{"total":23,"rows":[{"id":"2410.13835","title":"Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs","created":"2024-10-17","cited_by_count":56,"area":null},{"id":"2504.02441","title":"Cognitive Memory in Large Language Models","created":"2025-04-03","cited_by_count":29,"area":null},{"id":"2504.17768","title":"The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs","created":"2025-04-24","cited_by_count":29,"area":null},{"id":"2502.00919","title":"Attention Sinks: A 'Catch, Tag, Release' Mechanism for Embeddings","created":"2025-02-02","cited_by_count":16,"area":null},{"id":"2411.13476","title":"When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training","created":"2024-11-20","cited_by_count":15,"area":null},{"id":"2407.15176","title":"ReAttention: Training-Free Infinite Context with Finite Attention Scope","created":"2024-07-21","cited_by_count":13,"area":null},{"id":"2504.03889","title":"Identifying and Evaluating Inactive Heads in Pretrained LLMs","created":"2025-04-04","cited_by_count":8,"area":null},{"id":"2505.16175","title":"QuickVideo: Real-Time Long Video Understanding with System Algorithm Co-Design","created":"2025-05-22","cited_by_count":8,"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":"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":"2507.22798","title":"Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models","created":"2025-07-30","cited_by_count":3,"area":null},{"id":"2410.12850","title":"RecurFormer: Not All Transformer Heads Need Self-Attention","created":"2024-10-10","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":"2603.06248","title":"Gradient Flow Polarizes Softmax Outputs towards Low-Entropy Solutions","created":"2026-03-06","cited_by_count":2,"area":null},{"id":"2604.01178","title":"Screening Is Enough","created":"2026-04-01","cited_by_count":1,"area":null},{"id":"2605.09877","title":"Key-Value Means: Transformers with Expandable Block-Recurrent Compressed Memory","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2607.24555","title":"LOCKS: Page-Local Compact Key Summaries for Efficient Long-Context Decoding","created":"2026-07-27","cited_by_count":1,"area":null},{"id":"2510.11789","title":"Minimax Rates for Learning Pairwise Interactions in Attention-Style Models","created":"2025-10-13","cited_by_count":0,"area":null},{"id":"2606.00024","title":"ART: Attention Run-time Termination for Efficient Large Language Model Decoding","created":"2026-04-15","cited_by_count":0,"area":null},{"id":"2606.07604","title":"Contribution Weights: A Geometrical Analysis of Self-Attention Transformers","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2609.26052","title":"Optimizing Denoising Trajectories in dLLMs: A Lightweight Evolutionary Heuristic Approach","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2609.35593","title":"ReSS: Residual-Restoring Sparse Attention for 3D Vision Transformers","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.13337","title":"Exploring Attention Sinks in Text-to-Image Transformers from Diffusion and Autoregressive Models","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}