arXiv:2605.27686v1 Announce Type: cross Abstract: Transformers process images and videos by flattening space and time into long token sequences. While attention and KV caching preserve past features, their memory grows with sequence length and they lack an explicit, persistent spatial state, making long-horizon video understanding and occlusion-sensitive reasoning difficult. We propose Tensor Memory, a lightweight module that augments Transformer blocks with a fixed-size recurrent 3D memory tensor: tokens write into a voxel grid via a differentiable soft write that deposits content as a Gaussi
The continuous drive for more efficient and robust AI models, especially for handling long sequences in video and advanced spatial reasoning, necessitates novel architectural solutions like Tensor Memory.
This development addresses a fundamental limitation in current Transformer architectures, potentially enabling more sophisticated and context-aware AI agents and models for complex tasks.
Transformers can now maintain a persistent, fixed-size 3D spatial state, improving their ability to reason over long-horizon events and complex visual scenes without unbounded memory growth.
- · AI research labs
- · Robotics companies
- · Video analytics platforms
- · Autonomous vehicle developers
- · Legacy deep learning architectures
- · Companies reliant on simple, short-horizon vision models
Transformers become more efficient and capable for long-horizon video understanding and spatial reasoning.
This could accelerate the development of more robust general-purpose AI agents and advanced robotics.
Improved spatial and temporal reasoning in AI could lead to breakthroughs in areas like scientific discovery and industrial automation.
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Read at arXiv cs.AI