arXiv:2602.10796v3 Announce Type: replace Abstract: Generative sequence modeling faces a fundamental tension between the expressivity of Transformers and the efficiency of linear sequence models. Existing efficient architectures are theoretically bounded by shallow, single-step linear updates, while powerful iterative methods like Test-Time Training (TTT) break hardware parallelism due to two dimensions of serial dependency: token-level state reliance and step-level iteration loops. We propose PRISM (Parallel Residual Iterative Sequence Model) to resolve this tension. PRISM explicitly reconstr
The continuous push for more efficient and performant foundational AI models drives research toward resolving fundamental architectural tensions in generative sequence modeling.
This breakthrough provides a potential path to achieving both the high expressive power of Transformers and the computational efficiency demanded by real-world AI applications, influencing the scalability and deployment of future AI systems.
The explicit re-conception of iterative sequence modeling with residual connections and parallel processing could lead to a new dominant architecture that bypasses current performance bottlenecks.
- · AI research institutions
- · Generative AI companies
- · AI hardware manufacturers
- · Cloud computing providers
- · Developers reliant on less efficient traditional LSTM/RNNs
- · Companies unable to adapt to new model architectures
PRISM could enable the development of more complex and sophisticated AI models that run on less compute.
Increased efficiency may accelerate the deployment of advanced generative AI across various industries, lowering operational costs.
The democratization of more powerful AI could further societal shifts in automation and information generation.
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Read at arXiv cs.LG