Compressing Long Context into Answer-Aligned Memory Embeddings for LLM Inference
Large language model (LLM) inference is constrained by the quadratic scaling of self-attention and the linear scaling of the KV cache, increasing latency, energy consumption, and GPU memory demand as context length scales. Existing soft-compression methods either lack query-guided memory selection at inference time, train without answer-targeted supervision, or couple compression tightly to a specific decoder architecture. We propose a Context-to-Answer-Aligned Memory Compression (CMC) framework, which compresses long input contexts into compact Context Memory Embeddings (CMEs) aligned to any frozen decoder's embedding space, reducing inference costs without modifying decoder weights. CMC...
| Authors | Md Mostafizer Rahman, Md Faizul Ibne Amin, Md Shahajada Mia et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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