Compressed-Sensing-Guided, Inference-Aware Structured Reduction for Large Language Models
Large language models deliver strong generative performance but at the cost of massive parameter counts, memory use, and decoding latency. Prior work has shown that pruning and structured sparsity can preserve accuracy under substantial compression, while prompt-compression methods reduce latency by removing redundant input tokens. However, these two directions remain largely separate. Most model-compression methods are static and optimized offline, and they do not exploit the fact that different prompts and decoding steps activate different latent computational pathways. Prompt-compression methods reduce sequence length, but they do not adapt the executed model subnetwork. We propose a u...
| Authors | Andrew Kiruluta |
|---|---|
| Primary category | cs.CL |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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