GradMem
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GradMem: Learning to Write Context into Memory with Test-Time Gradient Descent
arXiv:2603.13875v2 Announce Type: replace Abstract: Many large language model applications require conditioning on long contexts. Transformers typically support this by storing a large per-layer KV-cache of past activations, which incurs substantial memory overhead. A desirable alternative is compressive memory: read a context once, store it in a compact state, and answer many queries from that state.