arXiv:2603.16689v2 Announce Type: replace Abstract: Next-token predictors often appear to develop internal representations of the latent world and its rules. The probabilistic nature of these models suggests a deep connection between the structure of the world and the geometry of probability distributions. In order to understand this link more precisely, we use a minimal stochastic process as a controlled setting: constrained random walks on a two-dimensional lattice that must reach a fixed endpoint after a predetermined number of steps. Optimal prediction of this process solely depends on a s
This paper represents current research exploring the fundamental mechanisms by which AI models form internal representations, building on the recent rapid advancements in 'next-token predictors' like large language models.
Understanding how AI models develop internal world representations is crucial for developing more robust, interpretable, and generalizable AI, moving beyond purely black-box systems.
This research contributes to the growing body of knowledge that deepens our theoretical understanding of AI, potentially guiding future architectural and training innovations, rather than immediate technological shifts.
- · AI researchers
- · Machine learning theoreticians
- · Academic institutions
- · AI developers relying solely on empirical scaling laws
Improved theoretical understanding of emergent AI capabilities and internal representations.
Development of new AI architectures or training methodologies that leverage insights into how models construct world models.
More efficient and less 'black box' AI systems capable of more complex reasoning and planning based on robust internal representations.
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Read at arXiv cs.LG