LASER: An Efficient Target-Aware Segmented Attention Framework for End-to-End Long Sequence Modeling

• URL: http://arxiv.org/abs/2602.11562v1
• Date: Thu, 12 Feb 2026 04:33:37 GMT
• Title: LASER: An Efficient Target-Aware Segmented Attention Framework for End-to-End Long Sequence Modeling
• Authors: Tianhe Lin, Ziwei Xiong, Baoyuan Ou, Yingjie Qin, Lai Xu, Xiaocheng Zhong, Yao Hu, Zhiyong Wang, Tao Zhou, Yubin Xu, Di Wu,
• Abstract summary: We present LASER, a full-stack optimization framework developed and deployed at Xiaohongshu (RedNote)<n>System efficiency: We introduce SeqVault, a unified schema-aware serving infrastructure for long user histories.<n>Algorithmic efficiency: We propose a Segmented Target Attention (STA) mechanism to address the computational overhead.
• Score: 20.507605423606282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling ultra-long user behavior sequences is pivotal for capturing evolving and lifelong interests in modern recommendation systems. However, deploying such models in real-time industrial environments faces a strict "Latency Wall", constrained by two distinct bottlenecks: the high I/O latency of retrieving massive user histories and the quadratic computational complexity of standard attention mechanisms. To break these bottlenecks, we present LASER, a full-stack optimization framework developed and deployed at Xiaohongshu (RedNote). Our approach tackles the challenges through two complementary innovations: (1) System efficiency: We introduce SeqVault, a unified schema-aware serving infrastructure for long user histories. By implementing a hybrid DRAM-SSD indexing strategy, SeqVault reduces retrieval latency by 50% and CPU usage by 75%, ensuring millisecond-level access to full real-time and life-cycle user histories. (2) Algorithmic efficiency: We propose a Segmented Target Attention (STA) mechanism to address the computational overhead. Motivated by the inherent sparsity of user interests, STA employs a sigmoid-based gating strategy that acts as a silence mechanism to filter out noisy items. Subsequently, a lightweight Global Stacked Target Attention (GSTA) module refines these compressed segments to capture cross-segment dependencies without incurring high computational costs. This design performs effective sequence compression, reducing the complexity of long-sequence modeling while preserving critical signals. Extensive offline evaluations demonstrate that LASER consistently outperforms state-of-the-art baselines. In large-scale online A/B testing serving over 100 million daily active users, LASER achieved a 2.36% lift in ADVV and a 2.08% lift in revenue, demonstrating its scalability and significant commercial impact.

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