Tootfinder

A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler
Wenxuan Zhang (, Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China), Shuai Li (, Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China), Xinyi Wang (, Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China), Yu Sun (, Department of Bi…

A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler
The Circle of Willis (CoW), vital for ensuring consistent blood flow to the brain, is closely linked to ischemic stroke. Accurate assessment of the CoW is important for identifying individuals at risk and guiding appropriate clinical management. Among existing imaging methods, Transcranial Color-coded Doppler (TCCD) offers unique advantages due to its radiation-free nature, affordability, and accessibility. However, reliable TCCD assessments depend heavily on operator expertise for identifying …

bible_nouns: Bible noun phrases
A network of noun phrases (places and names) in the King James Version of the Bible. Each node is a noun phrase, and an edge exists if the noun phrases co-occur in a Bible verse. Edge weight denotes how often the two words co-occur.
This network has 1773 nodes and 9131 edges.
Tags: Informational, Language, Weighted

Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data
Francisco L\'opez, Eduardo Di Santi, Cl\'ement Lefebvre, Nenad Mijatovic, Michele Pugnaloni, Victor Mart\'in, Kenza Saiah
https://arxiv.org/abs/2508.11693

Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data
Track Circuits (TC) are the main signalling devices used to detect the presence of a train on a rail track. It has been used since the 19th century and nowadays there are many types depending on the technology. As a general classification, Track Circuits can be divided into 2 main groups, DC (Direct Current) and AC (Alternating Current) circuits. This work is focused on a particular AC track circuit, called "Smart Train Detection System" (STDS), designed with both high and low-frequency bands. …

Mind the Gap: Data Rewriting for Stable Off-Policy Supervised Fine-Tuning
Shiwan Zhao, Xuyang Zhao, Jiaming Zhou, Aobo Kong, Qicheng Li, Yong Qin
https://arxiv.org/abs/2509.15157

Mind the Gap: Data Rewriting for Stable Off-Policy Supervised Fine-Tuning
Supervised fine-tuning (SFT) of large language models can be viewed as an off-policy learning problem, where expert demonstrations come from a fixed behavior policy while training aims to optimize a target policy. Importance sampling is the standard tool for correcting this distribution mismatch, but large policy gaps lead to high variance and training instability. Existing approaches mitigate this issue using KL penalties or clipping, which passively constrain updates rather than actively redu…

Matrix-Game 2.0: An Open-Source, Real-Time, and Streaming Interactive World Model
Xianglong He, Chunli Peng, Zexiang Liu, Boyang Wang, Yifan Zhang, Qi Cui, Fei Kang, Biao Jiang, Mengyin An, Yangyang Ren, Baixin Xu, Hao-Xiang Guo, Kaixiong Gong, Cyrus Wu, Wei Li, Xuchen Song, Yang Liu, Eric Li, Yahui Zhou
https://arxiv.org/abs/2508.13009

Matrix-Game 2.0: An Open-Source, Real-Time, and Streaming Interactive World Model
Recent advances in interactive video generations have demonstrated diffusion model's potential as world models by capturing complex physical dynamics and interactive behaviors. However, existing interactive world models depend on bidirectional attention and lengthy inference steps, severely limiting real-time performance. Consequently, they are hard to simulate real-world dynamics, where outcomes must update instantaneously based on historical context and current actions. To address this, we pr…

From Scores to Skills: A Cognitive Diagnosis Framework for Evaluating Financial Large Language Models
Ziyan Kuang, Feiyu Zhu, Maowei Jiang, Yanzhao Lai, Zelin Wang, Zhitong Wang, Meikang Qiu, Jiajia Huang, Min Peng, Qianqian Xie, Sophia Ananiadou
https://arxiv.org/abs/2508.13491

From Scores to Skills: A Cognitive Diagnosis Framework for Evaluating Financial Large Language Models
Large Language Models (LLMs) have shown promise for financial applications, yet their suitability for this high-stakes domain remains largely unproven due to inadequacies in existing benchmarks. Existing benchmarks solely rely on score-level evaluation, summarizing performance with a single score that obscures the nuanced understanding of what models truly know and their precise limitations. They also rely on datasets that cover only a narrow subset of financial concepts, while overlooking othe…

FURINA: Free from Unmergeable Router via LINear Aggregation of mixed experts
Jiayi Han, Liang Du, Yinda Chen, Xiao Kang, Weiyang Ding, Donghong Han
https://arxiv.org/abs/2509.14900

FURINA: Free from Unmergeable Router via LINear Aggregation of mixed experts
The Mixture of Experts (MoE) paradigm has been successfully integrated into Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning (PEFT), delivering performance gains with minimal parameter overhead. However, a key limitation of existing MoE-LoRA methods is their reliance on a discrete router, which prevents the integration of the MoE components into the backbone model. To overcome this, we propose FURINA, a novel Free from Unmergeable Router framework based on the LINear Aggregation o…

MME-SCI: A Comprehensive and Challenging Science Benchmark for Multimodal Large Language Models
Jiacheng Ruan, Dan Jiang, Xian Gao, Ting Liu, Yuzhuo Fu, Yangyang Kang
https://arxiv.org/abs/2508.13938

MME-SCI: A Comprehensive and Challenging Science Benchmark for Multimodal Large Language Models
Recently, multimodal large language models (MLLMs) have achieved significant advancements across various domains, and corresponding evaluation benchmarks have been continuously refined and improved. In this process, benchmarks in the scientific domain have played an important role in assessing the reasoning capabilities of MLLMs. However, existing benchmarks still face three key challenges: 1) Insufficient evaluation of models' reasoning abilities in multilingual scenarios; 2) Inadequate assess…

A1: Asynchronous Test-Time Scaling via Conformal Prediction
Jing Xiong, Qiujiang Chen, Fanghua Ye, Zhongwei Wan, Chuanyang Zheng, Chenyang Zhao, Hui Shen, Alexander Hanbo Li, Chaofan Tao, Haochen Tan, Haoli Bai, Lifeng Shang, Lingpeng Kong, Ngai Wong
https://arxiv.org/abs/2509.15148

A1: Asynchronous Test-Time Scaling via Conformal Prediction
Large language models (LLMs) benefit from test-time scaling, but existing methods face significant challenges, including severe synchronization overhead, memory bottlenecks, and latency, especially during speculative decoding with long reasoning chains. We introduce A1 (Asynchronous Test-Time Scaling), a statistically guaranteed adaptive inference framework that addresses these challenges. A1 refines arithmetic intensity to identify synchronization as the dominant bottleneck, proposes an online…