arXiv:2607.08196v1 Announce Type: cross Abstract: As part of a series on first-principles modeling of cognitive functions, this paper attempts to provide a mathematical formulation of thinking and perception. It formally derives slow thinking or more generally, active perception, and encompasses the design, training and inference of slow thinking large language models. Our starting point is the lifting and projection of probability distributions on the observable and latent spaces, with the objective of representing complex data distributions by simple function families such as neural networks
This paper attempts a foundational mathematical formulation of cognitive functions like thinking and perception, signalling a deeper theoretical dive into AI before potential scaling plateaus.
A robust first-principles theory could unify and accelerate AI research, leading to more generalizable and efficient AI systems, and clarify the limitations of current approaches.
The focus might shift from purely empirical scaling laws to theory-driven design and optimization for future large language models and active perception systems.
- · AI researchers
- · Deep learning framework developers
- · Cognitive science
- · Advanced AI computing providers
- · Companies relying on brute-force, untheorized AI scaling
- · Fragmented AI research areas
This paper provides a theoretical basis for novel AI architectures and training methodologies, especially for 'slow thinking' and 'active perception'.
Improved theoretical understanding could de-risk and accelerate the development of more robust and human-like AI agents, impacting various industries.
A unified theory of intelligence could lead to a 'Cambrian explosion' in AI capabilities, fundamentally altering the human-machine interface and economic structures.
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Read at arXiv cs.CL