Activation- and Influence-Aware Ranks (AIR): Function-Preserving SVD Compression for LLMs
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Computer Science > Machine Learning
Title:Activation- and Influence-Aware Ranks (AIR): Function-Preserving SVD Compression for LLMs
Abstract:We present Activation- and Influence-Aware Ranks (AIR), an SVD-based LLM compression framework that guides each weight matrix's low-rank approximation with a backward-signal influence metric. Starting from the activation-aware optimum of SVD-LLM(W), AIR runs a single closed-form alternating least squares (ALS) sweep that integrates influence element-wise under a monotone-descent guarantee. AIR is layer-local and composes orthogonally with end-to-end methods: alone it exceeds ACIP, and AIR+LoRA outperforms it further. AIR improves perplexity over SVD-LLM(W) by >18% at <=60% parameter retention, matches its quality with ~90% less calibration data, and turns parameter savings into FLOP, peak-memory, and per-token latency gains.
| Comments: | Accepted at the ICML 2026 Workshop on Resource-Adaptive Foundation Model Inference (AdaptFM), Seoul, South Korea (non-archival) |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.19993 [cs.LG] |
| (or arXiv:2606.19993v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.19993
arXiv-issued DOI via DataCite (pending registration)
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