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GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation
Ning Gao, Yilun Chen, Shuai Yang, Xinyi Chen, Yang Tian, Hao Li, Haifeng Huang, Hanqing Wang, Tai Wang, Jiangmiao Pang
https://arxiv.org/abs/2506.10966

GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation
Robotic manipulation in real-world settings remains challenging, especially regarding robust generalization. Existing simulation platforms lack sufficient support for exploring how policies adapt to varied instructions and scenarios. Thus, they lag behind the growing interest in instruction-following foundation models like LLMs, whose adaptability is crucial yet remains underexplored in fair comparisons. To bridge this gap, we introduce GenManip, a realistic tabletop simulation platform tailore…

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$\Delta$-AttnMask: Attention-Guided Masked Hidden States for Efficient Data Selection and Augmentation
Jucheng Hu, Suorong Yang, Dongzhan Zhou
https://arxiv.org/abs/2508.09199 h…

$Δ$-AttnMask: Attention-Guided Masked Hidden States for Efficient Data Selection and Augmentation
Visual Instruction Finetuning (VIF) is pivotal for post-training Vision-Language Models (VLMs). Unlike unimodal instruction finetuning in plain-text large language models, which mainly requires instruction datasets to enable model instruction-following ability, VIF also requires multimodal data to enable joint visual and textual understanding; therefore, it typically requires more data. Consequently, VIF imposes stricter data selection challenges: the method must scale efficiently to handle lar…

In some UK woodlands, every young tree has died. What’s going wrong? https://www.theguardian.com/environment/2025/jul/10/britain-ancient-woodlands-failing-regenerate-forests-climate-drought-heat-dis…

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following
Hao Peng, Yunjia Qi, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li
https://arxiv.org/abs/2506.09942 …

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following
Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing large language models (LLMs), with verification engineering playing a central role. However, best practices for RL in instruction following remain underexplored. In this work, we explore the verification challenge in RL for instruction following and propose VerIF, a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model (e.g., QwQ-32B). …

If you read the headlines or on social media, you’ve seen what’s happening.
• The career professional laid off after 22 years who can't get rehired
• The small business owner who can’t keep up with tariffs or inflation
• The young adult feeling like they’ll never be able to afford a home
• The retiree forced back to work after their retirement account took a hit
These aren't isolated incidents. They're symptoms of a fundamental shift.

Momentum-Resolved Relaxation-Time Approach for Size-Dependent Conductivity in Anisotropic Metallic Films
YoungJun Lee, Jin Soo Lee, Seungjun Lee, Seoung-Hun Kang, Young-Kyun Kwon
https://arxiv.org/abs/2508.08622

Momentum-Resolved Relaxation-Time Approach for Size-Dependent Conductivity in Anisotropic Metallic Films
Shrinking CMOS interconnect dimensions to the nanometer scale intensifies electron scattering at surfaces, interfaces, and grain boundaries, causing severe conductivity loss and challenging copper-based designs. Here we present a momentum-resolved relaxation time framework that integrates density functional theory with the semiclassical Boltzmann transport equation to predict size-dependent resistivity in metallic thin films. Electron phonon interactions are computed from first principles, and …

DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search
Zerui Yang, Yuwei Wan, Yinqiao Li, Yudai Matsuda, Tong Xie, Linqi Song
https://arxiv.org/abs/2507.07426

DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search
Recent advances in large language models have demonstrated considerable potential in scientific domains such as drug discovery. However, their effectiveness remains constrained when reasoning extends beyond the knowledge acquired during pretraining. Conventional approaches, such as fine-tuning or retrieval-augmented generation, face limitations in either imposing high computational overhead or failing to fully exploit structured scientific data. To overcome these challenges, we propose DrugMCTS…

ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation
Xi Chen, Zhiqiang Shen, Peng Cao, Jinzhu Yang, Osmar R. Zaiane
https://arxiv.org/abs/2506.10675

ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation
Medical images are usually collected from multiple domains, leading to domain shifts that impair the performance of medical image segmentation models. Domain Generalization (DG) aims to address this issue by training a robust model with strong generalizability. Recently, numerous domain randomization-based DG methods have been proposed. However, these methods suffer from the following limitations: 1) constrained efficiency of domain randomization due to their exclusive dependence on image style…

Large unconventional anomalous Hall effect far above room temperature in epitaxial Fe$_3$Ga$_4$ films
Jing Meng, Huali Yang, Yu Shen, Kun Zheng, Hongru Wang, Yuhao Wang, Keqi Xia, Bocheng Yu, Xiaoyan Zhu, Baiqing Lv, Yaobo Huang, Jie Ma, Dariusz Jakub Gawryluk, Toni Shiroka, Zhenzhong Yang, Yang Xu, Qingfeng Zhan, Tian Shang
https://

Large unconventional anomalous Hall effect far above room temperature in epitaxial Fe$_3$Ga$_4$ films
Noncoplanar spin textures usually exhibit a finite scalar spin chirality (SSC) that can generate effective magnetic fields and lead to additional contributions to the Hall effect, namely topological or unconventional anomalous Hall effect (UAHE). Unlike topological spin textures (e.g., magnetic skyrmions), materials that exhibit fluctuation-driven SSC and UAHE are rare. So far, their realization has been limited to either low temperatures or high magnetic fields, both of which are unfavorable f…