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Papers for June 30, 2026

10 papers found

Yen-Jen Wang, Jiaman Li, Sirui Chen, Takara E. Truong, Pei Xu, Pieter Abbeel, Rocky Duan, Koushil Sreenath, Angjoo Kanazawa, Carmelo Sferrazza, Guanya Shi, Karen Liu 6/29/2026 arxiv

robotics

Perception-based humanoid loco-manipulation requires connecting egocentric observations and task instructions to whole-body motion. Learning this mapping requires synchronized egocentric images, language commands, and robot-compatible kinematic trajectories, yet no existing data source provides this...

Keywords: humanoid loco-manipulation, synthetic interactions, reconstructed scenes, vision-language-kinematics, robot learning

Shun Lei, Huaicheng Zhang, Dapeng Wu, Yaoxun Xu, Lishi Zuo, Wei Tan, Hangting Chen, Guangzheng Li, Jianwei Yu, Zhiyong Wu, Dong Yu 6/29/2026 arxiv

machine learning

Full-length song generation must preserve coherence and musicality, render detailed vocal and accompaniment acoustics, and follow lyrics and prompts. Existing language model-based systems face a structural trade-off: mixed-token modeling preserves vocal-instrument coordination but obscures track-spe...

Keywords: LeVo 2, song generation, hierarchical modeling, music codec, aesthetics-guided training

Xuan Zhang, Wenxuan Zhang, See-Kiong Ng, Yang Deng 6/29/2026 arxiv

machine learning

World models offer a principled way to equip long-horizon LLM agents with foresight: predictions of action consequences before execution. However, unreliable foresight can be ignored, misused, or even degrade downstream decision-making. In this paper, we introduce WorldEvolver, a self-evolving world...

Keywords: WorldEvolver, world model, LLM agent, foresight, planning, Episodic Memory, Semantic Memory, Selective Foresight

Jameel Hassan, Yasiru Ranasinghe, Vishal Patel 6/29/2026 arxiv

computer vision

3D Gaussian Splatting (3DGS) has emerged at the forefront of 3D scene reconstruction. Extending 3DGS with language-driven, open-vocabulary understanding has gained significant attention for real-world applications such as embodied AI. Recent methods achieve this by learning an instance feature attri...

Keywords: 3D scene reconstruction, open-vocabulary understanding, GaussDet, referring expression grounding, 3D Gaussian Splatting

Philip Zmushko, Egor Petrov, Nursultan Abdullaev, Mikhail Khrushchev, Samuel HorvΓ‘th 6/29/2026 arxiv

machine learning

Modern large-scale LLM pretraining benefits from utilizing Pipeline Parallelism; however, synchronous implementations leave GPUs idle during pipeline bubbles, wasting computational resources. Asynchronous Pipeline Parallelism eliminates these bubbles, maximizing throughput at the cost of gradient st...

Keywords: asynchronous pipeline parallelism, gradient delay, LLM pretraining, optimizer, Muon, PipeDream-2BW

Yuhong Deng, Yuyao Liu, David Hsu 6/29/2026 arxiv

computer vision

Can the robot use a plate to cut a cake if no knife is available? Tool use greatly expands robot capabilities, but to use tools creatively beyond their intended functions, the robot faces the challenge of $\textit{open-world affordance grounding}$: select an open-category object to act as a tool and...

Keywords: robot tool use, affordance grounding, Vision-Language Models, VLMs, 3D region grounding, open-world affordance, robotic manipulation

Subramanyam Sahoo, Aman Chadha, Vinija Jain, Divya Chaudhary 6/29/2026 arxiv

machine learning

Conservative offline training is widely advocated as a safe foundation for subsequent online adaptation: if a policy stays close to well-supported behaviour, the argument goes, it is less likely to exploit imperfections in a learned reward model. We challenge this intuition empirically and mechanist...

Keywords: offline training, online adaptation, reward hacking, conservatism, AI safety, policy entropy

Xinlei Yu, Gen Li, Qingyi Si, Guibin Zhang, Yuqi Xu, Congcong Wang, Shuai Dong, Kaiwen Tuo, Xiangyu Zeng, Kaituo Feng, Qunzhong Wang, Yang Shi, Xiaobin Hu, Xiangyu Yue, Jiaqi Wang, Shuicheng Yan 6/29/2026 arxiv

machine learning

On-policy distillation (OPD) offers superior capacity transfer by supervising student-sampled trajectories with dense token-level signals. To furnish high-quality supervision sources and thereby elevate the performance frontier of distillation, an intuitive direction is to infuse privileged informat...

Keywords: DOPD, distillation, machine learning, computer vision, privileged information, token-level supervision

Lei Bai, Zongsheng Cao, Yang Chen, Zhiyao Cui, Shangheng Du, Yue Fan, Shiyang Feng, Zijie Guo, Haonan He, Liang He, Xiaohan He, Shuyue Hu, Yusong Hu, Songtao Huang, Yichen Jiang, Hao Li, Xin Li, Dahua Lin, Weihao Lin, Fenghua Ling, Dongrui Liu, Zhuo Liu, Runmin Ma, Chunjiang Mu, Haoyang Peng, Tianshuo Peng, Jinxin Shi, Luohe Shi, Boyuan Sun, Zelin Tan, Shengji Tang, Qianyi Wang, Yiming Wu, Yi Xie, Xiangchao Yan, Jingqi Ye, Peng Ye, Fangchen Yu, Jiakang Yuan, Bihao Zhan, Bo Zhang, Chen Zhang, Shufei Zhang, Shuaiyu Zhang, Wenlong Zhang, Yiqun Zhang, Junpeng Zhao, Zhijie Zhong, Bowen Zhou, Yuhao Zhou 6/29/2026 arxiv

machine learning

We introduce Agents-A1, a 35B Mixture-of-Experts Agentic Model that reaches trillion-parameter-level performance by scaling the agent horizon. We investigate agent-horizon scaling from two perspectives: scaling long-horizon trajectories and scaling heterogeneous agent abilities. To support this goal...

Keywords: Agents-A1, trillion parameters, long-horizon tasks, knowledge-action infrastructure, domain-routed distillation

Bryce Grant, Aryeh Rothenberg, Logan Senning, Zonghe Chua, Zach Patterson, Peng Wang 6/29/2026 arxiv

robotics

We present Sequential Planning via Anchored Robotic Keypoints, SPARK, a training-free neurosymbolic manipulation system that reaches 43.7% on six LIBERO-PRO position \& task cells, more than doubling CaP-Agent0 and Vision-Language-Action (VLA) baselines. CaP-Agent0, a multi-turn code-generation ...

Keywords: Sequential Planning, Anchored Robotic Keypoints, Neurosymbolic Manipulation, LIBERO-PRO, Vision-Language-Action, Behavior Trees, Recovery Loops
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