Deep space missions increasingly require spacecraft capable of autonomously operating in environments with communication delays and bandwidth limitations. A key enabler of such autonomy is the ability to run learning-based algorithms directly onboard. In this work, we present a flight deployment on a 6U CubeSat that de...
Elena Sorina Lupu, Hannah Grauer, Phaedrus Leeds et al.· 0 citations
Achieving human-like dexterous manipulation remains a central challenge in robotics. Learning from human hand-object interaction (HOI) datasets offers a promising pathway to diverse dexterous skills, yet existing physics-based approaches train specialist policies for individual trajectories, thereby limiting skill reus...
Zhe-Xi Luo, Zhi-Xuan Xu, Rui Yang et al.· 0 citations
CyberArena is a dynamic, symmetric adversarial benchmark built around multi-round attack-and-defense combat, and introduces a dynamical model that maps match dynamics into unified, multidimensional scores across offensive, defensive, and domain-specific capabilities.
Bangya Liu, Jianhong Tu, Yu-Jing Pan et al.· 0 citations
Non-Abelian anyons, strange particles forming the basis of fault-tolerant topological quantum computing, are predicted to emerge in fractional quantum Hall states at even-denominator filling factors. Identifying which topological order is realized has proven to be a challenging problem that requires an accurate, non-pe...
Pierre-Antoine Graham, Ahmed Abouelkomsan, Liang Fu· 0 citations
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Clinical-context-Aware and Patient-adaptive Deformable Image Registration can improve contour usability and dose mapping for adaptive radiotherapy and show that clinically informed, longitudinal registration can improve contour usability and dose mapping for adaptive radiotherapy.
Cai-Wen Jiang, Song-Lin Zhao, Yu-Zhen Ding et al.· 1 citation
We present a suite of 50 unpublished research-level theoretical physics problems with verifiable answers. Without AI assistance, a typical problem in our collection could be solved by a world expert in about a day or less but would take considerably longer for experts in adjacent specialties. We test the ability of off...
A. Chandra, Aaghaz Mahajan, A. Sanyal et al.· 1 citation
3D object composition is pivotal to digital content creation and robot simulation, while also serving as a critical testbed for physical intelligence in foundation models. Previous methods either leverage large language models to directly predict object coordinates, or utilize generative diffusion priors to synthesize...
Weichen Lyu, Chong Xia, Ming-Yu Dong et al.· 0 citations
Today's agentic systems are typically designed, built, and deployed by single developers with full control over all participating agents. While this simplifies development, it limits the potential scale of agentic systems. We study multi-developer agentic systems (MDAs) which enable developers to collaboratively build...
Austin W. Hanjie, Carlos E. Jimenez, Zirui Wang et al.· 0 citations
Large language model (LLM) agents increasingly face tasks from unseen domains and execution contexts, making zero-shot tool selection essential for identifying tools that can satisfy task requirements. Existing dominant methods mainly rely on relevance ranking, but semantic matching alone cannot ensure that a tool is f...
Xiaoxuan Dong, Jin Zhang, Zichen Yuan et al.· 0 citations
Vision-Language Models (VLMs) have made substantial progress in spatial reasoning, and self-evolving agents further extend these capabilities to embodied environments by learning from interactions. However, current self-evolution remains particularly challenging in embodied spatial tasks, complex and multi-step decisio...
Xin Li, Xiao-Huan Gu, Yi-Fan Wang et al.· 0 citations
Counterfactual reasoning (asking what would have happened, had a decision been different) is a powerful tool for identifying causes of failure and explaining the behaviour of AI systems in sequential decision-making tasks. In Markov Decision Processes (MDPs), two sources of uncertainty complicate counterfactual inferen...
Large Language Models (LLMs) are increasingly deployed in multi-agent environments that produce unanimous agreement, both in intended interactions that need coordination and in unintended emergent interactions. Consequently, understanding how LLMs with competing incentives reach consensus is crucial to better interpret...
Isabel Jones, Matthew Edwards, J. Hoffmann· 0 citations
