arXiv Machine Learning
Shared Phase and Retention Control for Efficient Adaptive Spectral Recurrence
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arXiv AI
Jun 2
SHARP: Sleep-based Hierarchical Accelerated Replay for Long Range Non-Stationary Temporal Pattern Recognition
arXiv:2606. 00732v1 Announce Type: new Abstract: Learning long-range non-stationary temporal patterns remains a core challenge for modern sequence models, particularly in strict streaming settings.
arXiv Machine Learning
Aug 14
MARCH: Scaling Recurrent Memory with Content-Routed State Anchors
arXiv:2608. 12435v1 Announce Type: new Abstract: Transformers owe much of their strong long-context retrieval capability to a token-level memory that grows with context length.
arXiv AI
Jul 9
Exploration of Fast-Slow Latent Recurrence for Train-Short, Test-Long Generalization
arXiv:2604. 01577v3 Announce Type: replace-cross Abstract: We study out of distribution generalization in streaming tasks where models are trained on short sequences but must operate over much longer, unknown horizons under bounded memory.
arXiv Machine Learning
Sep 2
Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior
arXiv:2605.26797v2 Announce Type: replace Abstract: We study Latent Recurrent Transformer (LRT), a lightweight augmentation of autoregressive transformers that reuses a high-level source-layer hidden...
arXiv Machine Learning
Aug 17
Rollplex: Cross-Phase GPU Spatial Sharing for Vision Language Model Post-Training
arXiv:2608. 14498v1 Announce Type: new Abstract: Vision-language models (VLMs) enable embodied agents to reason and act from visual observations and language instructions.
arXiv AI
Jun 9
Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications
arXiv:2605. 11855v2 Announce Type: replace-cross Abstract: Sequence learning is dominated by Transformers and parallelizable recurrent neural networks (RNNs) such as state-space models, yet learning long-term dependencies remains challenging, and state-of-the-art designs trade power consumption for performance.