Real-world applications like video gaming and virtual reality often demand the ability to model 3D scenes that users can explore along custom camera trajectories. While significant progress has been made in generating 3D objects from text or images, creating long-range, 3D-consistent, explorable 3D scenes remains a complex and challenging problem. In this work, we present Voyager , a novel video diffusion framework that generates world-consistent 3D point-cloud sequences from a single image with user-defined camera path. Unlike existing approaches, Voyager achieves end-to-end scene generation and reconstruction with inherent consistency across frames, eliminating the need for 3D reconstruction pipelines (e.g., structure-from-motion or multi-view stereo). Our method integrates three key components: 1) World-Consistent Video Diffusion : A unified architecture that jointly generates aligned RGB and depth video sequences, conditioned on existing world observation to ensure global coherence 2) Long-Range World Exploration : An efficient world cache with point culling and an auto-regressive inference with smooth video sampling for iterative scene extension with context-aware consistency, and 3) Scalable Data Engine : A video reconstruction pipeline that automates camera pose estimation and metric depth prediction for arbitrary videos, enabling large-scale, diverse training data curation without manual 3D annotations. Collectively, these designs result in a clear improvement over existing methods in visual quality and geometric accuracy, with versatile applications. Code for this paper are at https://github.com/Tencent-Hunyuan/HunyuanWorld-Voyager.

Advertising platforms have evolved in estimating Lifetime Value (LTV) to better align with advertisers' true performance metric which considers cumulative sum of purchases a customer contributes over a period. Accurate LTV estimation is crucial for the precision of the advertising system and the effectiveness of advertisements. However, the sparsity of real-world LTV data presents a significant challenge to LTV predictive model(i.e., pLTV), severely limiting the their capabilities. Therefore, we propose to utilize external data, in addition to the internal data of advertising platform, to expand the size of purchase samples and enhance the LTV prediction model of the advertising platform. To tackle the issue of data distribution shift between internal and external platforms, we introduce an Adaptive Difference Siamese Network (ADSNet), which employs cross-domain transfer learning to prevent negative transfer. Specifically, ADSNet is designed to learn information that is beneficial to the target domain. We introduce a gain evaluation strategy to calculate information gain, aiding the model in learning helpful information for the target domain and providing the ability to reject noisy samples, thus avoiding negative transfer. Additionally, we also design a Domain Adaptation Module as a bridge to connect different domains, reduce the distribution distance between them, and enhance the consistency of representation space distribution. We conduct extensive offline experiments and online A/B tests on a real advertising platform. Our proposed ADSNet method outperforms other methods, improving GINI by 2%. The ablation study highlights the importance of the gain evaluation strategy in negative gain sample rejection and improving model performance. Additionally, ADSNet significantly improves long-tail prediction. The online A/B tests confirm ADSNet's efficacy, increasing online LTV by 3.47% and GMV by 3.89%.

Flow scheduling is crucial in data centers, as it directly influences user experience of applications. According to different assumptions and design goals, there are four typical flow scheduling problems/solutions: SRPT, LAS, Fair Queueing, and Deadline-Aware scheduling. When implementing these solutions in commodity switches with limited number of queues, they need to set static parameters by measuring traffic in advance, while optimal parameters vary across time and space. This paper proposes a generic framework, namely QCluster, to adapt all scheduling problems for limited number of queues. The key idea of QCluster is to cluster packets with similar weights/properties into the same queue. QCluster is implemented in Tofino switches, and can cluster packets at a speed of 3.2 Tbps. To the best of our knowledge, QCluster is the fastest clustering algorithm. Experimental results in testbed with programmable switches and ns-2 show that QCluster reduces the average flow completion time (FCT) for short flows up to 56.6%, and reduces the overall average FCT up to 21.7% over state-of-the-art. All the source code in ns-2 is available in Github [45].

Multi-set membership queries are fundamental operations in data science. In this paper, we propose a new data structure for multi-set membership queries, named coloring embedder, which is fast, accurate, and memory efficient. The idea of coloring embedder is to first map elements to a high-dimensional space, which nearly eliminates hashing collisions, and then use a dimensional reduction representation, similar to coloring a graph, to save memory. Theoretical proofs and experimental results show that the coloring embedder is effective in solving the problem of multi-set membership queries. We also find that web cache sharing is one of the typical application scenarios of the multi-set membership queries and current methods based on Bloom filters always send redundant queries. We apply coloring embedder to web cache sharing by arranging our data structure on the on-chip and off-chip memory and designing query, insertion and deletion operations for this scenario. The experimental results show that compared with the present method, our method can reduce the queries sent by proxies while reaching equal hit rate with the same size of on-chip memory. The source code of coloring embedder has been released on Github.

Multi-set query is a fundamental issue in data science. When the sizes of multi-sets are large, exact matching methods like hash tables need too much memory, and they cannot achieve high query speed. Bloom filters are recently used to handle big data query, but they cannot achieve high accuracy when the memory space is tight. In this paper, we propose a new data structure named coloring embedder, which is fast, accurate as well as memory efficient. The insight is to first map elements to a high dimensional space to almost eliminate hashing collisions, and then use a dimensional reduction representation, which is similar to coloring a graph, to save memory. Theoretical proofs and experimental results show that compared to the state-of-the[1]art, the error rate of the coloring embedder is thousands of times smaller even with much less memory usage, and the query speed of the coloring embedder is about 2 times faster. The source code of coloring embedder is released on Github.

When network is undergoing problems such as congestion, scan attack, DDoS attack, etc, measurements are much more important than usual. In this case, traffic characteristics including available bandwidth, packet rate, and flow size distribution vary drastically, significantly degrading the performance of measurements. To address this issue, we propose the Elastic sketch. It is adaptive to currently traffic characteristics. Besides, it is generic to measurement tasks and platforms. We implement the Elastic sketch on six platforms: P4, FPGA, GPU, CPU, multi-core CPU, and OVS, to process six typical measurement tasks. Experimental results and theoretical analysis show that the Elastic sketch can adapt well to traffic characteristics. Compared to the state-of-the-art, the Elastic sketch achieves 44.6 ~ 45.2 times faster speed and 2.0 ~ 273.7 smaller error rate.

When network is undergoing problems such as congestion, scan attack, DDoS attack, etc., measurements are much more important than usual. In this case, traffic characteristics including available bandwidth, packet rate, and flow size distribution vary drastically, significantly degrading the performance of measurements. To address this issue, we propose the Elastic sketch. It is adaptive to currently traffic characteristics. Besides, it is generic to measurement tasks and platforms. We implement the Elastic sketch on six platforms: P4, FPGA, GPU, CPU, multi-core CPU, and OVS, to process six typical measurement tasks. Experimental results and theoretical analysis show that the Elastic sketch can adapt well to traffic characteristics. Compared to the state-of-the-art, the Elastic sketch achieves 44.6 ∼ 45.2 times faster speed and 2.0 ∼ 273.7 smaller error rate.

Approximate stream processing algorithms, such as Count-Min sketch, Space-Saving, etc., support numerous applications in databases, storage systems, networking, and other domains. However, the unbalanced distribution in real data streams poses great challenges to existing algorithms. To enhance these algorithms, we propose a meta-framework, called Cold Filter (CF), that enables faster and more accurate stream processing.