OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence

Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses on domain-specific data production, leaving a critical void: the absence of a principled, open-source engine capable of fully unleashing the potential of high-quality spatial data. To bridge this gap, we elucidate the design principles of a robust data generation system and introduce OpenSpatial -- an open-source data engine engineered for high quality, extensive scalability, broad task diversity, and optimized efficiency. OpenSpatial adopts 3D bounding boxes as the fundamental primitive to construct a comprehensive data hierarchy across five foundational tasks: Spatial Measurement (SM), Spatial Relationship (SR), Camera Perception (CP), Multi-view Consistency (MC), and Scene-Aware Reasoning (SAR). Leveraging this scalable infrastructure, we curate OpenSpatial-3M, a large-scale dataset comprising 3 million high-fidelity samples. Extensive evaluations demonstrate that versatile models trained on our dataset achieve state-of-the-art performance across a wide spectrum of spatial reasoning benchmarks. Notably, the best-performing model exhibits a substantial average improvement of 19 percent, relatively. Furthermore, we provide a systematic analysis of how data attributes influence spatial perception. By open-sourcing both the engine and the 3M-scale dataset, we provide a robust foundation to accelerate future research in spatial intelligence.

HarmonRank: Ranking-aligned Multi-objective Ensemble for Live-streaming E-commerce Recommendation

Recommendation for live-streaming e-commerce is gaining increasing attention due to the explosive growth of the live streaming economy. Different from traditional e-commerce, live-streaming e-commerce shifts the focus from products to streamers, which requires ranking mechanism to balance both purchases and user-streamer interactions for long-term ecology. To trade off multiple objectives, a popular solution is to build an ensemble model to integrate multi-objective scores into a unified score. The ensemble model is usually supervised by multiple independent binary classification losses of all objectives. However, this paradigm suffers from two inherent limitations. First, the optimization direction of the binary classification task is misaligned with the ranking task (evaluated by AUC). Second, this paradigm overlooks the alignment between objectives, e.g., comment and buy behaviors are partially dependent which can be revealed in labels correlations. The model can achieve better trade-offs if it learns the aligned parts of ranking abilities among different objectives. To mitigate these limitations, we propose a novel multi-objective ensemble framework HarmonRank to fulfill both alignment to the ranking task and alignment among objectives. For alignment to ranking, we formulate ranking metric AUC as a rank-sum problem and utilize differentiable ranking techniques for ranking-oriented optimization. For inter-objective alignment, we change the original one-step ensemble paradigm to a two-step relation-aware ensemble scheme. Extensive offline experiments results on two industrial datasets and online experiments demonstrate that our approach significantly outperforms existing state-of-the-art methods. The proposed method has been fully deployed in Kuaishou's live-streaming e-commerce recommendation platform with 400 million DAUs, contributing over 2% purchase gain.