arXiv 论文速递

构建个性化机器人操作基准，填补通用机器人进入家庭场景时个性化能力评估的空白，推动实用化机器人研究。

Personalization is essential for general-purpose robots to transition into household environments and achieve true practicality. While general robotic manipulation has been extensi…

利用大语言模型自动发现先进量子电路，将LLM应用于量子信息处理器的电路设计，有望加速实用量子计算实现。

Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum advantage also requires the d…

Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitl…

Generative simulation can automate robot environment construction, but physically valid environments are not necessarily useful for training. The challenge is to identify environme…

Time series foundation models are becoming increasingly popular for general-purpose forecasting. However, most leading models predict marginal quantiles, leaving the dependence str…

Mixture-of-experts (MoE) models have become an important approach for scaling model parameters. Modern MoE architectures increasingly combine two design choices: fine-grained exper…

针对真实世界客户任务定义模糊的问题构建智能体基准，弥补现有基准任务过于明确的缺陷，推动AI落地最后一公里。

Existing agent benchmarks typically evaluate agents on well-specified tasks defined by issue reports, specifications, or task tickets. Real-world customer delivery is different: fo…

探索让大模型自主完成机器学习工程任务的通用智能体，拓展LLM从辅助科研到自主开发AI系统的能力边界。

Large language models (LLMs) are expanding from assisting scientific research and engineering to autonomously developing and improving AI systems, advancing AI for AI (AI4AI) as a …

Recent 3D world models increasingly use features from geometry foundation models (GFMs) as predictive world states, benefiting from their rich geometric priors. However, the view d…

We introduce 3DCodeVerse, a research ecosystem that enables coding agents to create editable and verifiable 3D content at scale. 3DCodeVerse comprises large-scale, high-quality dat…

提出DSP方法让机器人通过监督式玩耍自主采集接触丰富的灵巧操作经验，解决灵巧操作数据获取难题，对机器人自主学习意义重大。

How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present DSP: Dexterity from supervised play, a framework in which a robot…

Retrieving relevant lemmas is crucial for formal theorem proving, enabling provers to build machine-checkable proofs from established mathematical results. Recent retrieval systems…