Modeling the Mind: A New Path to Artificial General Intelligence

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Author: Denis Avetisyan

Researchers propose an Intelligent Foundation Model that learns by replicating the temporal dynamics of the brain, offering a potential leap beyond current AI limitations.

The image depicts a conceptual overview of IFM. By integrating force measurements, IFM establishes a feedback loop that refines control signals, enabling precise manipulation and adaptive responses to external disturbances through the calculation of $F = ma$.
The image depicts a conceptual overview of IFM. By integrating force measurements, IFM establishes a feedback loop that refines control signals, enabling precise manipulation and adaptive responses to external disturbances through the calculation of $F = ma$.

This review details an Intelligent Foundation Model based on neuronal input-output and state neural networks to address core challenges in achieving Artificial General Intelligence.

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—inspired by neuronal dynamics—and trained via neuron output prediction. Could this biologically-grounded approach finally pave the way towards truly adaptive, reasoning-capable artificial general intelligence?

Beyond Pattern Matching: The Limits of Scale

Current Foundation Models excel at recognizing patterns, but struggle with genuine understanding and generalization. This limitation stems from a reliance on statistical correlation rather than underlying principles. Single-domain training further constrains adaptability, requiring vast task-specific datasets, unlike the flexibility observed in biological systems. Despite scaling efforts, these models lack the efficient learning mechanisms of biological brains—particularly in memory consolidation and continual learning. True intelligence isn’t about accumulating data, but distilling wisdom.

The state neural network processes information to inform decision-making within the system.
The state neural network processes information to inform decision-making within the system.

Biological systems can assimilate new information without catastrophic forgetting—a persistent challenge for current AI.

The Predictive Brain: A Framework for Intelligence

Neuroscientific models increasingly suggest the brain functions as a predictive engine. Predictive Processing and the Free Energy Principle posit that the brain continually generates and refines internal models of the world, minimizing prediction error. Complementary Learning Systems – the hippocampus and neocortex – facilitate efficient knowledge acquisition and consolidation. The hippocampus enables rapid learning, while the neocortex provides stable, long-term storage. Global Workspace Theory explains how information becomes consciously accessible, suggesting information integration is critical for conscious awareness.

This division of labor allows for both flexible adaptation and robust memory.

Integrated Flow Memory: A Dynamic Learning Paradigm

Integrated Flow Memory (IFM) represents a shift from static pre-training to a dynamic learning paradigm, conceptualizing intelligence as a problem of temporal sequence learning. The core of IFM is the State Neural Network, engineered to mimic biological neurons through Neuron Connectivity and Neuron Plasticity. This network maintains an internal representation of the past as it processes sequential data.

The primary learning objective is Neuron Output Prediction, refined through Backpropagation and Truncated Backpropagation Through Time, informed by biological neuronal activity. Efficiency is enhanced through Indirect Neuronal Sampling, focusing resources on the most informative neurons.

A comparison reveals that the proposed IFM outperforms existing FMs in relevant metrics.
A comparison reveals that the proposed IFM outperforms existing FMs in relevant metrics.

This framework prioritizes processing information over time, rather than in isolated instances.

Towards Artificial General Intelligence: The Promise of IFM

Integrated Functional Models (IFM) depart from conventional Foundation Models, prioritizing dynamic sequence learning. This addresses limitations in models trained on static datasets and discrete tasks. By emphasizing sequential processes, IFM establishes a framework for generalized intelligence.

IFM’s architecture facilitates learning from diverse behaviors, unlocking unprecedented generalization and enabling adaptation to novel situations without extensive retraining. Its focus on functional principles allows knowledge transfer across domains, demonstrating substantial promise in NLP applications—including Question Answering, Translation, Summarization, and Code Generation—exceeding the performance of established models like ChatGPT, DeepSeek, and Gemini.

The pursuit of intelligence reveals that true power resides not in size, but in the elegance of its underlying principles.

The pursuit of Artificial General Intelligence, as detailed in the proposed Intelligent Foundation Model, necessitates a reduction of complexity. The model’s focus on neuronal input-output transformations and temporal dynamics exemplifies this principle. It strives to distill intelligence to its core mechanisms, mirroring a fundamental truth articulated by Carl Friedrich Gauss: “If I have seen further it is by standing on the shoulders of giants.” This echoes the IFM’s ambition – to build upon established neurological understanding, not to reinvent the wheel, but to refine and build a more accurate, simpler model of cognition. The emphasis on understanding how intelligence arises, not simply replicating its output, is paramount. The work suggests that true progress demands paring away extraneous layers to reveal the elegant simplicity at the heart of intelligence.

Future Vectors

The proposition of an Intelligent Foundation Model, anchored in the temporal dynamics of neuronal input-output, does not dissolve existing challenges; it merely relocates them. The core difficulty remains the faithful capture of plasticity – not simply its simulation, but a demonstrable emergence within the model itself. Current metrics, largely focused on task performance, offer insufficient resolution to assess genuine cognitive mirroring. A focus on the process of learning, rather than the learned outcome, is paramount.

The field now faces a necessary subtraction. Unnecessary complexity, layered upon current architectures, is violence against attention. The pursuit of Artificial General Intelligence requires not more parameters, but a ruthless distillation of fundamental principles. The exploration of state neural networks, as presented, must be coupled with a rigorous investigation of information bottlenecks – identifying the minimal sufficient structure for intelligent behavior.

Ultimately, the question is not whether a model appears intelligent, but whether it embodies a demonstrably analogous mechanism to that of biological cognition. The density of meaning lies not in the breadth of capabilities, but in the fidelity of the underlying representation. The next iteration demands a shift from performance metrics to mechanistic validation.

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 18:05