arXiv Machine Learning
Retrieval Capacity of Self-Attention Under Competition
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arXiv AI
Sep 3
Language Models Can Control Their Own Attention
The paper introduces Declarative Attention (DA), a protocol that lets language models explicitly declare which parts of their context to focus on during generation. By partitioning decoding into full-context, region-specific, and recent-output-only modes, the inference engine can skip large portions of the KV cache, dramatically reducing attended tokens. Experiments on 15 long-context tasks with off-the-shelf models show significant savings (52.0% and 31.1% reductions) with only modest accuracy drops that diminish as model size increases.
arXiv Machine Learning
Aug 4
Geometric Analysis of Token Selection in Multi-Head Attention
arXiv:2602. 01893v2 Announce Type: replace-cross Abstract: We present a geometric framework for analysing multi-head attention in large language models (LLMs).
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 Computation and Language
Sep 14
SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking
arXiv:2609.13141v1 Announce Type: new Abstract: Post-training attention sparsification reduces the quadratic cumulative attention cost of pretrained Transformers by selecting a small set of context u...
arXiv AI
Aug 10
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin
arXiv:2608. 06411v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) achieve strong performance across diverse vision-language tasks, but their efficiency is limited by the cost of processing numerous visual tokens.
arXiv AI
Jun 9
Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps
arXiv:2605. 16928v2 Announce Type: replace-cross Abstract: Long-context inference in large language models is bottlenecked by the quadratic cost of full attention.