arXiv AI
Jun 8
Limitations of Normalization in Attention Mechanism
arXiv:2508. 17821v3 Announce Type: replace-cross Abstract: This paper investigates the limitations of the normalization in attention mechanisms.
Variational-Ising-Attention (VIA):TailoredAttentionMattersfor Science
arXiv:2607. 23634v1 Announce Type: cross Abstract: Attention enables context modeling via query-key scoring with softmax normalization.
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arXiv AI
Jul 28
What Softmax Throws Away: Mass-Aware Attention for Evidence Accumulation
arXiv:2607. 22781v1 Announce Type: cross Abstract: High task performance does not show whether a model retains prediction-relevant structural information in its internal representation.
arXiv Machine Learning
Jun 17
Softmax as Linear Attention in the Large-Prompt Regime: a Measure-based Perspective
arXiv:2512. 11784v2 Announce Type: replace Abstract: Softmax attention is a central component of transformer architectures, yet its nonlinear structure poses significant challenges for theoretical analysis.
arXiv Machine Learning
Jun 3
Neural Attention Search Linear: Towards Adaptive Token-Level Hybrid Attention Models
arXiv:2602. 03681v2 Announce Type: replace-cross Abstract: The quadratic computational complexity of softmax transformers has become a bottleneck in long-context scenarios.
arXiv Machine Learning
Jun 9
Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization
arXiv:2510. 13554v2 Announce Type: replace-cross Abstract: The reasoning pattern of Large language models (LLMs) remains opaque, and reinforcement learning (RL) typically applies uniform credit across an entire generation, blurring the distinction between pivotal and routine steps.
arXiv Machine Learning
Aug 11
Training-Free Universal Approximation by Prompting Random Transformers
arXiv:2608. 09558v1 Announce Type: new Abstract: How expressive is prompting a transformer?
arXiv Machine Learning
Jul 9
Nectar: Neural Estimation of Cached-Token Attention via Regression
arXiv:2605. 09778v2 Announce Type: replace Abstract: Evaluating softmax attention over a fixed long context requires reading every cached key-value pair for each new query token.
arXiv Machine Learning
Jun 2
Don't Read Everything: A Curvature-Conditioned Query for Linear Attention
arXiv:2606. 01294v1 Announce Type: cross Abstract: Linear attention reduces the quadratic cost of softmax attention by maintaining a recurrent fast-weight state, but it consistently lags on in-context retrieval and long-context tasks.
arXiv Machine Learning
Jul 2
Ghost in the Kernel: In-Context Learning with Efficient Transformers via Domain Generalization
arXiv:2607. 00479v1 Announce Type: new Abstract: Transformer-based large models have demonstrated remarkable generalization abilities across different tasks by leveraging a context-aware attention module for in-context learning.
arXiv Machine Learning
Jun 24
Asymptotic Signal Subspace Recovery in Softmax Attention Models
arXiv:2606. 22406v2 Announce Type: replace Abstract: Attention mechanisms have demonstrated remarkable empirical success in identifying relevant information from large collections of tokens, yet the theoretical principles underlying this behavior remain poorly understood.
arXiv Machine Learning
Aug 10
Faster Query-Key Learning Sharpens Attention in Self-Attention Models
arXiv:2608. 06776v1 Announce Type: new Abstract: A standard self-attention layer consists of two interacting circuits: the query-key circuit that governs attention allocation, and the output-value circuit that maps attended representations to predictions.
arXiv Machine Learning
Jul 10
Threshold Differential Attention for Sink-Free, Ultra-Sparse, and Non-Dispersive Language Modeling
arXiv:2601. 12145v3 Announce Type: replace Abstract: Softmax attention struggles with long contexts due to structural limitations: the strict sum-to-one constraint forces attention sinks on irrelevant tokens, and probability mass disperses as sequence lengths increase.