{"total":46,"rows":[{"id":"2210.09461","title":"Token Merging: Your ViT But Faster","created":"2022-10-17","cited_by_count":1021,"area":"inference"},{"id":"2305.17118","title":"Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time","created":"2023-05-26","cited_by_count":439,"area":"inference"},{"id":"2406.19707","title":"InfiniGen: Efficient Generative Inference of Large Language Models with Dynamic KV Cache Management","created":"2024-06-28","cited_by_count":245,"area":"inference"},{"id":"2404.14294","title":"A Survey on Efficient Inference for Large Language Models","created":"2024-04-22","cited_by_count":205,"area":"inference"},{"id":"2108.01390","title":"Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer","created":"2021-08-03","cited_by_count":177,"area":"inference"},{"id":"2302.07863","title":"Speculative Decoding with Big Little Decoder","created":"2023-02-15","cited_by_count":164,"area":"inference"},{"id":"2410.02367","title":"SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration","created":"2024-10-03","cited_by_count":160,"area":"inference"},{"id":"2107.00910","title":"Learned Token Pruning for Transformers","created":"2021-07-02","cited_by_count":139,"area":"inference"},{"id":"2312.01026","title":"Token Fusion: Bridging the Gap between Token Pruning and Token Merging","created":"2023-12-02","cited_by_count":125,"area":"inference"},{"id":"2405.14366","title":"MiniCache: KV Cache Compression in Depth Dimension for Large Language Models","created":"2024-05-23","cited_by_count":125,"area":"inference"},{"id":"2401.06104","title":"Transformers are Multi-State RNNs","created":"2024-01-11","cited_by_count":120,"area":"inference"},{"id":"2312.15234","title":"Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems","created":"2023-12-23","cited_by_count":102,"area":"inference"},{"id":"2407.18003","title":"Keep the Cost Down: A Review on Methods to Optimize LLM' s KV-Cache Consumption","created":"2024-07-25","cited_by_count":97,"area":"inference"},{"id":"2503.12491","title":"CAKE: Cascading and Adaptive KV Cache Eviction with Layer Preferences","created":"2025-03-16","cited_by_count":58,"area":"inference"},{"id":"2306.07050","title":"Revisiting Token Pruning for Object Detection and Instance Segmentation","created":"2023-06-12","cited_by_count":50,"area":"inference"},{"id":"2407.01527","title":"KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches","created":"2024-07-01","cited_by_count":42,"area":"inference"},{"id":"2402.06262","title":"On the Efficacy of Eviction Policy for Key-Value Constrained Generative Language Model Inference","created":"2024-02-09","cited_by_count":32,"area":"inference"},{"id":"2406.02657","title":"Block Transformer: Global-to-Local Language Modeling for Fast Inference","created":"2024-06-04","cited_by_count":32,"area":"inference"},{"id":"2407.15504","title":"Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models","created":"2024-07-22","cited_by_count":19,"area":"inference"},{"id":"2512.03324","title":"Cache What Lasts: Token Retention for Memory-Bounded KV Cache in LLMs","created":"2025-12-03","cited_by_count":19,"area":"inference"},{"id":"2403.01273","title":"NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention","created":"2024-03-02","cited_by_count":12,"area":"inference"},{"id":"2407.03157","title":"Let the Code LLM Edit Itself When You Edit the Code","created":"2024-07-03","cited_by_count":10,"area":"inference"},{"id":"2403.08312","title":"StreamingDialogue: Prolonged Dialogue Learning via Long Context Compression with Minimal Losses","created":"2024-03-13","cited_by_count":9,"area":"inference"},{"id":"2503.10714","title":"ZSMerge: Zero-Shot KV Cache Compression for Memory-Efficient Long-Context LLMs","created":"2025-03-13","cited_by_count":8,"area":"inference"},{"id":"2505.00019","title":"An Empirical Study on Prompt Compression for Large Language Models","created":"2025-04-24","cited_by_count":8,"area":"inference"},{"id":"2403.14919","title":"Hierarchical Skip Decoding for Efficient Autoregressive Text Generation","created":"2024-03-22","cited_by_count":7,"area":"inference"},{"id":"2406.01283","title":"Focus on the Core: Efficient Attention via Pruned Token Compression for Document Classification","created":"2024-06-03","cited_by_count":7,"area":"inference"},{"id":"2408.01890","title":"Cross-layer Attention Sharing for Pre-trained Large Language Models","created":"2024-08-04","cited_by_count":5,"area":"inference"},{"id":"2502.15955","title":"Compression Barriers for Autoregressive Transformers","created":"2025-02-21","cited_by_count":5,"area":"inference"},{"id":"2508.00367","title":"Representation Shift: Unifying Token Compression with FlashAttention","created":"2025-08-01","cited_by_count":5,"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":"2406.07528","title":"QuickLLaMA: Query-aware Inference Acceleration for Large Language Models","created":"2024-06-11","cited_by_count":4,"area":"inference"},{"id":"2604.04987","title":"Cactus: Accelerating Auto-Regressive Decoding with Constrained Acceptance Speculative Sampling","created":"2026-04-05","cited_by_count":4,"area":"inference"},{"id":"2608.07009","title":"HiSparse: Scaling Sparse-Attention Decoding with Hierarchical KV Cache Management","created":"2026-08-07","cited_by_count":3,"area":"inference"},{"id":"2601.11471","title":"Low-Rank Key Value Attention","created":"2026-01-16","cited_by_count":2,"area":"inference"},{"id":"2403.02352","title":"ATP: Enabling Fast LLM Serving via Attention on Top Principal Keys","created":"2024-03-01","cited_by_count":1,"area":"inference"},{"id":"2403.17411","title":"PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large Language Models","created":"2024-03-26","cited_by_count":1,"area":"inference"},{"id":"2412.05225","title":"BEExformer: A Fast Inferencing Binarized Transformer with Early Exits","created":"2024-12-06","cited_by_count":0,"area":"inference"},{"id":"2504.12526","title":"MOM: Memory-Efficient Offloaded Mini-Sequence Inference for Long Context Language Models","created":"2025-04-16","cited_by_count":0,"area":"inference"},{"id":"2507.19409","title":"Modality Agnostic Efficient Long Range Encoder","created":"2025-07-25","cited_by_count":0,"area":"inference"},{"id":"2509.11155","title":"AQUA: Attention via QUery mAgnitudes for Memory and Compute Efficient Inference in LLMs","created":"2025-09-14","cited_by_count":0,"area":"inference"},{"id":"2509.16686","title":"EG-MLA: Embedding-Gated Multi-head Latent Attention for Scalable and Efficient LLMs","created":"2025-09-20","cited_by_count":0,"area":"inference"},{"id":"2510.12856","title":"Efficient Adaptive Transformer: An Empirical Study and Reproducible Framework","created":"2025-10-14","cited_by_count":0,"area":"inference"},{"id":"2605.14913","title":"Representative Attention For Vision Transformers","created":"2026-05-14","cited_by_count":0,"area":"inference"},{"id":"2606.21848","title":"Keyless Attention: Value-Space Routing and Value-Only Caching for Efficient Transformers","created":"2026-06-20","cited_by_count":0,"area":"inference"},{"id":"2607.19368","title":"Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing","created":"2026-06-12","cited_by_count":0,"area":"inference"}],"next_cursor":null}