{"total":146,"rows":[{"id":"2407.02490","title":"MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention","created":"2024-07-02","cited_by_count":383,"area":null},{"id":"2312.12456","title":"PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU","created":"2023-12-16","cited_by_count":217,"area":null},{"id":"2312.03863","title":"Efficient Large Language Models: A Survey","created":"2023-12-06","cited_by_count":186,"area":null},{"id":"2406.19384","title":"The Remarkable Robustness of LLMs: Stages of Inference?","created":"2024-06-27","cited_by_count":146,"area":null},{"id":"2507.10524","title":"Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation","created":"2025-07-14","cited_by_count":133,"area":null},{"id":"2407.18921","title":"Mobile Edge Intelligence for Large Language Models: A Contemporary Survey","created":"2024-07-09","cited_by_count":128,"area":null},{"id":"2409.00088","title":"On-Device Language Models: A Comprehensive Review","created":"2024-08-26","cited_by_count":104,"area":null},{"id":"2402.09025","title":"SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks","created":"2024-02-14","cited_by_count":97,"area":null},{"id":"2401.08092","title":"A Survey of Resource-efficient LLM and Multimodal Foundation Models","created":"2024-01-16","cited_by_count":86,"area":null},{"id":"2310.18547","title":"Punica: Multi-Tenant LoRA Serving","created":"2023-10-28","cited_by_count":83,"area":null},{"id":"2410.20672","title":"Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA","created":"2024-10-28","cited_by_count":78,"area":null},{"id":"2402.03804","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","created":"2024-02-06","cited_by_count":67,"area":null},{"id":"2409.17481","title":"MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models","created":"2024-09-26","cited_by_count":50,"area":null},{"id":"2505.13898","title":"Do Language Models Use Their Depth Efficiently?","created":"2025-05-20","cited_by_count":50,"area":null},{"id":"2404.08763","title":"CATS: Contextually-Aware Thresholding for Sparsity in Large Language Models","created":"2024-04-12","cited_by_count":41,"area":null},{"id":"2412.04315","title":"Densing Law of LLMs","created":"2024-12-05","cited_by_count":40,"area":null},{"id":"2403.18344","title":"LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models","created":"2024-03-27","cited_by_count":35,"area":null},{"id":"2405.10313","title":"How Far Are We From AGI: Are LLMs All We Need?","created":"2024-05-16","cited_by_count":31,"area":null},{"id":"2406.02913","title":"Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity","created":"2024-06-05","cited_by_count":28,"area":null},{"id":"2410.01518","title":"InfiniPot: Infinite Context Processing on Memory-Constrained LLMs","created":"2024-10-02","cited_by_count":27,"area":null},{"id":"2407.10969","title":"Q-Sparse: All Large Language Models can be Fully Sparsely-Activated","created":"2024-07-15","cited_by_count":25,"area":null},{"id":"2312.14950","title":"TypeFly: Flying Drones with Large Language Model","created":"2023-12-08","cited_by_count":23,"area":null},{"id":"2405.16700","title":"Implicit Multimodal Alignment: On the Generalization of Frozen LLMs to Multimodal Inputs","created":"2024-05-26","cited_by_count":23,"area":null},{"id":"2410.07265","title":"A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models","created":"2024-10-08","cited_by_count":23,"area":null},{"id":"2408.03624","title":"AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging","created":"2024-08-07","cited_by_count":22,"area":null},{"id":"2408.13233","title":"Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time","created":"2024-08-23","cited_by_count":22,"area":null},{"id":"2409.02877","title":"Configurable Foundation Models: Building LLMs from a Modular Perspective","created":"2024-09-04","cited_by_count":22,"area":null},{"id":"2411.02335","title":"Sparsing Law: Towards Large Language Models with Greater Activation Sparsity","created":"2024-11-04","cited_by_count":22,"area":null},{"id":"2402.06126","title":"Learn To be Efficient: Build Structured Sparsity in Large Language Models","created":"2024-02-09","cited_by_count":20,"area":null},{"id":"2405.20935","title":"Effective Interplay between Sparsity and Quantization: From Theory to Practice","created":"2024-05-31","cited_by_count":19,"area":null},{"id":"2408.12151","title":"A Tighter Complexity Analysis of SparseGPT","created":"2024-08-22","cited_by_count":19,"area":null},{"id":"2403.20041","title":"Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs","created":"2024-03-29","cited_by_count":17,"area":null},{"id":"2501.02086","title":"Instruction-Following Pruning for Large Language Models","created":"2025-01-03","cited_by_count":17,"area":null},{"id":"2405.15743","title":"Sparse maximal update parameterization: A holistic approach to sparse training dynamics","created":"2024-05-24","cited_by_count":16,"area":null},{"id":"2411.15707","title":"Nimbus: Secure and Efficient Two-Party Inference for Transformers","created":"2024-11-24","cited_by_count":16,"area":null},{"id":"2401.15347","title":"A Comprehensive Survey of Compression Algorithms for Language Models","created":"2024-01-27","cited_by_count":15,"area":null},{"id":"2410.18311","title":"CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation","created":"2024-10-23","cited_by_count":14,"area":null},{"id":"2502.15618","title":"Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing","created":"2025-02-21","cited_by_count":14,"area":null},{"id":"2403.12900","title":"Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference","created":"2024-03-19","cited_by_count":13,"area":null},{"id":"2407.11717","title":"Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Large Models","created":"2024-07-16","cited_by_count":13,"area":null},{"id":"2402.14833","title":"CliqueParcel: An Approach For Batching LLM Prompts That Jointly Optimizes Efficiency And Faithfulness","created":"2024-02-17","cited_by_count":12,"area":null},{"id":"2405.01814","title":"Efficient Heterogeneous Large Language Model Decoding with Model-Attention Disaggregation","created":"2024-05-03","cited_by_count":12,"area":null},{"id":"2401.02643","title":"Training and Serving System of Foundation Models: A Comprehensive Survey","created":"2024-01-05","cited_by_count":11,"area":null},{"id":"2407.11534","title":"LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices","created":"2024-07-16","cited_by_count":11,"area":null},{"id":"2409.13199","title":"CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information","created":"2024-09-20","cited_by_count":11,"area":null},{"id":"2404.09529","title":"Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models","created":"2024-04-15","cited_by_count":10,"area":null},{"id":"2405.15926","title":"Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers","created":"2024-05-24","cited_by_count":9,"area":null},{"id":"2410.07549","title":"OneNet: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model Prompting","created":"2024-10-10","cited_by_count":9,"area":null},{"id":"2506.16500","title":"SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity","created":"2025-06-19","cited_by_count":9,"area":null},{"id":"2403.11621","title":"Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model","created":"2024-03-18","cited_by_count":8,"area":null},{"id":"2404.01430","title":"Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs","created":"2024-04-01","cited_by_count":7,"area":null},{"id":"2406.06567","title":"DHA: Learning Decoupled-Head Attention from Transformer Checkpoints via Adaptive Heads Fusion","created":"2024-06-03","cited_by_count":7,"area":null},{"id":"2409.12490","title":"CritiPrefill: A Segment-wise Criticality-based Approach for Prefilling Acceleration in LLMs","created":"2024-09-19","cited_by_count":7,"area":null},{"id":"2412.20677","title":"Align Attention Heads Before Merging Them: An Effective Way for Converting MHA to GQA","created":"2024-12-30","cited_by_count":7,"area":null},{"id":"2502.00840","title":"Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense","created":"2025-02-02","cited_by_count":7,"area":null},{"id":"2509.00454","title":"Universal Properties of Activation Sparsity in Modern Large Language Models","created":"2025-08-30","cited_by_count":7,"area":null},{"id":"2311.13624","title":"A Theoretical Insight into Attack and Defense of Gradient Leakage in Transformer","created":"2023-11-22","cited_by_count":6,"area":null},{"id":"2311.14652","title":"One Pass Streaming Algorithm for Super Long Token Attention Approximation in Sublinear Space","created":"2023-11-24","cited_by_count":6,"area":null},{"id":"2404.18311","title":"Towards Incremental Learning in Large Language Models: A Critical Review","created":"2024-04-28","cited_by_count":6,"area":null},{"id":"2410.12168","title":"COMET: Towards Partical W4A4KV4 LLMs Serving","created":"2024-10-16","cited_by_count":6,"area":null},{"id":"2502.20727","title":"SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models","created":"2025-02-28","cited_by_count":6,"area":null},{"id":"2505.14884","title":"Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity","created":"2025-05-20","cited_by_count":6,"area":null},{"id":"2406.13170","title":"Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference","created":"2024-06-19","cited_by_count":5,"area":null},{"id":"2412.07174","title":"Post-Training Statistical Calibration for Higher Activation Sparsity","created":"2024-12-10","cited_by_count":5,"area":null},{"id":"2502.09647","title":"Unveiling Simplicities of Attention: Adaptive Long-Context Head Identification","created":"2025-02-11","cited_by_count":5,"area":null},{"id":"2502.13533","title":"Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models","created":"2025-02-19","cited_by_count":5,"area":null},{"id":"2507.07580","title":"COALA: Numerically Stable and Efficient Framework for Context-Aware Low-Rank Approximation","created":"2025-07-10","cited_by_count":5,"area":null},{"id":"2401.04044","title":"FFSplit: Split Feed-Forward Network For Optimizing Accuracy-Efficiency Trade-off in Language Model Inference","created":"2024-01-08","cited_by_count":4,"area":null},{"id":"2407.02328","title":"Efficient Sparse Attention needs Adaptive Token Release","created":"2024-07-02","cited_by_count":4,"area":null},{"id":"2408.17322","title":"Investigating Neuron Ablation in Attention Heads: The Case for Peak Activation Centering","created":"2024-08-30","cited_by_count":4,"area":null},{"id":"2409.03856","title":"Sirius: Contextual Sparsity with Correction for Efficient LLMs","created":"2024-09-05","cited_by_count":4,"area":null},{"id":"2503.03777","title":"FlexInfer: Breaking Memory Constraint via Flexible and Efficient Offloading for On-Device LLM Inference","created":"2025-03-04","cited_by_count":4,"area":null},{"id":"2504.08378","title":"Scaling Up On-Device LLMs via Active-Weight Swapping Between DRAM and Flash","created":"2025-04-11","cited_by_count":4,"area":null},{"id":"2505.07239","title":"Comet: Accelerating Private Inference for Large Language Model by Predicting Activation Sparsity","created":"2025-05-12","cited_by_count":4,"area":null},{"id":"2508.06041","title":"DP-LLM: Runtime Model Adaptation with Dynamic Layer-wise Precision Assignment","created":"2025-08-08","cited_by_count":4,"area":null},{"id":"2604.03258","title":"SoLA: Leveraging Soft Activation Sparsity and Low-Rank Decomposition for Large Language Model Compression","created":"2026-03-12","cited_by_count":4,"area":null},{"id":"2404.13855","title":"Understanding the role of FFNs in driving multilingual behaviour in LLMs","created":"2024-04-22","cited_by_count":3,"area":null},{"id":"2409.09071","title":"Elastic On-Device LLM Service","created":"2024-09-08","cited_by_count":3,"area":null},{"id":"2410.14740","title":"Harnessing Your DRAM and SSD for Sustainable and Accessible LLM Inference with Mixed-Precision and Multi-level Caching","created":"2024-10-17","cited_by_count":3,"area":null},{"id":"2412.11053","title":"NITRO: LLM Inference on Intel Laptop NPUs","created":"2024-12-15","cited_by_count":3,"area":null},{"id":"2502.04348","title":"Prompt-based Depth Pruning of Large Language Models","created":"2025-02-04","cited_by_count":3,"area":null},{"id":"2502.19078","title":"Sparse Brains are Also Adaptive Brains: Cognitive-Load-Aware Dynamic Activation for LLMs","created":"2025-02-26","cited_by_count":3,"area":null},{"id":"2503.18216","title":"Adaptive Rank Allocation: Speeding Up Modern Transformers with RaNA Adapters","created":"2025-03-23","cited_by_count":3,"area":null},{"id":"2506.05647","title":"Learning to Weight Parameters for Training Data Attribution","created":"2025-06-06","cited_by_count":3,"area":null},{"id":"2510.19861","title":"Some Attention is All You Need for Retrieval","created":"2025-10-21","cited_by_count":3,"area":null},{"id":"2603.18426","title":"Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression","created":"2026-03-19","cited_by_count":3,"area":null},{"id":"2405.01943","title":"Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models","created":"2024-05-03","cited_by_count":2,"area":null},{"id":"2406.09041","title":"ME-Switch: A Memory-Efficient Expert Switching Framework for Large Language Models","created":"2024-06-13","cited_by_count":2,"area":null},{"id":"2407.15516","title":"Attention Is All You Need But You Don't Need All Of It For Inference of Large Language Models","created":"2024-07-22","cited_by_count":2,"area":null},{"id":"2407.15866","title":"SmartQuant: CXL-based AI Model Store in Support of Runtime Configurable Weight Quantization","created":"2024-07-17","cited_by_count":2,"area":null},{"id":"2410.10267","title":"big.LITTLE Vision Transformer for Efficient Visual Recognition","created":"2024-10-14","cited_by_count":2,"area":null},{"id":"2412.05644","title":"Mixture of Hidden-Dimensions Transformer","created":"2024-12-07","cited_by_count":2,"area":null},{"id":"2503.13737","title":"AccelGen: Heterogeneous SLO-Guaranteed High-Throughput LLM Inference Serving for Diverse Applications","created":"2025-03-17","cited_by_count":2,"area":null},{"id":"2510.15964","title":"Long Exposure: Accelerating Parameter-Efficient Fine-Tuning for LLMs under Shadowy Sparsity","created":"2025-10-12","cited_by_count":2,"area":null},{"id":"2602.08686","title":"CompilerKV: Risk-Adaptive KV Compression via Offline Experience Compilation","created":"2026-02-09","cited_by_count":2,"area":null},{"id":"2606.29184","title":"BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning","created":"2026-06-28","cited_by_count":2,"area":null},{"id":"2404.04900","title":"Radial Networks: Dynamic Layer Routing for High-Performance Large Language Models","created":"2024-04-07","cited_by_count":1,"area":null},{"id":"2405.13216","title":"Equipping Transformer with Random-Access Reading for Long-Context Understanding","created":"2024-05-21","cited_by_count":1,"area":null},{"id":"2408.10473","title":"Enhancing One-shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism","created":"2024-08-20","cited_by_count":1,"area":null},{"id":"2412.10856","title":"RWKV-edge: Deeply Compressed RWKV for Resource-Constrained Devices","created":"2024-12-14","cited_by_count":1,"area":null}],"next_cursor":"100"}