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Core Viewpoint - The article emphasizes the importance of open-source projects in advancing mobile and dual-arm robotic operations, highlighting their role in breaking down technical barriers and accelerating innovation in various applications, from household robots to industrial automation [3]. Group 1: Open-Source Projects Overview - XLeRobot, developed by Nanyang Technological University, focuses on flexible movement and precise operation in complex environments, providing a reference framework for mobile and dual-arm control [4]. - AhaRobot from Tianjin University emphasizes autonomy and environmental adaptability in dual-arm operations, integrating perception, planning, and control modules for service robots [6]. - ManiGaussian++, released by Tsinghua University, optimizes dual-arm operation accuracy using Gaussian models, particularly in 3D environment perception and motion planning [8]. - H-RDT, a collaboration between Tsinghua University and Horizon Robotics, aims at efficient decision-making and real-time operations for mobile robots in various settings [11]. - RoboTwin 2.0, developed by Shanghai Jiao Tong University and the University of Hong Kong, integrates simulation and physical platforms for mobile and dual-arm operations [14]. - Open X-Embodiment, from Arizona State University, focuses on a generalized learning framework for robotic operations, supporting cross-scenario skill transfer [16]. - 3D FlowMatch Actor, a joint project by Carnegie Mellon University and NVIDIA, enhances dynamic adaptability in 3D space for mobile and dual-arm operations [19]. - OmniH2O, developed by Carnegie Mellon University, focuses on human-robot action mapping and humanoid operation, facilitating remote control and action teaching [24]. - TidyBot++, a collaboration between Princeton University and Stanford University, targets household organization tasks, integrating object recognition and dual-arm collaboration algorithms [27]. - robosuite, from the University of California, Berkeley, is a mature simulation platform for robotic operations, providing standardized tasks and evaluation tools [29]. - SO-ARM100, a standardized dual-arm operation hardware and software solution, aims to lower development barriers for educational and research purposes [32]. - GOAT, developed by UIUC and CMU, focuses on goal-directed movement and operation for robots, emphasizing robustness and versatility [34]. - Mobile ALOHA, from Stanford University, combines mobile chassis and dual-arm operations for low-cost, easily deployable service robots [35].

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Cryptocurrency Listing Poll - A poll is being conducted to determine which cryptocurrency project should be listed on Binance first [1] - The projects under consideration are $WLFI, $GOAT, $WKC, and $KAS [1] Social Media Engagement - Participants are encouraged to repost and follow @BSCGemsAlert [1] - The hashtag BSCGemsAlert is used to promote the poll [1]

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Project Listing - Binance is considering listing one of four projects: $GOAT, $WKC, $WLFI, or $MANYU [1] - The document is soliciting community input on which project should be listed first [1]

Cryptocurrency Listing - Binance is considering listing coins from a list including $GOAT, $COPE, $DOOPE, $WKC, and $MANYU [1] - The presale for $DOOPE (@DoopeOnSol) is currently live [1] Community Engagement - The post encourages users to like and retweet if they want Binance to list the mentioned coins [1] - The post also asks for suggestions on what to add to the list [1]

New Listing - Bitget Onchain 上线新的交易对[1] - 新上线的交易对包括 $GOAT, $MEOW, $SMS [1] Trading Information - 用户可以通过 Bitget App 的 Markets → Onchain 板块进行交易[1] - 提供了交易链接: https://t.co/qHgg0ymkyg 和 https://t.co/Ht6sM30tcu [1]

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Core Insights - The article discusses the advancements in low-rank adaptation (LoRA) methods for fine-tuning large pre-trained models, highlighting the introduction of a new framework called GOAT that improves performance while maintaining efficiency [2][3][18]. Group 1: LoRA and Its Challenges - Large foundational models like Qwen, GPT, and DeepSeek R1 are essential in modern deep learning, but their extensive parameter sizes lead to high fine-tuning costs [1]. - Traditional LoRA methods reduce trainable parameters significantly (typically adjusting only 0.1%-5%) but often underperform compared to full fine-tuning [6]. - Existing methods for optimizing LoRA performance, such as random initialization or static singular value decomposition (SVD), fail to fully leverage the knowledge embedded in pre-trained models [6][12]. Group 2: GOAT Framework - The GOAT framework introduces adaptive singular value initialization and mixed expert gradient alignment strategies, addressing the performance limitations of LoRA [3][18]. - GOAT has been validated across 25 multi-domain tasks, achieving performance that matches or exceeds full parameter fine-tuning while only adjusting a minimal percentage of parameters [3][18]. - The framework allows for a significant reduction in memory usage, with training LLaMA7B requiring only 35GB compared to 640GB for full parameter fine-tuning MoE [18]. Group 3: Experimental Results - In natural language generation tasks, GOAT outperformed mainstream LoRA MoE variants by 4.2% in Mt-Bench, 6.3% in GSM8K, and 3.1% in HumanEval, approaching full fine-tuning levels [18]. - In image classification, GOAT achieved 99% of full parameter fine-tuning performance using only 2.24% of the parameters, surpassing other LoRA variants by 6% [18]. - The average accuracy in common sense reasoning tasks reached 82.73%, exceeding ChatGPT by 7.42%, demonstrating strong knowledge transfer capabilities [18].