{"total":27,"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":"2411.07191","title":"The Super Weight in Large Language Models","created":"2024-11-11","cited_by_count":52,"area":null},{"id":"2603.05498","title":"The Spike, the Sparse and the Sink: Anatomy of Massive Activations and Attention Sinks","created":"2026-03-05","cited_by_count":27,"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":"2506.19697","title":"Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models","created":"2025-06-24","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":"2603.11487","title":"Attention Sinks Are Provably Necessary in Softmax Transformers: Evidence from Trigger-Conditional Tasks","created":"2026-03-12","cited_by_count":11,"area":null},{"id":"2410.01866","title":"House of Cards: Massive Weights in LLMs","created":"2024-10-02","cited_by_count":9,"area":null},{"id":"2501.01059","title":"Dynamic Attention-Guided Context Decoding for Mitigating Context Faithfulness Hallucinations in Large Language Models","created":"2025-01-02","cited_by_count":9,"area":null},{"id":"2510.09017","title":"Value-State Gated Attention for Mitigating Extreme-Token Phenomena in Transformers","created":"2025-10-10","cited_by_count":8,"area":null},{"id":"2506.05413","title":"SmoothRot: Combining Channel-Wise Scaling and Rotation for Quantization-Friendly LLMs","created":"2025-06-04","cited_by_count":4,"area":null},{"id":"2512.22213","title":"On the Existence and Behavior of Secondary Attention Sinks","created":"2025-12-22","cited_by_count":4,"area":null},{"id":"2508.18387","title":"Integral Transformer: Denoising Attention, Not Too Much Not Too Little","created":"2025-08-25","cited_by_count":3,"area":null},{"id":"2602.02047","title":"Dissecting Outlier Dynamics in LLM NVFP4 Pretraining","created":"2026-02-02","cited_by_count":3,"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":"2605.08504","title":"A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models","created":"2026-05-08","cited_by_count":2,"area":null},{"id":"2605.17887","title":"Attention Sinks and Outliers in Attention Residuals","created":"2026-05-18","cited_by_count":2,"area":null},{"id":"2606.02288","title":"Massive Spikes in LLMs are Bias Vectors: Mechanistic Uncovering and Spike-Free Quantization","created":"2026-06-01","cited_by_count":2,"area":null},{"id":"2504.21553","title":"Precision Where It Matters: A Novel Spike Aware Mixed-Precision Quantization Strategy for LLaMA-based Language Models","created":"2025-04-30","cited_by_count":1,"area":null},{"id":"2605.06611","title":"The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2407.15508","title":"Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners","created":"2024-07-22","cited_by_count":0,"area":null},{"id":"2504.13989","title":"Gradual Binary Search and Dimension Expansion : A general method for activation quantization in LLMs","created":"2025-04-18","cited_by_count":0,"area":null},{"id":"2602.02126","title":"Two-Stage Grid Optimization for Group-wise Quantization of LLMs","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2602.07465","title":"On the Importance of a Multi-Scale Calibration for Quantization","created":"2026-02-07","cited_by_count":0,"area":null},{"id":"2606.16112","title":"Scaling Adaptive Depth with Norm-Agnostic Residual Networks","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2607.08321","title":"Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2609.23585","title":"Global Ranks Survive, Selected Heads Shift: BOS-Sink Topology under 4-bit Weight-Only Quantization","created":"2026-09-20","cited_by_count":0,"area":null}],"next_cursor":null}