arXiv 论文速递

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

用大语言模型自动发现先进量子电路，把LLM能力引入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…

针对机器人世界模型无法观测环境隐因素的问题，提出测试时优化环境潜变量，提升动作后果预测的准确性，对基于模型的机器人控制很重要。

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…

指出物理可行的生成仿真环境未必对训练有用，提出数据引擎与动作策略协同演化，提升生成式仿真训练机器人的实际效用。

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…

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…

构建面向自主机器学习工程的通用智能体模型，将LLM从辅助科研推进到自动开发与改进AI系统，是AI研发自动化的关键一步。

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方法让机器人自主采集接触丰富的经验来学习灵巧操作，解决灵巧操作数据稀缺这一核心瓶颈，对通用机器人技能学习意义重大。

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…