WhiteMatter: All-to-All Cross-Layer Connections via KV Source Mixing
When generating text, a Transformer produces representations of past tokens at every layer, but each layer can normally use only representations from the same depth. This restriction prevents the model from fully reusing information it has already computed. We introduce WhiteMatter, which allows every layer to draw on past-token representations from any depth. A learned mixer selects the most useful depths for the current context and combines their representations into shared key-value (KV) cache channels. Sharing these channels across layers can reduce the cache size. Given the same number of training tokens, WhiteMatter with a full-size cache performs comparably to a standard Transforme...
| Authors | Wenbo Zhang, Xiang Ren |
|---|---|
| Primary category | cs.CL |
| Published | 19 August 2026 |
| Last updated at source | 27 September 2026 |
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