{"total":36,"rows":[{"id":"2310.08461","title":"DistillSpec: Improving Speculative Decoding via Knowledge Distillation","created":"2023-10-12","cited_by_count":165,"area":"inference"},{"id":"2403.05527","title":"GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM","created":"2024-03-08","cited_by_count":158,"area":"inference"},{"id":"2402.13516","title":"ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models","created":"2024-02-21","cited_by_count":45,"area":"inference"},{"id":"2504.06261","title":"Hogwild! Inference: Parallel LLM Generation via Concurrent Attention","created":"2025-04-08","cited_by_count":32,"area":"inference"},{"id":"2505.10938","title":"Accurate KV Cache Quantization with Outlier Tokens Tracing","created":"2025-05-16","cited_by_count":18,"area":"inference"},{"id":"2412.17483","title":"A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression","created":"2024-12-23","cited_by_count":17,"area":"inference"},{"id":"2504.20039","title":"AutoJudge: Judge Decoding Without Manual Annotation","created":"2025-04-28","cited_by_count":13,"area":"inference"},{"id":"2509.02718","title":"Efficient Training-Free Online Routing for High-Volume Multi-LLM Serving","created":"2025-09-02","cited_by_count":13,"area":"inference"},{"id":"2507.01438","title":"EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices","created":"2025-07-02","cited_by_count":11,"area":"inference"},{"id":"2510.05373","title":"KVLinC : KV Cache Quantization with Hadamard Rotation and Linear Correction","created":"2025-10-06","cited_by_count":10,"area":"inference"},{"id":"2406.07368","title":"When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models","created":"2024-06-11","cited_by_count":9,"area":"inference"},{"id":"2410.01485","title":"A Little Goes a Long Way: Efficient Long Context Training and Inference with Partial Contexts","created":"2024-10-02","cited_by_count":9,"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":"2512.04550","title":"AdmTree: Compressing Lengthy Context with Adaptive Semantic Trees","created":"2025-12-04","cited_by_count":8,"area":"inference"},{"id":"2507.11942","title":"DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression","created":"2025-07-16","cited_by_count":7,"area":"inference"},{"id":"2602.05992","title":"DSB: Dynamic Sliding Block Scheduling for Diffusion LLMs","created":"2026-02-05","cited_by_count":7,"area":"inference"},{"id":"2504.11004","title":"Dynamic Compressing Prompts for Efficient Inference of Large Language Models","created":"2025-04-15","cited_by_count":6,"area":"inference"},{"id":"2508.02053","title":"ProCut: LLM Prompt Compression via Attribution Estimation","created":"2025-08-04","cited_by_count":6,"area":"inference"},{"id":"2508.02751","title":"SmallKV: Small Model Assisted Compensation of KV Cache Compression for Efficient LLM Inference","created":"2025-08-03","cited_by_count":6,"area":"inference"},{"id":"2602.21233","title":"AngelSlim: A more accessible, comprehensive, and efficient toolkit for large model compression","created":"2026-02-07","cited_by_count":6,"area":"inference"},{"id":"2510.05090","title":"Finish First, Perfect Later: Test-Time Token-Level Cross-Validation for Diffusion Large Language Models","created":"2025-10-06","cited_by_count":5,"area":"inference"},{"id":"2509.10798","title":"Judge Q: Trainable Queries for Optimized Information Retention in KV Cache Eviction","created":"2025-09-13","cited_by_count":4,"area":"inference"},{"id":"2510.13602","title":"NOSA: Native and Offloadable Sparse Attention","created":"2025-10-15","cited_by_count":4,"area":"inference"},{"id":"2506.17286","title":"GTA: Grouped-head latenT Attention","created":"2025-06-15","cited_by_count":3,"area":"inference"},{"id":"2506.18728","title":"PARALLELPROMPT: Extracting Parallelism from Large Language Model Queries","created":"2025-06-23","cited_by_count":3,"area":"inference"},{"id":"2606.06467","title":"You Only Index Once: Cross-Layer Sparse Attention with Shared Routing","created":"2026-06-04","cited_by_count":2,"area":"inference"},{"id":"2609.17184","title":"LoopSpec: Pipelined Self-Speculative Decoding for Looped Transformers","created":"2026-09-15","cited_by_count":2,"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":"2508.12407","title":"ZigzagAttention: Efficient Long-Context Inference with Exclusive Retrieval and Streaming Heads","created":"2025-08-17","cited_by_count":1,"area":"inference"},{"id":"2609.26368","title":"HySparse2: Hybrid Sparse Attention with Two-Level KV Sharing","created":"2026-09-22","cited_by_count":1,"area":"inference"},{"id":"2604.07023","title":"MARS: Enabling Autoregressive Models Multi-Token Generation","created":"2026-04-08","cited_by_count":0,"area":"inference"},{"id":"2604.26951","title":"Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models","created":"2026-04-29","cited_by_count":0,"area":"inference"},{"id":"2608.03839","title":"Oilbird: Training-Free Speculative Decoding with Keys the Verifier Already Computes","created":"2026-08-04","cited_by_count":0,"area":"inference"},{"id":"2609.37062","title":"vSkipper: Translating Dynamic Layer Skipping into LLM Serving Gains","created":"2026-09-29","cited_by_count":0,"area":"inference"},{"id":"2610.03027","title":"Tailoring the Quantization Space for 1-Bit KV Cache Compression","created":"2026-10-02","cited_by_count":0,"area":"inference"},{"id":"2610.09013","title":"Enabling Dynamic Computation in Looped LMs","created":"2026-10-06","cited_by_count":0,"area":"inference"}],"next_cursor":null}