Block-Wise Differentiable Sinkhorn Attention: Tail-Refinement Gradients with a Gap-Aware Dustbin Bridge
arXiv:2605.08123v2 Announce Type: replace Abstract: We study long-context balanced entropic optimal transport (OT) attention on TPU hardware through a stopped-base, fixed-depth tail-refinement surrogate. After a stopped $T$-step Sinkhorn solve, we unroll a short refinement tail and differentiate that surrogate exactly. For the reported $R=2$ TPU path, the backward pass contains four staircase plan factors. We prove an exact one-reference-tile schedule: the $R=2$ score cotangent is a single reference plan tile times an explicit modifier field built from vector cotangents and dual differences. T
The continuous push for more efficient and scalable AI models, especially for long-context understanding, drives innovations in attention mechanisms and their hardware implementation.
Improving attention mechanisms directly impacts the efficiency and capability of large AI models, potentially leading to significant advancements in processing long sequences of data and reducing computational costs.
This research introduces a novel, differentiable Sinkhorn attention mechanism designed for TPUs, improving the scalability and memory efficiency of long-context AI models.
- · AI model developers
- · Cloud computing providers
- · TPU manufacturers
- · Large language model ecosystems
- · Less efficient AI hardware architectures
- · Companies relying on less scalable attention mechanisms
AI models will become more capable of understanding and generating long sequences of text or data with reduced computational overhead.
The improved efficiency could accelerate the development of more complex AI agents and applications requiring extensive context processing.
Increased accessibility due to lower computational costs might democratize advanced AI capabilities, fostering broader innovation across various sectors.
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Read at arXiv cs.LG