Intelligence Foundation Model: A New Perspective to Approach Artificial General Intelligence

TL;DR

This paper introduces the intelligence foundation model (IFM), a biologically inspired approach aiming to learn the underlying principles of intelligence from diverse behaviors, advancing toward artificial general intelligence.

Contribution

It proposes a novel network architecture and learning objective inspired by biological neural dynamics to capture general intelligence mechanisms.

Findings

IFM demonstrates improved generalization across multiple domains.

The state neural network effectively models temporal neural dynamics.

Neuron output prediction unifies learning of structural neural behavior.

Abstract

We propose a new perspective for approaching artificial general intelligence (AGI) through an intelligence foundation model (IFM). Unlike existing foundation models (FMs), which specialize in pattern learning within specific domains such as language, vision, or time series, IFM aims to acquire the underlying mechanisms of intelligence by learning directly from diverse intelligent behaviors. Vision, language, and other cognitive abilities are manifestations of intelligent behavior; learning from this broad range of behaviors enables the system to internalize the general principles of intelligence. Based on the fact that intelligent behaviors emerge from the collective dynamics of biological neural systems, IFM consists of two core components: a novel network architecture, termed the state neural network, which captures neuron-like dynamic processes, and a new learning objective, neuron output prediction, which trains the system to predict neuronal outputs from collective dynamics. The state neural network emulates the temporal dynamics of biological neurons, allowing the system to store, integrate, and process information over time, while the neuron output prediction objective provides a unified computational principle for learning these structural dynamics from intelligent behaviors. Together, these innovations establish a biologically grounded and computationally scalable foundation for building systems capable of generalization, reasoning, and adaptive learning across domains, representing a step toward truly AGI.