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Recent Test-Time Training (TTT) architectures compress context into fast weights that are updated online and queried as memory. Existing TTT designs keep this memory private to each layer: it recurs only over time, and depth merely indexes L separate memories. We argue that memory ownership need not be tied to depth, a...
Multi-Head Attention Residuals (MHAR) is introduced: the routing query is reshaped into H per-subspace heads, each with its own softmax over the depth history, and a direct probe of the trained queries confirms that learned subspace disagreement is the underlying driver.
Test-Time Training with Next-Token Prediction (TTT-NTP), a drop-in fast-weight adaptation method for pretrained LLMs that instead supervises updates using the model's own next contextual hidden state, while preserving commonsense and knowledge performance.
Chimera, a hybrid visual diffusion backbone with a principled scaling recipe that combines Kimi Delta Attention for long-context state tracking with O(N) complexity, interleaved Multi-head Latent Attention (MLA) for direct global interaction, and modality-aware short convolutions for local spatiotemporal context is int...
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