{"total":25,"rows":[{"id":"2503.10996","title":"Taming Knowledge Conflicts in Language Models","created":"2025-03-14","cited_by_count":30,"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":"2604.10098","title":"Attention Sink in Transformers: A Survey on Utilization, Interpretation, and Mitigation","created":"2026-04-11","cited_by_count":11,"area":null},{"id":"2508.03616","title":"Hidden Dynamics of Massive Activations in Transformer Training","created":"2025-08-05","cited_by_count":10,"area":null},{"id":"2511.11663","title":"SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization","created":"2025-11-11","cited_by_count":10,"area":null},{"id":"2510.16670","title":"All You Need is One: Capsule Prompt Tuning with a Single Vector","created":"2025-10-19","cited_by_count":8,"area":null},{"id":"2510.24657","title":"Group Relative Attention Guidance for Image Editing","created":"2025-10-28","cited_by_count":8,"area":null},{"id":"2602.04735","title":"From Data to Behavior: Predicting Unintended Model Behaviors Before Training","created":"2026-02-04","cited_by_count":8,"area":null},{"id":"2505.13027","title":"Deconstructing Positional Information: From Attention Logits to Training Biases","created":"2025-05-19","cited_by_count":5,"area":null},{"id":"2511.09146","title":"DoPE: Denoising Rotary Position Embedding","created":"2025-11-12","cited_by_count":5,"area":null},{"id":"2603.03415","title":"Farther the Shift, Sparser the Representation: Analyzing OOD Mechanisms in LLMs","created":"2026-03-03","cited_by_count":4,"area":null},{"id":"2602.05258","title":"CoPE: Clipped RoPE as A Scalable Free Lunch for Long Context LLMs","created":"2026-02-05","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":"2607.07678","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","created":"2026-07-08","cited_by_count":2,"area":null},{"id":"2608.12036","title":"Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence","created":"2026-08-12","cited_by_count":2,"area":null},{"id":"2503.01832","title":"Rotary Offset Features in Large Language Models","created":"2025-03-03","cited_by_count":1,"area":null},{"id":"2509.18762","title":"When Long Helps Short: How Context Length in Supervised Fine-tuning Affects Behavior of Large Language Models","created":"2025-09-23","cited_by_count":1,"area":null},{"id":"2511.22316","title":"Outlier Smoothing with Closed-Form Rotations for W4A4 Large Language Model Quantization","created":"2025-11-27","cited_by_count":1,"area":null},{"id":"2604.01025","title":"Fast and Accurate Probing of In-Training LLMs' Downstream Performances","created":"2026-04-01","cited_by_count":1,"area":null},{"id":"2605.10886","title":"LoKA: Low-precision Kernel Applications for Recommendation Models At Scale","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2608.06564","title":"Which Decisions Low-Bit Quantization Breaks, and How to Predict Them","created":"2026-08-06","cited_by_count":1,"area":null},{"id":"2509.16629","title":"Causality-Induced Positional Encoding for Transformer-Based Representation Learning of Non-Sequential Features","created":"2025-09-20","cited_by_count":0,"area":null},{"id":"2511.21733","title":"RoSA: Enhancing Parameter-Efficient Fine-Tuning via RoPE-aware Selective Adaptation in Large Language Models","created":"2025-11-21","cited_by_count":0,"area":null},{"id":"2610.06963","title":"WavePrune: One period is often enough for RoPE","created":"2026-10-03","cited_by_count":0,"area":null},{"id":"2610.07848","title":"Dynamic Positional Attention Modulation for Parameter-Efficient Fine-Tuning of Large Language Models","created":"2026-10-06","cited_by_count":0,"area":null}],"next_cursor":null}