SeDeM: Selective Decompression of Hidden-State Memories for Long-Context Question Answering
Long-context inference with large language models (LLMs) is costly: self-attention during prefill scales quadratically with sequence length, and the key-value (KV) cache grows with the number of processed tokens. Larger context windows also do not ensure reliable evidence use. Context compression reduces this cost, but many soft-compression methods use LLMs as compressors and rely on compact memory tokens both to preserve information and to condition the decoder. We propose SeDeM, a selective decompression framework that decouples compact memory storage from decoder conditioning. SeDeM stores context as compact hidden-state memory blocks, selects query-relevant blocks, and decompresses on...
| Authors | Maryam Haghifam, Jason Cong, Yizhou Sun |
|---|---|
| Primary category | cs.CL |
| Published | 31 July 2026 |
| Last updated at source | 10 September 2026 |
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