Industrial large-scale recommendation models (LRMs) face the challenge of jointly modeling long-range user behavior sequences and heterogeneous non-sequential features under strict efficiency constraints. However, most existing architectures employ a decoupled pipeline: long sequences are first compressed with a query-token based sequence compressor like LONGER, followed by fusion with dense features through token-mixing modules like RankMixer, which thereby limits both the representation capacity and the interaction flexibility. This paper presents HyFormer, a unified hybrid transformer architecture that tightly integrates long-sequence modeling and feature interaction into a single backbone. From the perspective of sequence modeling, we revisit and redesign query tokens in LRMs, and frame the LRM modeling task as an alternating optimization process that integrates two core components: Query Decoding which expands non-sequential features into Global Tokens and performs long sequence decoding over layer-wise key-value representations of long behavioral sequences; and Query Boosting which enhances cross-query and cross-sequence heterogeneous interactions via efficient token mixing. The two complementary mechanisms are performed iteratively to refine semantic representations across layers. Extensive experiments on billion-scale industrial datasets demonstrate that HyFormer consistently outperforms strong LONGER and RankMixer baselines under comparable parameter and FLOPs budgets, while exhibiting superior scaling behavior with increasing parameters and FLOPs. Large-scale online A/B tests in high-traffic production systems further validate its effectiveness, showing significant gains over deployed state-of-the-art models. These results highlight the practicality and scalability of HyFormer as a unified modeling framework for industrial LRMs.

Current face forgery detection models struggle to generalize across diverse forgery types due to limitations in feature extraction and refinement. Drawing inspiration from cognitive psychology, we propose an end-to-end detection framework SFMM to boost generalization capabilities. Based on dual-process theory, human cognition involves two complementary learning processes. The Fast Learning process rapidly captures explicit forgery-specific features using the Spatial-Frequency Transformer. The Slow Learning process gradually formulates generalized patterns across various forgeries by continuously updating a Momentum Memory Bank with the extracted features. This generalized knowledge is then employed to refine the feature extraction process. The two reciprocal processes are jointly optimized to foster a comprehensive and robust face forgery detection capability. Extensive experiments on benchmark datasets demonstrate that SFMM surpasses state-of-the-art methods in generalization tasks, achieving 1.0% and 3.7% AUC improvement on GAN-based and diffusion-based forgery datasets respectively.