Test-time training can now process arbitrarily long sequences by maintaining an anchor state that balances learning new information with remembering old information, solving the catastrophic forgetting problem that limited previous approaches to single large chunks.
This paper improves test-time training for 3D/4D scene reconstruction by preventing the model from forgetting previous information as it processes long sequences. The key innovation is using an elastic prior (inspired by elastic weight consolidation) to stabilize learning during inference, allowing the model to handle longer sequences in smaller chunks without catastrophic forgetting.