{"total":61,"rows":[{"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":"2407.11062","title":"EfficientQAT: Efficient Quantization-Aware Training for Large Language Models","created":"2024-07-10","cited_by_count":127,"area":null},{"id":"2407.12391","title":"LLM Inference Serving: Survey of Recent Advances and Opportunities","created":"2024-07-17","cited_by_count":46,"area":null},{"id":"2504.09629","title":"Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization","created":"2025-04-13","cited_by_count":41,"area":null},{"id":"2510.12633","title":"Laminar: A Scalable Asynchronous RL Post-Training Framework","created":"2025-10-14","cited_by_count":37,"area":null},{"id":"2503.08311","title":"Mind the Memory Gap: Unveiling GPU Bottlenecks in Large-Batch LLM Inference","created":"2025-03-11","cited_by_count":33,"area":null},{"id":"2406.05981","title":"ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization","created":"2024-06-10","cited_by_count":30,"area":null},{"id":"2510.24824","title":"Parallel Loop Transformer for Efficient Test-Time Computation Scaling","created":"2025-10-28","cited_by_count":24,"area":null},{"id":"2410.02950","title":"LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences","created":"2024-10-03","cited_by_count":19,"area":null},{"id":"2510.25602","title":"INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats","created":"2025-10-29","cited_by_count":18,"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":"2505.14302","title":"Scaling Law for Quantization-Aware Training","created":"2025-05-20","cited_by_count":17,"area":null},{"id":"2405.18295","title":"Intent3D: 3D Object Detection in RGB-D Scans Based on Human Intention","created":"2024-05-28","cited_by_count":16,"area":null},{"id":"2502.18755","title":"M-ANT: Efficient Low-bit Group Quantization for LLMs via Mathematically Adaptive Numerical Type","created":"2025-02-26","cited_by_count":16,"area":null},{"id":"2405.00314","title":"Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey","created":"2024-05-01","cited_by_count":13,"area":null},{"id":"2410.05265","title":"PrefixQuant: Eliminating Outliers by Prefixed Tokens for Large Language Models Quantization","created":"2024-10-07","cited_by_count":12,"area":null},{"id":"2502.13178","title":"Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis","created":"2025-02-18","cited_by_count":12,"area":null},{"id":"2409.12963","title":"Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner","created":"2024-09-19","cited_by_count":11,"area":null},{"id":"2509.09679","title":"ButterflyQuant: Ultra-low-bit LLM Quantization through Learnable Orthogonal Butterfly Transforms","created":"2025-09-11","cited_by_count":10,"area":null},{"id":"2407.14645","title":"Performance Modeling and Workload Analysis of Distributed Large Language Model Training and Inference","created":"2024-07-19","cited_by_count":9,"area":null},{"id":"2411.01236","title":"AutoPT: How Far Are We from the End2End Automated Web Penetration Testing?","created":"2024-11-02","cited_by_count":8,"area":null},{"id":"2504.11750","title":"Characterizing and Optimizing LLM Inference Workloads on CPU-GPU Coupled Architectures","created":"2025-04-16","cited_by_count":8,"area":null},{"id":"2408.11393","title":"First Activations Matter: Training-Free Methods for Dynamic Activation in Large Language Models","created":"2024-08-21","cited_by_count":6,"area":null},{"id":"2509.13765","title":"TENET: An Efficient Sparsity-Aware LUT-Centric Architecture for Ternary LLM Inference On Edge","created":"2025-09-17","cited_by_count":6,"area":null},{"id":"2505.03031","title":"Radio: Rate-Distortion Optimization for Large Language Model Compression","created":"2025-05-05","cited_by_count":5,"area":null},{"id":"2506.06579","title":"Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques","created":"2025-06-06","cited_by_count":5,"area":null},{"id":"2412.14170","title":"E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling","created":"2024-12-18","cited_by_count":4,"area":null},{"id":"2502.18890","title":"TokenSwift: Lossless Acceleration of Ultra Long Sequence Generation","created":"2025-02-26","cited_by_count":4,"area":null},{"id":"2511.16682","title":"Bench360: Benchmarking Local LLM Inference from 360 Degrees","created":"2025-11-12","cited_by_count":4,"area":null},{"id":"2502.15443","title":"When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models","created":"2025-02-21","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":"2503.00461","title":"Leveraging Compute-in-Memory for Efficient Generative Model Inference in TPUs","created":"2025-03-01","cited_by_count":3,"area":null},{"id":"2503.08857","title":"Interpretable and Robust Dialogue State Tracking via Natural Language Summarization with LLMs","created":"2025-03-11","cited_by_count":3,"area":null},{"id":"2510.19389","title":"ARA: Adaptive Rank Allocation for Efficient Large Language Model SVD Compression","created":"2025-10-22","cited_by_count":3,"area":null},{"id":"2603.28845","title":"OneComp: One-Line Revolution for Generative AI Model Compression","created":"2026-03-30","cited_by_count":3,"area":null},{"id":"2604.03298","title":"ENEC: A Lossless AI Model Compression Method Enabling Fast Inference on Ascend NPUs","created":"2026-03-28","cited_by_count":3,"area":null},{"id":"2605.08044","title":"Fast Byte Latent Transformer","created":"2026-05-08","cited_by_count":3,"area":null},{"id":"2409.02026","title":"Foundations of Large Language Model Compression -- Part 1: Weight Quantization","created":"2024-09-03","cited_by_count":2,"area":null},{"id":"2509.26534","title":"Rearchitecting Datacenter Lifecycle for AI: A TCO-Driven Framework","created":"2025-09-30","cited_by_count":2,"area":null},{"id":"2605.04738","title":"OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization","created":"2026-05-06","cited_by_count":2,"area":null},{"id":"2606.00288","title":"Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture","created":"2026-05-29","cited_by_count":2,"area":null},{"id":"2607.16183","title":"A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing","created":"2026-07-17","cited_by_count":2,"area":null},{"id":"2410.14268","title":"MoDification: Mixture of Depths Made Easy","created":"2024-10-18","cited_by_count":1,"area":null},{"id":"2508.19087","title":"APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration","created":"2025-08-26","cited_by_count":1,"area":null},{"id":"2512.13494","title":"SkipCat: Rank-Maximized Low-Rank Compression of Large Language Models via Shared Projection and Block Skipping","created":"2025-12-15","cited_by_count":1,"area":null},{"id":"2512.19125","title":"SAP: Syntactic Attention Pruning for Transformer-based Language Models","created":"2025-12-22","cited_by_count":1,"area":null},{"id":"2512.20861","title":"Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs","created":"2025-12-24","cited_by_count":1,"area":null},{"id":"2603.19296","title":"TTQ: Activation-Aware Test-Time Quantization to Accelerate LLM Inference On The Fly","created":"2026-03-11","cited_by_count":1,"area":null},{"id":"2608.30505","title":"Tensor Methods for Language Models: From Token Representation to Training, Adaptation, Inference, Compression, and Interpretability","created":"2026-08-31","cited_by_count":1,"area":null},{"id":"2609.12923","title":"Dissecting GPU Utilization for LLM Inference on Nvidia Hopper","created":"2026-09-11","cited_by_count":1,"area":null},{"id":"2410.07531","title":"Reducing the Cost of Dropout in Flash-Attention by Hiding RNG with GEMM","created":"2024-10-10","cited_by_count":0,"area":null},{"id":"2411.09580","title":"Software Performance Engineering for Foundation Model-Powered Software","created":"2024-11-14","cited_by_count":0,"area":null},{"id":"2506.07695","title":"Towards a Small Language Model Lifecycle Framework","created":"2025-06-09","cited_by_count":0,"area":null},{"id":"2512.14090","title":"Arithmetic-Intensity-Aware Quantization","created":"2025-12-16","cited_by_count":0,"area":null},{"id":"2601.16615","title":"AuroraEdge-V-2B: A Faster And Stronger Edge Visual Large Language Model","created":"2026-01-23","cited_by_count":0,"area":null},{"id":"2603.09986","title":"Quantifying Hallucinations in Language Language Models on Medical Textbooks","created":"2026-02-12","cited_by_count":0,"area":null},{"id":"2605.03109","title":"Gated Subspace Inference for Transformer Acceleration","created":"2026-05-04","cited_by_count":0,"area":null},{"id":"2605.30544","title":"On-Device Generative AI for GDPR-Compliant Visual Monitoring: Natural Language Alerts from Local Object Detection","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2606.08565","title":"EinSort: Sorting is All We Need for Tensorizing LLM","created":"2026-06-07","cited_by_count":0,"area":null},{"id":"2606.09879","title":"Operator Fusion for LLM Inference on the Tensix Architecture","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2607.02558","title":"MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems","created":"2026-06-28","cited_by_count":0,"area":null}],"next_cursor":null}