SARM: LLM-Augmented Semantic Anchor for End-to-End Live-Streaming Ranking

• URL: http://arxiv.org/abs/2602.09401v1
• Date: Tue, 10 Feb 2026 04:15:53 GMT
• Title: SARM: LLM-Augmented Semantic Anchor for End-to-End Live-Streaming Ranking
• Authors: Ruochen Yang, Yueyang Liu, Zijie Zhuang, Changxin Lao, Yuhui Zhang, Jiangxia Cao, Jia Xu, Xiang Chen, Haoke Xiao, Xiangyu Wu, Xiaoyou Zhou, Xiao Lv, Shuang Yang, Tingwen Liu, Zhaojie Liu, Han Li, Kun Gai,
• Abstract summary: Large-scale live-streaming recommendation requires precise modeling of non-stationary content semantics under real-time serving constraints.<n>We propose textbfSARM, an end-to-end ranking architecture that integrates natural-language semantic anchors directly into ranking optimization.<n>SARM is fully deployed and serves over 400 million users daily.
• Score: 49.109782956280064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale live-streaming recommendation requires precise modeling of non-stationary content semantics under strict real-time serving constraints. In industrial deployment, two common approaches exhibit fundamental limitations: discrete semantic abstractions sacrifice descriptive precision through clustering, while dense multimodal embeddings are extracted independently and remain weakly aligned with ranking optimization, limiting fine-grained content-aware ranking. To address these limitations, we propose \textbf{SARM}, an end-to-end ranking architecture that integrates natural-language semantic anchors directly into ranking optimization, enabling fine-grained author representations conditioned on multimodal content. Each semantic anchor is represented as learnable text tokens jointly optimized with ranking features, allowing the model to adapt content descriptions to ranking objectives. A lightweight dual-token gated design captures domain-specific live-streaming semantics, while an asymmetric deployment strategy preserves low-latency online training and serving. Extensive offline evaluation and large-scale A/B tests show consistent improvements over production baselines. SARM is fully deployed and serves over 400 million users daily.

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