arXiv:2608.24079v1 Announce Type: cross
Abstract: A shared search-and-recommendation index must score new items from features alone because search has no exploration slot. In a public log covering bo...
By Theodore Rogers, Joe Standerfer, Dmitrii Timoshenko, Haoxue Li, Zuhaib Akhtar, Soyoung Yang
arXiv:2607. 27692v1 Announce Type: cross Abstract: Top-$K$ sparse attention reduces the cost of Softmax and value aggregation by attending to only a small subset of key--value (KV) entries.
By Wenshuai Yao, Wenyong Zhou, Hanyong Shao, Yizhe Chen, Zhiyuan Ning, Yuannuo Feng, Ru Huang, Kechao Tang
arXiv:2609.37345v1 Announce Type: new
Abstract: Streaming video understanding requires Video Large Language Models (Video-LLMs) to reason over continuous visual streams under causal constraints. As t...
By Xiang Hu, Jiazuo Yu, Lu Zhang, Yunzhi Zhuge, Huchuan Lu
arXiv:2607. 27475v1 Announce Type: cross Abstract: In modern recommendation systems, retrieval serves as a primary stage responsible for filtering billions of candidate items down to thousands prior to refined ranking.
By Ziwei Li, Shuyao Li, Xufeng Cai, Xue Zou, Yiming Ma, Huiting Lu, Wujie Yan, Zhichen Zhao, Yang Lu, Zhe Wang, Rui Luo, Zhengyu Su, Dan Zhang, Ji Liu
The paper examines LLM-based recommendation rerankers that are often evaluated under an oracle protocol, which guarantees the ground-truth item is present in the scored set. Across Amazon datasets, this protocol overestimates realistic NDCG@10 by 92–95% because realistic retrieval only covers 2–19% of relevant items at K=100, creating a recall ceiling that limits any closed-candidate reranker's top‑k NDCG. The authors find that various optimisation strategies—including prompt engineering, model scaling, sequential models, supervised neural rerankers, LoRA fine‑tuning, hybrid retrieval, score‑aware prompting, and LLM+CF fusion—do not significantly improve over a collaborative‑filtering baseline under realistic retrieval, and they propose a Recall‑Aware Evaluation Protocol (RAEP) to better assess rerankers in low‑recall regimes.
By Zhaohui Wang
The paper introduces the AutoRegressive Retriever (ARR), a multimodal retrieval model that iteratively refines a query embedding by selecting informative items and incorporating their content. ARR alternates between retrieval and query update, using stepwise contrastive supervision and reinforcement learning to optimize item selection and final ranking. Experiments show ARR outperforms baselines on in‑domain and zero‑shot benchmarks, with feedback improving both inference performance and initial query embeddings.
By Jianfei Zhao, Yifan Wang, Feng Zhang, Xin Sun, Chong Feng, Zhixing Tan, Yang Luo, Boyuan Pan, Xu Kai, Yao Hu