arXiv:2511.05963v2 Announce Type: replace Abstract: Transformers replace recurrence with a memory that grows with sequence length and self-attention that enables ad-hoc lookups over past tokens. Consequently, they lack an inherent incentive to compress history into compact latent states with consistent transition rules. This often leads to learning solutions that generalize poorly. We introduce Next-Latent Prediction (NextLat), which extends standard next-token training with self-supervised predictions in the latent space. Specifically, NextLat trains a transformer to learn latent representati
The paper addresses a known limitation of current transformer architectures regarding efficient knowledge representation, at a time when 'world models' are a major research frontier.
Improving the efficiency and generalization of foundational AI models directly impacts the capabilities and accessibility of future AI systems, potentially leading to more robust and less resource-intensive AI.
This research suggests a pathway to more compact and generalizable AI models by incentivizing internal compression of 'world knowledge,' potentially reducing the need for extremely large models and training datasets for certain tasks.
- · AI researchers
- · Generative AI developers
- · Robotics
- · Resource-constrained AI applications
- · Developers relying solely on brute-force scaling
- · Inefficient AI training methods
Transformers become more efficient at learning robust internal representations of complex environments.
This efficiency could accelerate the development of more capable AI agents and systems that require integrated 'world understanding' for advanced reasoning.
More efficient and generalizable AI models might reduce the compute and energy footprint of future AI, altering the competitive landscape and resource dependencies.
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Read at arXiv cs.LG