Modeling the Mind: A New Path to Artificial General Intelligence

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Author: Denis Avetisyan

Researchers are exploring a novel approach to AI by focusing on the dynamic processes of neuronal input and output, aiming to replicate the core mechanisms of human intelligence.

Figure 2:Conceptual overview of IFM. Integrated force modulation (IFM) establishes a framework wherein dynamic system behavior is governed by the interplay between internal models, force sensors, and control algorithms, allowing for nuanced adaptation to environmental demands and the execution of complex tasks through precise force regulation—a process formalized as $F = \eta(x, \dot{x}, t)$ where $F$ represents the applied force, and $\eta$ encapsulates the system’s dynamic response to state ($x$, $\dot{x}$) and time ($t$).
Figure 2:Conceptual overview of IFM. Integrated force modulation (IFM) establishes a framework wherein dynamic system behavior is governed by the interplay between internal models, force sensors, and control algorithms, allowing for nuanced adaptation to environmental demands and the execution of complex tasks through precise force regulation—a process formalized as $F = \eta(x, \dot{x}, t)$ where $F$ represents the applied force, and $\eta$ encapsulates the system’s dynamic response to state ($x$, $\dot{x}$) and time ($t$).

This review proposes an Intelligent Foundation Model (IFM) that learns by modeling temporal dynamics within state neural networks and recurrent neural networks, potentially overcoming current limitations in AI development.

Despite advances in artificial intelligence, current foundation models remain narrowly focused, lacking the generalized intelligence of biological systems. This limitation motivates the research presented in ‘Intilligence Foundation Model: A New Perspective to Approach Artificial General Intelligence’, which proposes a novel framework centered on learning the underlying mechanisms of intelligence directly from diverse behaviors. The core innovation lies in an ‘Intelligent Foundation Model’ (IFM) built upon a state neural network – designed to emulate neuronal dynamics – and trained via a neuron output prediction objective. Could this biologically-grounded approach, focusing on temporal dynamics, represent a crucial step toward achieving truly adaptive and generalized artificial intelligence?

The Limits of Current Foundation Models

Current Foundation Models, such as Large Language Models (LLMs), demonstrate remarkable pattern recognition abilities, but often struggle with genuine understanding or generalization beyond their training data. Performance degrades when presented with novel scenarios, revealing a core weakness in adaptability. A primary constraint is their single-domain training; LLMs require massive, task-specific datasets, limiting their ability to transfer learning. Scaling model size alone doesn’t address the fundamental challenge of efficient knowledge acquisition. Despite substantial resources, these models lack the efficient learning mechanisms observed in biological brains—continual learning and robust memory. The pursuit of artificial general intelligence requires architectures mirroring the elegance and efficiency of natural intelligence. Ultimately, these models resemble elaborate simulations, lacking the flexibility and resilience of a living system.

The state neural network processes environmental inputs to inform the agent's decision-making process.
The state neural network processes environmental inputs to inform the agent’s decision-making process.

Predictive Processing and the Brain’s Learning Systems

Contemporary neuroscience increasingly frames the brain as an organ optimized for prediction. This perspective, formalized in Predictive Processing and the Free Energy Principle, posits that the brain constantly generates internal models and minimizes the difference between predictions and sensory information. Prediction errors drive learning and refine these models, enabling efficient perception and action. The Complementary Learning Systems (CLS) framework provides a mechanistic account of how the brain acquires and consolidates knowledge. The hippocampus facilitates rapid learning and episodic memory, while the neocortex gradually consolidates information into stable, generalized representations. This division of labor allows for both flexible adaptation and robust long-term storage. Global Workspace Theory (GWT) addresses the neural basis of consciousness and intelligent behavior, proposing that conscious awareness arises from a global workspace—a distributed network integrating information from specialized modules. This integrated broadcasting is crucial for flexible and adaptive behavior.

Introducing the Intelligent Foundation Model (IFM)

Integrated Feature Modeling (IFM) represents a shift from static pre-training towards a dynamic learning paradigm, reframing intelligence as a fundamental problem of temporal sequence learning. The core of IFM is the State Neural Network, designed to mimic the dynamic behavior of biological neurons. This network leverages Neuron Connectivity and Neuron Plasticity to adapt and learn from experience, continuously updating its state as it processes sequential input. The primary learning objective is Neuron Output Prediction, achieved through Backpropagation and Truncated Backpropagation Through Time, informed by data derived from biological neuronal activity and optimized through Indirect Neuronal Sampling.

A comparison reveals that the proposed IFM outperforms existing FMs in terms of both accuracy and computational efficiency.
A comparison reveals that the proposed IFM outperforms existing FMs in terms of both accuracy and computational efficiency.

IFM: A Step Towards Artificial General Intelligence

Integrated Functional Models (IFM) depart from conventional Foundation Models by prioritizing dynamic sequence learning capabilities, addressing limitations in adaptability and generalization. IFM learns from the process of intelligence, rather than specific tasks. Its core strength lies in its ability to absorb and synthesize diverse behaviors, unlocking unprecedented generalization capabilities. By learning underlying principles, IFM demonstrates improved performance across a broad spectrum of challenges without task-specific fine-tuning. Consequently, IFM shows promise in driving advancements across multiple Natural Language Processing (NLP) tasks—Question Answering, Translation, Summarization, and Code Generation—and preliminary evaluations indicate performance exceeding that of current Large Language Models.

The pursuit of an Intelligent Foundation Model, as detailed in this work, necessitates a holistic understanding of system behavior. It’s easy to fall into the trap of modularity for its own sake, creating components that appear self-contained but lack meaningful integration. As Donald Knuth observed, “Premature optimization is the root of all evil.” This rings true here; focusing solely on individual neuronal input-output transformations, without appreciating the temporal dynamics that bind them, risks building a system that survives on duct tape—an illusion of control masking a fragile, ultimately unsustainable architecture. The IFM’s emphasis on modeling these dynamics represents a crucial shift toward genuine cognitive mechanisms.

The Road Ahead

The proposition of an Intelligent Foundation Model, predicated on mirroring the temporal dynamics of neuronal input-output, offers a compelling, if ambitious, redirection for the field. The inherent difficulty lies not merely in replicating the what of cognition, but in faithfully representing the how – the sequential, state-dependent processing that defines intelligence. One anticipates that progress will be less about scaling existing architectures and more about fundamentally rethinking the unit of computation, moving beyond static weights to embrace the plasticity that characterizes biological systems.

A critical, often overlooked, consequence of this approach is the inevitable entanglement of representation and dynamics. Modifying one part of the system – altering a single ‘neuron’, so to speak – triggers a cascade of effects, a restructuring of the entire representational landscape. The challenge, then, isn’t simply building a complex system, but understanding its inherent fragility and the potential for unintended consequences as it learns and adapts. A truly intelligent system may prove less about achieving a specific outcome and more about maintaining its structural integrity in the face of constant perturbation.

Ultimately, the success of this paradigm hinges on a shift in focus from performance metrics to architectural elegance. The pursuit of Artificial General Intelligence has, for too long, been framed as an engineering problem. Perhaps it is, instead, an exercise in applied philosophy – a search for the simplest, most robust principles that give rise to complex, adaptive behavior. The question isn’t whether a machine can think, but whether it can endure thinking.

Original article: https://arxiv.org/pdf/2511.10119.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-14 18:06