Tootfinder

"Mapping What I Feel": Understanding Affective Geovisualization Design Through the Lens of People-Place Relationships
Xingyu Lan, Yutong Yang, Yifan Wang
https://arxiv.org/abs/2507.11841

"Mapping What I Feel": Understanding Affective Geovisualization Design Through the Lens of People-Place Relationships
Affective visualization design is an emerging research direction focused on communicating and influencing emotion through visualization. However, as revealed by previous research, this area is highly interdisciplinary and involves theories and practices from diverse fields and disciplines, thus awaiting analysis from more fine-grained angles. To address this need, this work focuses on a pioneering and relatively mature sub-area, affective geovisualization design, to further the research in this…

Biased Tales: Cultural and Topic Bias in Generating Children's Stories
Donya Rooein, Vil\'em Zouhar, Debora Nozza, Dirk Hovy
https://arxiv.org/abs/2509.07908 https://

Biased Tales: Cultural and Topic Bias in Generating Children's Stories
Stories play a pivotal role in human communication, shaping beliefs and morals, particularly in children. As parents increasingly rely on large language models (LLMs) to craft bedtime stories, the presence of cultural and gender stereotypes in these narratives raises significant concerns. To address this issue, we present Biased Tales, a comprehensive dataset designed to analyze how biases influence protagonists' attributes and story elements in LLM-generated stories. Our analysis uncovers stri…

USIM and U0: A Vision-Language-Action Dataset and Model for General Underwater Robots
Junwen Gu, Zhiheng wu, Pengxuan Si, Shuang Qiu, Yukai Feng, Luoyang Sun, Laien Luo, Lianyi Yu, Jian Wang, Zhengxing Wu
https://arxiv.org/abs/2510.07869

USIM and U0: A Vision-Language-Action Dataset and Model for General Underwater Robots
Underwater environments present unique challenges for robotic operation, including complex hydrodynamics, limited visibility, and constrained communication. Although data-driven approaches have advanced embodied intelligence in terrestrial robots and enabled task-specific autonomous underwater robots, developing underwater intelligence capable of autonomously performing multiple tasks remains highly challenging, as large-scale, high-quality underwater datasets are still scarce. To address these…

Fundamental Quality Bound on Optical Quantum Communication
Tobias Rippchen, Ludovico Lami, Gerardo Adesso, Mario Berta
https://arxiv.org/abs/2510.07121 https://

Fundamental Quality Bound on Optical Quantum Communication
Sending quantum information reliably over long distances is a central challenge in quantum technology in general, and in quantum optics in particular, since most quantum communication relies on optical fibres or free-space links. Here, we address this problem by shifting the focus from the quantity of information sent to the quality of the transmission, i.e. the rate of decay of the transmission error with respect to the number of channel uses. For the general class of teleportation-simulable c…

Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicators
Hyo Jin Do, Rachel Ostrand, Werner Geyer, Keerthiram Murugesan, Dennis Wei, Justin Weisz
https://arxiv.org/abs/2508.06846

Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicators
Large language models (LLMs) are susceptible to generating inaccurate or false information, often referred to as "hallucinations" or "confabulations." While several technical advancements have been made to detect hallucinated content by assessing the factuality of the model's responses, there is still limited research on how to effectively communicate this information to users. To address this gap, we conducted two scenario-based experiments with a total of 208 participants to systematically co…

FlashCommunication V2: Bit Splitting and Spike Reserving for Any Bit Communication
Qingyuan Li, Bo Zhang, Hui Kang, Tianhao Xu, Yulei Qian, Yuchen Xie, Lin Ma
https://arxiv.org/abs/2508.03760

FlashCommunication V2: Bit Splitting and Spike Reserving for Any Bit Communication
Nowadays, communication bottlenecks have emerged as a critical challenge in the distributed training and deployment of large language models (LLMs). This paper introduces FlashCommunication V2, a novel communication paradigm enabling efficient cross-GPU transmission at arbitrary bit widths. Its core innovations lie in the proposed bit splitting and spike reserving techniques, which address the challenges of low-bit quantization. Bit splitting decomposes irregular bit widths into basic units, en…

Large AI Models for Wireless Physical Layer
Jiajia Guo, Yiming Cui, Shi Jin, Jun Zhang
https://arxiv.org/abs/2508.02314 https://arxiv.org/pdf/2508.02314

Large AI Models for Wireless Physical Layer
Large artificial intelligence models (LAMs) are transforming wireless physical layer technologies through their robust generalization, multitask processing, and multimodal capabilities. This article reviews recent advancements in LAM applications for physical layer communications, addressing limitations of conventional AI-based approaches. LAM applications are classified into two strategies: leveraging pre-trained LAMs and developing native LAMs designed specifically for physical layer tasks. T…

SlimPack: Fine-Grained Asymmetric Packing for Balanced and Efficient Variable-Length LLM Training
Yuliang Liu, Guohao Wu, Shenglong Zhang, Wei Zhang, Qianchao Zhu, Zhouyang Li, Chenyu Wang
https://arxiv.org/abs/2509.26246

SlimPack: Fine-Grained Asymmetric Packing for Balanced and Efficient Variable-Length LLM Training
The efficient distributed training of Large Language Models (LLMs) is severely hampered by the extreme variance in context lengths. This data heterogeneity, amplified by conventional packing strategies and asymmetric forward-backward costs, leads to critical inefficiencies such as cascading workload imbalances and severe hardware underutilization. Existing solutions attempt to mitigate these challenges, but often at the expense of memory or communication efficiency. To address these challenge…

When Machines Meet Each Other: Network Effects and the Strategic Role of History in Multi-Agent AI
Yu Liu, Wenwen Li, Yifan Dou, Guangnan Ye
https://arxiv.org/abs/2510.06903 htt…

When Machines Meet Each Other: Network Effects and the Strategic Role of History in Multi-Agent AI
As artificial intelligence (AI) enters the agentic era, large language models (LLMs) are increasingly deployed as autonomous agents that interact with one another rather than operate in isolation. This shift raises a fundamental question: how do machine agents behave in interdependent environments where outcomes depend not only on their own choices but also on the coordinated expectations of peers? To address this question, we study LLM agents in a canonical network-effect game, where economic …

IntentFlow: Interactive Support for Communicating Intent with LLMs in Writing Tasks
Yoonsu Kim, Brandon Chin, Kihoon Son, Seoyoung Kim, Juho Kim
https://arxiv.org/abs/2507.22134

IntentFlow: Interactive Support for Communicating Intent with LLMs in Writing Tasks
While large language models (LLMs) are widely used for writing, users often struggle to express their nuanced and evolving intents through prompt-based interfaces. Intents -- low-level strategies or preferences for achieving a writing goal -- are often vague, fluid, or even subconscious, making it difficult for users to articulate and adjust them. To address this, we present IntentFlow, which supports the communication of dynamically evolving intents throughout LLM-assisted writing. IntentFlow …