SeqLLM已在微信支付上线，日筛数百万商户，把筛查精度从92.0%拉到97.5%，还保住了语言能力。
SeqLLM将行为序列建模接入预训练大语言模型，同时保留原有语言能力。它由行为事件离散词表、两阶段对齐的轻量投影器、前缀引导能力注入三部分组成。该系统已在微信支付上线，每天筛查数百万商户，将DeepSeek生产基线的筛查精度从92.0%提升到97.5%。在MovieLens和Amazon上，SeqLLM的Recall@5相对User-LLM最高提升32%，且语言能力明显更强。在RecIF上，它仅用OneRec-8B五分之一GPU日，就把Pass@32提升了14.2%。
SeqLLM: Augmenting LLMs with Behavioral-Sequence Modeling for High-Stakes Decisions at WeChat Pay
Merchant risk control at large payment platforms screens tens of millions of merchants daily, where false positives harm legitimate merchants and false negatives leave harmful activity undetected. The hardest cases require jointly understanding a merchant's textual profile and long behavioral sequence. Large language models (LLMs) excel at text but cannot natively model such sequences, while adapting them often causes catastrophic forgetting. We present SeqLLM, a framework that adds behavioral-sequence modeling to a pretrained LLM while preserving its language ability. SeqLLM combines three components: a compact discrete vocabulary that represents behavioral events as native tokens; a lightweight projector, trained with a two-stage alignment curriculum, that grounds these tokens in the LLM's semantic space; and prefix-guided capability injection, which acquires sequence-modeling ability through task-prefixed supervised fine-tuning rather than continual pre-training. SeqLLM is deployed at WeChat Pay, screening millions of merchants daily. Against the production DeepSeek-based LLM baseline, it raises screening precision from 92.0% to 97.5%. Its pretrained behavior-token embeddings also improve Precision@Top-0.01% by 26.8 percentage points in a production fraud detector serving billion-scale transaction traffic. Beyond payments, SeqLLM achieves state-of-the-art results on public recommendation benchmarks. On MovieLens and Amazon, it surpasses the strong User-LLM baseline by up to 32% relative Recall@5 while retaining markedly stronger language ability. On RecIF, it improves Pass@32 by 14.2% over the full OneRec-8B pipeline using only one-fifth of its GPU-days.