Benjamin Pikus, Daniel Saunders, Mircea Mironenco, Salman Mohammadi, Mathew Mathew, Sirou Zhu, Divya Makkar, Xuhua Huang 等 14 位作者
AI 速览：提出ZAP方法，在仿真中仅用简单任务和模型自身成功经验训练小型开源VLM，使其获得运动控制技能，从而在真实机器人任务上达到与GPT-6 Astra相当的成功率，且更快更便宜。
Frontier models such as GPT-6 Astra now directly control robots and can generalize better than vision-language-action models (VLAs). We argue that this ability could arise from two distinct, composable skills: semantic reasoning, to understand what a task asks for in a scene, and motion control, to move the robot to do it. We show that small open VLMs are already capable of the first and mainly lack the second. We introduce Zero-real Agent Policy (ZAP), a recipe that teaches this missing motion skill, entirely in simulation on simple primitive tasks and from the model's own successes, with no frontier teacher or distillation and no real-world robot data. ZAP reaches 96.7% success on real-world tasks, matching GPT-6 Astra in our harness while running 3x faster and 17x cheaper, and 74.0% on a bimanual adaptation of RoboLab-120, on par with GPT-6 Astra's 72.2%. ZAP also generalizes to new objects, goals, and strategies, and in simulation to a robot embodiment on which it never trained.
Scientific discovery depends on reusing experimental knowledge, yet records scattered across papers and repositories are often incomplete. In materials synthesis, recovering missing conditions requires reasoning about the target material and the observed context of a specific experiment. Hyper-relational knowledge graphs (HKGs) represent this context through qualifiers, but existing completion methods leave domain knowledge and experimental regularities implicit. We propose RAHC, a rule-augmented HKG framework for single-slot and joint multi-slot synthesis-condition completion. RAHC builds a traceable rule catalog from training records and retrieved literature, retrieves rules for each query-candidate pair, and separately encodes graph structure and rule evidence. Hard rules constrain candidate validity, while soft rules guide ranking. RAHC-G aggregates rules using global reliability; RAHC-Q additionally learns query-specific relevance. Gated fusion combines structural and rule representations, while tuple-conclusion rules provide cross-condition evidence for joint completion. Experiments on five materials-synthesis datasets show improvements in single-slot and joint completion under Semi-inductive and Open-Answer Inductive settings, with Semi-inductive MRR gains of up to 15.6% over MAYPL. RAHC also improves performance on the entity-disjoint ILPC'22 Small benchmark, supporting transfer to fully inductive KG completion. These results support experimental condition selection, synthesis planning, and the reuse of scientific knowledge.
Dingyang Lyu, Zhengjia Xu, Jey Han Lau, Jianzhong Qi
Human mobility prediction, a core task of location-based web services, forecasts a user's next Point of Interest (POI) visit from historical trajectories, supporting applications from recommendation to urban planning. A general perception in the literature has been that visits to popular POIs are relatively easy to predict. Our analysis challenges this perception and shows that, even for visits to popular POIs, many predictions fail. The underlying cause is often transition-level sparsity, where a transition is a move from a source POI to a destination POI. While a POI may be popular, its transitions from other POIs may still be very sparse, since there are many POIs in a dataset. As a result, predicting a popular next POI can still be challenging if the transition from the source POI to it never appears in the training data. We therefore argue that a core bottleneck in human mobility prediction lies in the transition-level long tail, and propose REtrieval Combined with pArametric memory for next-POI prediction (RECAP). RECAP learns frequent transitions in the parameters of a small Transformer trained from scratch. Long-tail transitions give the parameters little or no training signal. RECAP retrieves evidence for them from two sources: (i) multi-hop transitivity learned from a global transition graph of all users, and (ii) revisit evidence from a target user's historical trajectory. RECAP further uses two-stage training to let the revisit component also learn from frequently seen transitions. Experiments on three real-world check-in datasets show that RECAP improves prediction accuracy, with clear gains on tail transitions. Our code is available at https://github.com/NiallLDY/RECAP.
Remote sensing object detection has advanced rapidly with the development of large-scale benchmarks and modern detection architectures. However, existing datasets and detectors remain fragmented. Most benchmarks focus on limited categories, fixed spatial resolutions, or a single sensor, while detectors still struggle to work across different sensors and categorical systems. In this paper, we introduce LEVIRDet-159, the largest and most comprehensive remote sensing object detection dataset to date, with 159 categories, 2.56 million bounding boxes, and 700k fine-grained annotations under a multi-level taxonomy. In each key scale dimension, LEVIRDet-159 exceeds the corresponding largest existing remote sensing object detection dataset, containing approximately (7x) more images, (6x) more object instances, and (4x) more categories. Based on this dataset, we design LEVIRDetNet, a scale-hierarchy-aware detection foundation model for universal remote sensing object detection. LEVIRDetNet couples online visual Ground Sampling Distance (GSD) prediction, GSD-conditioned query modulation and allocation, and a hierarchy-aware detection head for mixed-granularity remote sensing supervision. Under stringent evaluation settings, LEVIRDetNet demonstrates strong cross-domain generalization. Even without target-domain training or fine-tuning, it achieves state-of-the-art detection performance on 9 external benchmarks, improving the strongest fully supervised competing methods by 5.02 mAP on average under each benchmark's primary metric. We hope this study will facilitate the development of strongly generalizable remote sensing object detection across diverse category systems, spatial resolutions, and sensor platforms. The full image tiles, annotations, source-license manifest, code, and trained models have been released in a versioned project repository at https://qinzheyang.github.io/LEVIRDet/.