Tootfinder

HEAR: An EEG Foundation Model with Heterogeneous Electrode Adaptive Representation
Zhige Chen, Chengxuan Qin, Wenlong You, Rui Liu, Congying Chu, Rui Yang, Kay Chen Tan, Jibin Wu
https://arxiv.org/abs/2510.12515

HEAR: An EEG Foundation Model with Heterogeneous Electrode Adaptive Representation
Electroencephalography (EEG) is an essential technique for neuroscience research and brain-computer interface (BCI) applications. Recently, large-scale EEG foundation models have been developed, exhibiting robust generalization capabilities across diverse tasks and subjects. However, the heterogeneity of EEG devices not only hinders the widespread adoption of these models but also poses significant challenges to their further scaling and development. In this paper, we introduce HEAR, the first …

DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations
Elena Khasanova, Harsh Saini, Md Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen, Shashi Bhushan TN
https://arxiv.org/abs/2510.08152

DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations
The rapid advancements in Large Language Models (LLMs) have enabled their adoption in real-world industrial scenarios for various natural language processing tasks. However, the high inference cost of large-scale LLMs makes their deployment impractical, necessitating the use of smaller models. Despite their efficiency, smaller LLMs lack robust zero-shot instruction-following capabilities across diverse domains, limiting their adaptability to dynamic user requirements. Traditional fine-tuning ap…

RedCodeAgent: Automatic Red-teaming Agent against Diverse Code Agents
Chengquan Guo, Chulin Xie, Yu Yang, Zhaorun Chen, Zinan Lin, Xander Davies, Yarin Gal, Dawn Song, Bo Li
https://arxiv.org/abs/2510.02609

RedCodeAgent: Automatic Red-teaming Agent against Diverse Code Agents
Code agents have gained widespread adoption due to their strong code generation capabilities and integration with code interpreters, enabling dynamic execution, debugging, and interactive programming capabilities. While these advancements have streamlined complex workflows, they have also introduced critical safety and security risks. Current static safety benchmarks and red-teaming tools are inadequate for identifying emerging real-world risky scenarios, as they fail to cover certain boundary …

TridentServe: A Stage-level Serving System for Diffusion Pipelines
Yifei Xia, Fangcheng Fu, Hao Yuan, Hanke Zhang, Xupeng Miao, Yijun Liu, Suhan Ling, Jie Jiang, Bin Cui
https://arxiv.org/abs/2510.02838

TridentServe: A Stage-level Serving System for Diffusion Pipelines
Diffusion pipelines, renowned for their powerful visual generation capabilities, have seen widespread adoption in generative vision tasks (e.g., text-to-image/video). These pipelines typically follow an encode--diffuse--decode three-stage architecture. Current serving systems deploy diffusion pipelines within a static, manual, and pipeline-level paradigm, allocating the same resources to every request and stage. However, through an in-depth analysis, we find that such a paradigm is inefficient …

Designing AI-Infused Interactive Systems for Online Communities: A Systematic Literature Review
Yuanhao Zhang, Xiaoyu Wang, Jiaxiong Hu, Ziqi Pan, Zhenhui Peng, Xiaojuan Ma
https://arxiv.org/abs/2509.23309

Designing AI-Infused Interactive Systems for Online Communities: A Systematic Literature Review
AI-infused systems have demonstrated remarkable capabilities in addressing diverse human needs within online communities. Their widespread adoption has shaped user experiences and community dynamics at scale. However, designing such systems requires a clear understanding of user needs, careful design decisions, and robust evaluation. While research on AI-infused systems for online communities has flourished in recent years, a comprehensive synthesis of this space remains absent. In this work, w…

On-Premise AI for the Newsroom: Evaluating Small Language Models for Investigative Document Search
Nick Hagar, Nicholas Diakopoulos, Jeremy Gilbert
https://arxiv.org/abs/2509.25494

On-Premise AI for the Newsroom: Evaluating Small Language Models for Investigative Document Search
Investigative journalists routinely confront large document collections. Large language models (LLMs) with retrieval-augmented generation (RAG) capabilities promise to accelerate the process of document discovery, but newsroom adoption remains limited due to hallucination risks, verification burden, and data privacy concerns. We present a journalist-centered approach to LLM-powered document search that prioritizes transparency and editorial control through a five-stage pipeline -- corpus summar…

Introducing Large Language Models in the Design Flow of Time Sensitive Networking
Rubi Debnath, Luxi Zhao, Mohammadreza Barzegaran, Sebastian Steinhorst
https://arxiv.org/abs/2509.26368

Introducing Large Language Models in the Design Flow of Time Sensitive Networking
The growing demand for real-time, safety-critical systems has significantly increased both the adoption and complexity of Time Sensitive Networking (TSN). Configuring an optimized TSN network is highly challenging, requiring careful planning, design, verification, validation, and deployment. Large Language Models (LLMs) have recently demonstrated strong capabilities in solving complex tasks, positioning them as promising candidates for automating end-to-end TSN deployment, referred to as TSN or…

Full-Field Brillouin Microscopy with a Scanning Fabry-Perot Interferometer
Mikolaj Pochylski (Faculty of Physics,Astronomy, Adam Mickiewicz University, Poznan, Poland)
https://arxiv.org/abs/2510.00700 …

Full-Field Brillouin Microscopy with a Scanning Fabry-Perot Interferometer
Brillouin microscopy is an emerging optical technique for probing mechanical properties with submicron resolution, offering fully non-contact, label-free operation. Despite its unique capabilities, broader adoption has been limited by slow acquisition speeds, particularly in systems based on scanning Fabry-Perot interferometers (FPIs). Based on prior implementations, FPIs have typically been considered too slow for practical imaging, particularly when both spatial and spectral precision are req…