arXiv Machine Learning
You Do Not Fully Utilize Transformer's Representation Capacity
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Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior
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Full-bandwidth transformer
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DepthWeave-KV: Token-Adaptive Cross-Layer Residual Factorization for Long-Context KV Cache Compression
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Dynamic Short Convolutions Improve Transformers
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Shared Global KV with Layer-Specific Local History
The paper investigates how to combine shared global key‑value (KV) caches with layer‑specific local history in decoder‑only Transformer language models. By separating historical content from the input source used to form it, the authors show that adding local history can reduce held‑out test perplexity by about 1.4% compared to a current‑token local branch, while also demonstrating benefits in capacity, entry‑count, and training‑compute controls. Experiments on a 126M‑parameter model with 2K context reveal that local history remains valuable even when adjacent layers share local inputs, and that a sufficient suffix schedule can reduce upper‑layer construction work without losing cache completeness.