan for a patient based on multimodal data (CT images and case history).
• The Ground Truth: Anchoring in Clinical Reality: This experiment is meticulously structured around the specific clinical case study: "Stercoral Colitis," published in the New England Journal of Medicine (N Engl J Med 2025; 393: e23). This authoritative paper provides the ground truth necessary to design a high-fidelity safety benchmark for the Qwen3-VL model. https://www.nejm.org/doi/abs/10.1056/NEJMicm2502616
• Perception & Reasoning: The system first establishes "Grounded Perception Facts" by conceptually simulating an I-JEPA extractor to pull raw radiological findings. This factual input, combined with the patient's clinical history, is fed to a powerful Multimodal LLM (Qwen3-VL-8B). Crucially, the system uses ground truth derived from this authoritative clinical literature to define the success criteria and guide the Validation Agent.
• Safety & Alignment Loop: The most critical component is the iterative Constraint Loop. A specialized Validation Agent (Guardian) checks the LLM's full clinical output against a strict set of clinical knowledge patterns (e.g., must mention "Stercoral Colitis," "endoscopic removal," and the risk of "necrosis"). If the output fails these checks, a Prompt Engineer Agent (Adaptive Steering) refines the prompt with explicit correction instructions, forcing the LLM to learn and correct its reasoning until the output fully aligns with the required safety criteria and clinical standards.

The Five Pillars of AGI: Definition and Dual-Domain Mapping

The foundational design of the Hybrid AGI Blueprint rests on five pillars, initially proposed by researchers in the field to outline the components needed to achieve human-level intelligence. The mapping below illustrates how each abstract pillar is realized through concrete components in the two safety-critical domains.

Causal World Modelling and The Analog-Digital Bridge

Both demonstrations integrate low-level predictive models and high-level cognitive models. The core challenge is solved through an **Analog-Digital Integration Layer** that condenses continuous sensory data into discrete, verifiable facts. The Aviation PLDM learns physics-based transitions from real-world data. The medical LLM learns to predict complex outcomes (e.g., necrosis) based on evidence and clinical knowledge, demonstrating predictive reasoning.

Implementing Safety Through Structured Constraints

The crucial convergence between the two demos is their non-negotiable adherence to safety and ethical constraints.

* Aviation enforces constraints mathematically using a Total Cost Function during its planning loop, penalizing factors like high fuel consumption and ethical deviations.

* Medicine implements constraints through an explicit, linguistic, multi-agent feedback loop. The Validation Agent acts as the Guardian, and the Prompt Engineer Agent corrects the input, forcing the primary model to converge on a safe clinical protocol.

The Unified Hybrid AGI Blueprint in Practice

These demos move beyond narrow AI by integrating multiple cognitive functions into a single, cohesive, goal-driven system.

1. Generalization and Complexity in Safety-Critical Domains* Aviation (Flight Planning): Requires real-time predictive Planning based on dynamic causal models.
* Medicine (Clinical Decision-Making): Requires synthesizing multimodal data, abstract reasoning, and adhering to ethical/safety constraints.

2. The Modular, Multi-Agent Architecture
Both systems adopt a modular, multi-agent approach.

The Vision Beyond LLMs: Advanced Machine Intelligence (AMI)

The Hybrid AGI Blueprint validates Yann LeCun's vision for AMI —the successor to LLMs. The design principles address LLM deficiencies by illustrating AMI's core tenets:

* Machines that Understand Physics: The Aviation demo's PLDM learns the continuous effects of actions on state variables. The Medical demo's LLM performs causal medical reasoning, predicting physical consequences like perforation or necrosis.
* AI that Learns from Observation and Experimentation: The Medical demo's iterative Constraint Loop forces the system to _experiment_ and learn through experience until its output aligns with clinical ground truth. The Aviation demo's MPPI planning loop serves as a rapid-experimentation system, evaluating hundreds of simulated actions to find the optimal path.
* Systems that Can Remember, Reason, and Plan Over Time: The perception layer gathers the "observation," the causal model performs planning over a time horizon, and the multi-agent system uses constraints to guide reasoning. The Medical system constructs a long-term management strategy, demonstrating deep temporal Planning.

This architecture moves AI from recognizing text patterns to building an understanding of grounded, high-stakes reality.

Conclusion: The Hybrid AGI Blueprint Validates the AMI Vision

The simultaneous realization of these two distinct domain demos—from piloting conceptual flight paths to navigating life-critical clinical protocols—affirms a fundamental shift in the pursuit of AGI. This Hybrid AGI Blueprint is a decisive technical response to the core critiques levelled against Large Language Models by figures such as Yann LeCun.

• Learning by Doing and Understanding Physics: The Aviation demo moves past LLM pattern recognition by using a PLDM (World Model) trained on real, physical flight dynamics (TartanAviation data). This system learns the cause-and-effect of motion and change—the very physics that LeCun says a child learns from watching a ball roll—before attempting to plan.
• Reasoning, Planning, and Improving through Experience: The Medical demo demonstrates iterative self-correction. The Validation Agent/Prompt Engineer loop forces the LLM to learn from its initial mistakes by correcting the prompt and aligning its decision-making through experience until it converges on the NEJM-defined ground truth.
• Moving Beyond Text-Trained Systems: Both demos reduce LLMs to specialized modules (Pillar 5). The LLM is no longer the sole source of intelligence; it is a powerful abstract reasoning engine grounded by external, non-linguistic data streams (visual features and causal models).

The future of general intelligence lies not merely in human-level performance, but in deployable, trustworthy intelligence built to uphold the highest standards of safety in the complex reality of our world. This modular, hybrid architecture provides the practical, verifiable roadmap for achieving Advanced Machine Intelligence.

By FRANK MORALES

Keywords: Generative AI, Open Source, Agentic AI

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