Tootfinder

Metaverse Support Groups for LGBTQ Youth: An Observational Study on Safety, Self-Expression, and Early Intervention
Joe Hasei, Yosuke Matsumoto, Hiroki Kawai, Yuko Okahisa, Manabu Takaki, Toshifumi Ozaki
https://arxiv.org/abs/2507.21079

Metaverse Support Groups for LGBTQ+ Youth: An Observational Study on Safety, Self-Expression, and Early Intervention
This study assessed metaverse-based support groups designed to reduce social isolation and suicide risk among LGBTQ+ youths. Using the Cluster platform, enhanced anonymity, avatar-based self-expression, and accessibility were provided. Key findings showed that 79.2% chose avatars matching their gender identity, reporting high satisfaction (mean: 4.10/5) and low discomfort (mean: 1.79/5). Social confidence significantly improved in virtual spaces compared to real-world interactions (p<0.001), pa…

This is the time to speak out, to disembed our selves from a fascist system, to place principles over profit and self-advancement.
To be what Viet Thanh Nguyen calls “disagreeable”.
Yes, of course there are risks.
But at this moment, with ICE in the streets and at our door,
when each hour another liberty is being erased,
and those who speak truth to power are being removed from TV,
from universities, from cultural centers,
when the cultural platform…

LLMs-guided adaptive compensator: Bringing Adaptivity to Automatic Control Systems with Large Language Models
Zhongchao Zhou, Yuxi Lu, Yaonan Zhu, Yifan Zhao, Bin He, Liang He, Wenwen Yu, Yusuke Iwasawa
https://arxiv.org/abs/2507.20509

LLMs-guided adaptive compensator: Bringing Adaptivity to Automatic Control Systems with Large Language Models
With rapid advances in code generation, reasoning, and problem-solving, Large Language Models (LLMs) are increasingly applied in robotics. Most existing work focuses on high-level tasks such as task decomposition. A few studies have explored the use of LLMs in feedback controller design; however, these efforts are restricted to overly simplified systems, fixed-structure gain tuning, and lack real-world validation. To further investigate LLMs in automatic control, this work targets a key subfiel…

Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol
Wei Ma, Yixiao Yang, Qiang Hu, Shi Ying, Zhi Jin, Bo Du, Zhenchang Xing, Tianlin Li, Junjie Shi, Yang Liu, Linxiao Jiang
https://arxiv.org/abs/2508.20737

Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol
Applications of Large Language Models~(LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions. Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance. This paper decomposes LLM applications into a three-layer architecture: \textbf{\textit{System Shell Layer}}, \textbf{\textit{Prompt Orchestration Layer}}, and \textbf{\textit{LLM In…

Multilevel Decomposition of Generalized Entropy Measures Using Constrained Bayes Estimation: An Application to Japanese Regional Data
Yuki Kawakubo, Kazuhiko Kakamu
https://arxiv.org/abs/2506.21213

Multilevel Decomposition of Generalized Entropy Measures Using Constrained Bayes Estimation: An Application to Japanese Regional Data
We propose a method for multilevel decomposition of generalized entropy (GE) measures that explicitly accounts for nested population structures such as national, regional, and subregional levels. Standard approaches that estimate GE separately at each level do not guarantee compatibility with multilevel decomposition. Our method constrains lower-level GE estimates to match higher-level benchmarks while preserving hierarchical relationships across layers. We apply the method to Japanese income d…

Classification of color superconductivity by one-gluon exchange helicity amplitudes and renormalization group equations
Yuki Fujimoto
https://arxiv.org/abs/2508.19222 https://…

Classification of color superconductivity by one-gluon exchange helicity amplitudes and renormalization group equations
Quark matter at high baryon density exhibits diverse pairing patterns classified by color, flavor, and angular momentum quantum numbers. We compute one-gluon exchange (OGE) helicity amplitudes and introduce a nonrelativistic classification of the pairing channel, justified by the channel decomposition in a Lorentz-noninvariant medium and the decoupling of renormalization group flows at leading order. We find the new attractive channel in OGE; the medium effects can render the vacuum-repulsive c…

Multiphysics embedding localized orthogonal decomposition for thermomechanical coupling problems
Yuzhou Nan, Yajun Wang, Changqing Ye, Xiaofei Guan
https://arxiv.org/abs/2507.13644

Multiphysics embedding localized orthogonal decomposition for thermomechanical coupling problems
Multiscale modeling and analysis of multiphysics coupling processes in highly heterogeneous media present significant challenges. In this paper, we propose a novel multiphysics embedding localized orthogonal decomposition (ME-LOD) method for solving thermomechanical coupling problems, which also provides a systematic approach to address intricate coupling effects in multiphysical systems. Unlike the standard localized orthogonal decomposition (LOD) method that constructs separate multiscale spa…

HPD: Hybrid Projection Decomposition for Robust State Space Models on Analog CIM Hardware
Yuannuo Feng, Wenyong Zhou, Yuexi Lyu, Hanjie Liu, Zhengwu Liu, Ngai Wong, Wang Kang
https://arxiv.org/abs/2508.11935

HPD: Hybrid Projection Decomposition for Robust State Space Models on Analog CIM Hardware
State Space Models (SSMs) are efficient alternatives to traditional sequence models, excelling at processing long sequences with lower computational complexity. Their reliance on matrix multiplications makes them ideal for compute-in-memory (CIM) architectures, which improve energy efficiency by computing within memory arrays. However, device non-idealities in CIM introduce weight perturbations that can degrade inference accuracy. In this paper, we systematically analyze the robustness of SSMs …

GraphCogent: Overcoming LLMs' Working Memory Constraints via Multi-Agent Collaboration in Complex Graph Understanding
Rongzheng Wang, Qizhi Chen, Yihong Huang, Yizhuo Ma, Muquan Li, Jiakai Li, Ke Qin, Guangchun Luo, Shuang Liang
https://arxiv.org/abs/2508.12379

GraphCogent: Overcoming LLMs' Working Memory Constraints via Multi-Agent Collaboration in Complex Graph Understanding
Large language models (LLMs) show promising performance on small-scale graph reasoning tasks but fail when handling real-world graphs with complex queries. This phenomenon stems from LLMs' inability to effectively process complex graph topology and perform multi-step reasoning simultaneously. To address these limitations, we propose GraphCogent, a collaborative agent framework inspired by human Working Memory Model that decomposes graph reasoning into specialized cognitive processes: sense, buf…

Fair-CoPlan: Negotiated Flight Planning with Fair Deconfliction for Urban Air Mobility
Nicole Fronda, Phil Smith, Bardh Hoxha, Yash Pant, Houssam Abbas
https://arxiv.org/abs/2508.14380

Fair-CoPlan: Negotiated Flight Planning with Fair Deconfliction for Urban Air Mobility
Urban Air Mobility (UAM) is an emerging transportation paradigm in which Uncrewed Aerial Systems (UAS) autonomously transport passengers and goods in cities. The UAS have different operators with different, sometimes competing goals, yet must share the airspace. We propose a negotiated, semi-distributed flight planner that optimizes UAS' flight lengths {\em in a fair manner}. Current flight planners might result in some UAS being given disproportionately shorter flight paths at the expense of o…