{"total":46,"rows":[{"id":"2505.01618","title":"Don't be lazy: CompleteP enables compute-efficient deep transformers","created":"2025-05-02","cited_by_count":78,"area":null},{"id":"2604.12946","title":"Parcae: Scaling Laws For Stable Looped Language Models","created":"2026-04-14","cited_by_count":55,"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":"2512.17351","title":"Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers","created":"2025-12-19","cited_by_count":33,"area":null},{"id":"2503.01776","title":"Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation","created":"2025-03-03","cited_by_count":26,"area":null},{"id":"2601.22966","title":"A Unified View of Attention and Residual Sinks: Outlier-Driven Rescaling is Essential for Transformer Training","created":"2026-01-30","cited_by_count":23,"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":"2503.16672","title":"Accelerating Transformer Inference and Training with 2:4 Activation Sparsity","created":"2025-03-20","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":"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":"2410.01779","title":"Composing Global Solutions to Reasoning Tasks via Algebraic Objects in Neural Nets","created":"2024-10-02","cited_by_count":6,"area":null},{"id":"2506.20194","title":"DuoGPT: Training-free Dual Sparsity through Activation-aware Pruning in LLMs","created":"2025-06-25","cited_by_count":6,"area":null},{"id":"2507.01299","title":"La RoSA: Enhancing LLM Efficiency via Layerwise Rotated Sparse Activation","created":"2025-07-02","cited_by_count":6,"area":null},{"id":"2605.18797","title":"Simply Stabilizing the Loop via Fully Looped Transformer","created":"2026-05-11","cited_by_count":6,"area":null},{"id":"2410.19274","title":"Neuralink: Fast LLM Inference on Smartphones with Neuron Co-Activation Linking","created":"2024-10-25","cited_by_count":5,"area":null},{"id":"2506.06644","title":"Spark Transformer: Reactivating Sparsity in FFN and Attention","created":"2025-06-07","cited_by_count":5,"area":null},{"id":"2508.05571","title":"iFairy: the First 2-bit Complex LLM with All Parameters in $\\{\\pm1, \\pm i\\}$","created":"2025-08-07","cited_by_count":5,"area":null},{"id":"2406.06562","title":"Achieving Sparse Activation in Small Language Models","created":"2024-06-03","cited_by_count":4,"area":null},{"id":"2503.12167","title":"PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing","created":"2025-03-15","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":"2405.09274","title":"Dynamic Activation Pitfalls in LLaMA Models: An Empirical Study","created":"2024-05-15","cited_by_count":3,"area":null},{"id":"2502.01330","title":"Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity","created":"2025-02-03","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":"2605.23061","title":"Anytime Training with Schedule-Free Spectral Optimization","created":"2026-05-21","cited_by_count":3,"area":null},{"id":"2605.26647","title":"More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations","created":"2026-05-26","cited_by_count":3,"area":null},{"id":"2412.05644","title":"Mixture of Hidden-Dimensions Transformer","created":"2024-12-07","cited_by_count":2,"area":null},{"id":"2602.05779","title":"How Controlling the Variance can Improve Training Stability of Sparsely Activated DNNs and CNNs","created":"2026-02-05","cited_by_count":2,"area":null},{"id":"2412.12178","title":"Activation Sparsity Opportunities for Compressing General Large Language Models","created":"2024-12-13","cited_by_count":1,"area":null},{"id":"2505.17701","title":"COUNTDOWN: Contextually Sparse Activation Filtering Out Unnecessary Weights in Down Projection","created":"2025-05-23","cited_by_count":1,"area":null},{"id":"2603.12272","title":"ActTail: Global Activation Sparsity in Large Language Models","created":"2026-02-18","cited_by_count":1,"area":null},{"id":"2605.17659","title":"Bug or Feature$^2$: Weight Drift, Activation Sparsity and Spikes","created":"2026-05-17","cited_by_count":1,"area":null},{"id":"2410.08791","title":"Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models","created":"2024-10-11","cited_by_count":0,"area":null},{"id":"2506.04165","title":"A Faster Generalized Two-Stage Approximate Top-K","created":"2025-06-04","cited_by_count":0,"area":null},{"id":"2512.12744","title":"Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity","created":"2025-12-14","cited_by_count":0,"area":null},{"id":"2603.14110","title":"SVD Contextual Sparsity Predictors for Fast LLM Inference","created":"2026-03-14","cited_by_count":0,"area":null},{"id":"2604.24169","title":"PointTransformerX: Portable and Efficient 3D Point Cloud Processing without Sparse Algorithms","created":"2026-04-27","cited_by_count":0,"area":null},{"id":"2604.25150","title":"The Role of Symmetry in Optimizing Overparameterized Networks","created":"2026-04-28","cited_by_count":0,"area":null},{"id":"2607.00158","title":"Readable but Not Controllable: Neuron-Level Evidence for Medical LLM Hallucination","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2607.08991","title":"Sensitivity-Aware Thresholding and Token Routing for Activation Sparsification in Large Language Models","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2607.27591","title":"Prox: Training-Free FFN Activation Sparsity via Approximate Intermediate-Channel Salience in LLMs","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2608.25168","title":"See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detection","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2609.31975","title":"Model Casting and Low-Parameter Gating: Towards More Sparsely Activated FFNs","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2610.10303","title":"On the Necessity of Attention-FFN Split in Vision Transformers","created":"2026-10-07","cited_by_count":0,"area":null},{"id":"2610.19552","title":"Test-Time Forecasting: Recurrent Depth Enables In-Context Dynamics Prediction","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}