{"total":18,"rows":[{"id":"2404.14294","title":"A Survey on Efficient Inference for Large Language Models","created":"2024-04-22","cited_by_count":205,"area":"inference"},{"id":"2402.16363","title":"LLM Inference Unveiled: Survey and Roofline Model Insights","created":"2024-02-26","cited_by_count":152,"area":"inference"},{"id":"2410.14731","title":"MatryoshkaKV: Adaptive KV Compression via Trainable Orthogonal Projection","created":"2024-10-16","cited_by_count":29,"area":"inference"},{"id":"2505.24357","title":"ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline Calibration","created":"2025-05-30","cited_by_count":16,"area":"inference"},{"id":"2510.24273","title":"SALS: Sparse Attention in Latent Space for KV cache Compression","created":"2025-10-28","cited_by_count":11,"area":"inference"},{"id":"2512.05916","title":"KQ-SVD: Compressing the KV Cache with Provable Guarantees on Attention Fidelity","created":"2025-12-05","cited_by_count":10,"area":"inference"},{"id":"2605.17757","title":"OSCAR: Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization","created":"2026-05-18","cited_by_count":9,"area":"inference"},{"id":"2505.12942","title":"A3 : an Analytical Low-Rank Approximation Framework for Attention","created":"2025-05-19","cited_by_count":8,"area":"inference"},{"id":"2505.22913","title":"Mustafar: Promoting Unstructured Sparsity for KV Cache Pruning in LLM Inference","created":"2025-05-28","cited_by_count":7,"area":"inference"},{"id":"2603.17946","title":"CARE: Covariance-Aware and Rank-Enhanced Decomposition for Enabling Multi-Head Latent Attention","created":"2026-03-18","cited_by_count":5,"area":"inference"},{"id":"2601.21686","title":"Don't be so Stief! Learning KV Cache low-rank approximation over the Stiefel manifold","created":"2026-01-29","cited_by_count":3,"area":"inference"},{"id":"2605.08314","title":"FlashSVD v1.5: Making Low-Rank Transformers Inference Actually Fast","created":"2026-05-08","cited_by_count":3,"area":"inference"},{"id":"2604.01609","title":"Swift-SVD: Theoretical Optimality Meets Practical Efficiency in Low-Rank LLM Compression","created":"2026-04-02","cited_by_count":2,"area":"inference"},{"id":"2505.21077","title":"Efficient Large Language Model Inference with Neural Block Linearization","created":"2025-05-27","cited_by_count":1,"area":"inference"},{"id":"2511.02681","title":"Optimal Singular Damage: Efficient LLM Inference in Low Storage Regimes","created":"2025-11-04","cited_by_count":0,"area":"inference"},{"id":"2609.29913","title":"MILO: Efficient Many-shot In-Context Learning with Block-wise Low-rank Compression","created":"2026-09-24","cited_by_count":0,"area":"inference"},{"id":"2610.06927","title":"AttSVD:Prompt-Adaptive Low-Rank KV Cache Compression via Attention-Guided SVD","created":"2026-10-03","cited_by_count":0,"area":"inference"},{"id":"2610.17966","title":"BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation","created":"2026-10-13","cited_by_count":0,"area":"inference"}],"next_cursor":null}