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AI Research
arXiv CS.AI·May 28·5 min read
Tensor Memory: Fixed-Size Recurrent State for Long-Horizon Transformers
TL;DR
Long-horizon transformers struggle with memory limitations when processing extended sequences. Tensor Memory is a new technique that introduces a fixed-size recurrent state to enhance memory efficiency.
✦ Why It Matters
Engineers can leverage Tensor Memory to enhance transformer models for applications requiring long-term context retention.
Key Takeaways
- Integrate Tensor Memory for enhanced video understanding.
- Utilize fixed-size memory to manage long sequences efficiently.
- Apply Gaussian-weighted volume techniques for spatial context.
- Evaluate performance improvements on standard benchmarks.
How It Works
Tensor Memory enhances Transformers by introducing a fixed-size 3D memory tensor that allows tokens to write into a voxel grid. This is achieved through a differentiable soft write that deposits information around a predicted 3D location.
The memory is updated using a local interaction operator and gated recurrent dynamics, which helps maintain relevant context. Tokens can then read back this context via continuous sampling, ensuring that the model retains important spatial information throughout processing.