arXiv:2605.24330v1 Announce Type: new Abstract: Transformers and deep state space models (SSMs) sit at opposite ends of a basic design choice: attention routes each query through a growing key-value (KV) cache by content-based matching at quadratic cost, while deep SSMs compress context into a fixed-size recurrent state that is not directly addressed by query-key matching. We propose Interdomain Attention, which integrates an SSM into an attention module through kernel methods: an attention kernel is approximated by a finite feature map, the resulting key features and values are projected onto
The continuous drive to optimize computational efficiency and overcome scaling limitations in large language models necessitates novel architectural innovations like Interdomain Attention.
This research addresses a fundamental bottleneck in AI model design, potentially enabling more efficient and scalable large models, crucial for future AI development and application.
The proposed Interdomain Attention integrates state space models with attention mechanisms, offering a new paradigm for handling context in transformer-like architectures without incurring quadratic cost.
- · AI model developers
- · Cloud computing providers
- · Hardware manufacturers
- · Large language model companies
- · Companies reliant on current inefficient transformer architectures
More efficient and powerful AI models become feasible, accelerating research and deployment.
Reduced computational costs could democratize access to advanced AI capabilities.
New AI applications emerge that were previously computationally intractable, impacting various industries.
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Read at arXiv cs.LG