arXiv AI
Jun 30

MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers

arXiv:2606. 29844v1 Announce Type: cross Abstract: The quadratic computational cost of traditional attention mechanisms poses a major bottleneck to the scalability and practical deployment of large language models (LLMs), particularly in long-context scenarios.

By Linrui Ma, Chun Hei Lo, Xinyu Wang, Peng Lu, Xihao Yuan, Hanting Chen, Kai Han, Xinghao Chen, Chengjun Zhan, Hanlin Xu, Yichun Yin, Lifeng Shang, Feng Wen, Boxing Chen, Yufei Cui
arXiv Machine Learning
Oct 15

Continuum Attention

arXiv:2610.20586v1 Announce Type: new Abstract: We introduce Continuum Attention (CA), a causal Transformer attention operator that replaces discrete key and value lookup with integration over a cont...

By Afshin Khadangi
arXiv AI
Sep 16

QueryFormer: Winning Solution for KDD Cup 2026 Tencent UniRec Challenge

QueryFormer is a unified architecture designed for post‑click conversion rate prediction, addressing both feature interactions and sequential user behaviors. It introduces a stackable field–sequence block that generates query tokens via cross‑attention and packs sequence queries into shared‑parameter attention, improving efficiency and accuracy. The model won first place in the KDD Cup 2026 Tencent UniRec Challenge Industrial Track with an AUC of 0.83254, and scaling studies show that increasing view width slightly boosts validation AUC while maintaining low latency.

By Yuanzhe Zhou, Zhaoyang Zeng