Tootfinder

Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs
Ring Team, Bin Hu, Cai Chen, Deng Zhao, Ding Liu, Dingnan Jin, Feng Zhu, Hao Dai, Hongzhi Luan, Jia Guo, Jiaming Liu, Jiewei Wu, Jun Mei, Jun Zhou, Junbo Zhao, Junwu Xiong, Kaihong Zhang, Kuan Xu, Lei Liang, Liang Jiang, Liangcheng Fu, Longfei Zheng, Qiang Gao, Qing Cui, Quan Wan, Shaomian Zheng, Shuaicheng Li, Tongkai Yang, Wang Ren, Xiaodong Yan, Xiaopei Wan, Xiaoyun Feng, Xin Zhao, Xinxing Yang, Xinyu …

Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs
We present Ring-lite, a Mixture-of-Experts (MoE)-based large language model optimized via reinforcement learning (RL) to achieve efficient and robust reasoning capabilities. Built upon the publicly available Ling-lite model, a 16.8 billion parameter model with 2.75 billion activated parameters, our approach matches the performance of state-of-the-art (SOTA) small-scale reasoning models on challenging benchmarks (e.g., AIME, LiveCodeBench, GPQA-Diamond) while activating only one-third of the par…

CDP: Towards Robust Autoregressive Visuomotor Policy Learning via Causal Diffusion
Jiahua Ma, Yiran Qin, Yixiong Li, Xuanqi Liao, Yulan Guo, Ruimao Zhang
https://arxiv.org/abs/2506.14769

CDP: Towards Robust Autoregressive Visuomotor Policy Learning via Causal Diffusion
Diffusion Policy (DP) enables robots to learn complex behaviors by imitating expert demonstrations through action diffusion. However, in practical applications, hardware limitations often degrade data quality, while real-time constraints restrict model inference to instantaneous state and scene observations. These limitations seriously reduce the efficacy of learning from expert demonstrations, resulting in failures in object localization, grasp planning, and long-horizon task execution. To add…

Distinguishing Ram Pressure from Tidal Interactions: the Size-Shape Difference (SSD) measure
Rory Smith, Stephanie Tonnesen, Katarina Kraljic, Paula Calderon-Castillo, Antonino Marasco, Yara Jaffe, Benedetta Vulcani, Bianca M. Poggianti
https://arxiv.org/abs/2506.13884

Distinguishing Ram Pressure from Tidal Interactions: the Size-Shape Difference (SSD) measure
Context: In dense environments, disk galaxies can be subjected to tidal interactions with other galaxies and/or ram pressure stripping. Some morphological features are clearly associated with one or the other interaction (e.g. tidal bridges vs long one-sided linear gas tails). But, under certain circumstances, both mechanisms can result in morphological features that could be confused, such as lopsided or asymmetric disks and unwinding spiral arms. Aims: Our aim is to develop new measures for…

Humanity's Last Code Exam: Can Advanced LLMs Conquer Human's Hardest Code Competition?
Xiangyang Li, Xiaopeng Li, Kuicai Dong, Quanhu Zhang, Rongju Ruan, Xinyi Dai, Xiaoshuang Liu, Shengchun Xu, Yasheng Wang, Ruiming Tang
https://arxiv.org/abs/2506.12713

Humanity's Last Code Exam: Can Advanced LLMs Conquer Human's Hardest Code Competition?
Code generation is a core capability of large language models (LLMs), yet mainstream benchmarks (e.g., APPs and LiveCodeBench) contain questions with medium-level difficulty and pose no challenge to advanced LLMs. To better reflected the advanced reasoning and code generation ability, We introduce Humanity's Last Code Exam (HLCE), comprising 235 most challenging problems from the International Collegiate Programming Contest (ICPC World Finals) and the International Olympiad in Informatics (IOI)…

03:35—Good night #Fediverse! 😴💤
- https://pixelfed.social/p/midtsveen/839638250579307406
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Did a quick test fit of one of the Minisforum MS-A2s and ran some of the cabling. I think this should turn out pretty clean and everything fits better than expected which is a good thing. The PSUs will go in the bottom 2U "compartment" without issue which I didn't run.
#minisforum #homelab

SheetMind: An End-to-End LLM-Powered Multi-Agent Framework for Spreadsheet Automation
Ruiyan Zhu, Xi Cheng, Ke Liu, Brian Zhu, Daniel Jin, Neeraj Parihar, Zhoutian Xu, Oliver Gao
https://arxiv.org/abs/2506.12339

SheetMind: An End-to-End LLM-Powered Multi-Agent Framework for Spreadsheet Automation
We present SheetMind, a modular multi-agent framework powered by large language models (LLMs) for spreadsheet automation via natural language instructions. The system comprises three specialized agents: a Manager Agent that decomposes complex user instructions into subtasks; an Action Agent that translates these into structured commands using a Backus Naur Form (BNF) grammar; and a Reflection Agent that validates alignment between generated actions and the user's original intent. Integrated int…

Walk This Way
Walk This Way
Walk This Way
https://www.youtube.com/watch?v=4B_UYYPb-Gk

A Novel Thermal Network Model and Electro-Thermal Coupling Study for NSFETs and CFETs Considering Thermal Crosstalk
Tianci Miao, Qihang Zheng, Yangyang Hu, Xiaoyu Cheng, Jie Liang, Liang Chen, Aiying Guo, Jingjing Liu, Kailin Ren, Jianhua Zhang
https://arxiv.org/abs/2506.12264

A Novel Thermal Network Model and Electro-Thermal Coupling Study for NSFETs and CFETs Considering Thermal Crosstalk
As the technology node continues to shrink, nanosheet field effect transistors (NSFETs) and complementary FETs (CFETs) become valid candidates for the 3nm and sub-nanometre nodes. However, due to the shrinking device size, self-heating and inter-device thermal crosstalk of NSFETs and CFETs become more severe. It is important to accurately calculate the self-heating and thermal crosstalk of devices and to study the electrical and thermal characteristics of logic gates, etc. In this work, a therm…

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation
Changxin Ke, Rui Zhang, Shuo Wang, Li Ding, Guangli Li, Yuanbo Wen, Shuoming Zhang, Ruiyuan Xu, Jin Qin, Jiaming Guo, Chenxi Wang, Ling Li, Qi Guo, Yunji Chen
https://arxiv.org/abs/2506.11153

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation
The rise of GPU-based high-performance computing (HPC) has driven the widespread adoption of parallel programming models such as CUDA. Yet, the inherent complexity of parallel programming creates a demand for the automated sequential-to-parallel approaches. However, data scarcity poses a significant challenge for machine learning-based sequential-to-parallel code translation. Although recent back-translation methods show promise, they still fail to ensure functional equivalence in the translate…