Capturing user interests across extensive watch histories is critical for short-form video recommendation, yet scaling sequence length is limited by two bottlenecks: the semantic sparsity of atomic Video IDs and the quadratic computational complexity of Transformers. Traditional orthogonal Video IDs fail to capture content relationships and demand large embedding tables, while the quadratic complexity of self-attention restricts the maximum sequence length under strict industrial latency and resource constraints. In this work, we present a production-deployed framework for modeling ultra-long user behavior sequences at a billion-user scale. We first address the representation bottleneck by adopting content-native Semantic IDs. By utilizing depth-truncated, coarse-grained Semantic IDs, we shrink the embedding table size from corpus cardinality. This compact representation naturally generalizes to cold-start content through shared semantic prefixes. Second, to overcome the sequence scaling barrier, we introduce a Global-Aware Compression Transformer that leverages non-parametric temporal folding and unified global query integration to effectively condense the sequence, alleviating both the memory and computational bottlenecks of standard self-attention. Offline profiling on our computing infrastructure demonstrates an order-of-magnitude reduction in peak memory footprint and a drastic decrease in computational overhead. This efficiency gain enables supporting longer sequence lengths at an affordable cost in production, yielding substantial online gains in satisfied user engagement and satisfied content consumption in large-scale online A/B tests.

Text-to-Image diffusion models have shown remarkable capabilities in generating high-quality images. However, current models often struggle to adhere to the complete set of conditions specified in the input text and return unfaithful generations. Existing works address this problem by either fine-tuning the base model or modifying the latent representations during the inference stage with gradient-based updates. Not only are these approaches computationally expensive, but also they usually only improve limited kinds of errors (e.g., the count of objects). In this work, we propose an intervention-based mechanism to enhance the faithfulness of diffusion models by controlling the denoising process. Starting with layout-conditional diffusion models, our approach first detects incorrectly-generated/missing objects during denoising steps. Next, a layout is constructed from the erroneous objects (feedback). Finally, we return to an earlier denoising step. The new layout is fed to the diffusion model to obtain its latent representation. Correction is applied by composing the new latents with the original ones and continuing the generation process, thereby driving the generation away from erroneous directions. As additional feedback and correction strategy, we also explore retrieval-augmented generation to help the model recover missing objects. We conduct experiments on VPEval and HRS-Bench datasets and measure faithfulness across four dimensions; presence of objects, object counts, scale of objects and spatial relations between objects. Compared to GLIGEN, the state-of-the-art model on the VPEval dataset, our approach significantly improves on all metrics (+6.7% average accuracy increase). On HRS-Bench dataset, it also outperforms existing models in count and scale metrics.

Learning from multimodal data has become a popular research topic in recent years. Multimodal coreference resolution (MCR) is an important task in this area. MCR involves resolving the references across different modalities, e.g., text and images, which is a crucial capability for building next-generation conversational agents. MCR is challenging as it requires encoding information from different modalities and modeling associations between them. Although significant progress has been made for visual-linguistic tasks such as visual grounding, most of the current works involve single turn utterances and focus on simple coreference resolutions. In this work, we propose an MCR model that resolves coreferences made in multi-turn dialogues with scene images. We present GRAVL-BERT, a unified MCR framework which combines visual relationships between objects, background scenes, dialogue, and metadata by integrating Graph Neural Networks with VL-BERT. We present results on the SIMMC 2.0 multimodal conversational dataset, achieving the rank-1 on the DSTC-10 SIMMC 2.0 MCR challenge with F1 score 0.783. Our code is available at https://github.com/alexa/gravl-bert.

Imaging anisotropy poses a critical challenge in applying deep learning models to 3D medical image analysis. Anisotropy downgrades model performance, especially when slice spacing varies significantly between training and clinical datasets. We propose a transformer-based model to tackle the anisotropy problem. It is adaptable to different levels of anisotropy and is computationally efficient. Our model outperforms baseline models in 3D lung cancer segmentation experiments.