Tootfinder

Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution
Haosong Liu, Xiancheng Zhu, Huanqiang Zeng, Jianqing Zhu, Jiuwen Cao, Junhui Hou
https://arxiv.org/abs/2509.04824

Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution
Recently, Mamba-based methods, with its advantage in long-range information modeling and linear complexity, have shown great potential in optimizing both computational cost and performance of light field image super-resolution (LFSR). However, current multi-directional scanning strategies lead to inefficient and redundant feature extraction when applied to complex LF data. To overcome this challenge, we propose a Subspace Simple Scanning (Sub-SS) strategy, based on which we design the Subspace …

Adversarial Decision-Making in Partially Observable Multi-Agent Systems: A Sequential Hypothesis Testing Approach
Haosheng Zhou, Daniel Ralston, Xu Yang, Ruimeng Hu
https://arxiv.org/abs/2509.03727

Adversarial Decision-Making in Partially Observable Multi-Agent Systems: A Sequential Hypothesis Testing Approach
Adversarial decision-making in partially observable multi-agent systems requires sophisticated strategies for both deception and counter-deception. This paper presents a sequential hypothesis testing (SHT)-driven framework that captures the interplay between strategic misdirection and inference in adversarial environments. We formulate this interaction as a partially observable Stackelberg game, where a follower agent (blue team) seeks to fulfill its primary task while actively misleading an ad…

Prompt-based Multimodal Semantic Communication for Multi-spectral Image Segmentation
Haoshuo Zhang, Yufei Bo, Hongwei Zhang, Meixia Tao
https://arxiv.org/abs/2508.17920 https://…

Prompt-based Multimodal Semantic Communication for Multi-spectral Image Segmentation
Multimodal semantic communication has gained widespread attention due to its ability to enhance downstream task performance. A key challenge in such systems is the effective fusion of features from different modalities, which requires the extraction of rich and diverse semantic representations from each modality. To this end, we propose ProMSC-MIS, a Prompt-based Multimodal Semantic Communication system for Multi-spectral Image Segmentation. Specifically, we propose a pre-training algorithm whe…

ProMSC-MIS: Prompt-based Multimodal Semantic Communication for Multi-Spectral Image Segmentation
Haoshuo Zhang, Yufei Bo, Meixia Tao
https://arxiv.org/abs/2508.20057 https://

ProMSC-MIS: Prompt-based Multimodal Semantic Communication for Multi-Spectral Image Segmentation
Multimodal semantic communication has great potential to enhance downstream task performance by integrating complementary information across modalities. This paper introduces ProMSC-MIS, a novel Prompt-based Multimodal Semantic Communication framework for Multi-Spectral Image Segmentation. It enables efficient task-oriented transmission of spatially aligned RGB and thermal images over band-limited channels. Our framework has two main design novelties. First, by leveraging prompt learning and co…

Feature Request Analysis and Processing: Tasks, Techniques, and Trends
Feifei Niu, Chuanyi Li, Haosheng Zuo, Jionghan Wu, Xin Xia
https://arxiv.org/abs/2508.12436 https://

Feature Request Analysis and Processing: Tasks, Techniques, and Trends
Feature requests are proposed by users to request new features or enhancements of existing features of software products, which represent users' wishes and demands. Satisfying users' demands can benefit the product from both competitiveness and user satisfaction. Feature requests have seen a rise in interest in the past few years and the amount of research has been growing. However, the diversity in the research topics suggests the need for their collective analysis to identify the challenges a…

Leveraging Multiple Speech Enhancers for Non-Intrusive Intelligibility Prediction for Hearing-Impaired Listeners
Boxuan Cao, Linkai Li, Hanlin Yu, Changgeng Mo, Haoshuai Zhou, Shan Xiang Wang
https://arxiv.org/abs/2509.16979

Leveraging Multiple Speech Enhancers for Non-Intrusive Intelligibility Prediction for Hearing-Impaired Listeners
Speech intelligibility evaluation for hearing-impaired (HI) listeners is essential for assessing hearing aid performance, traditionally relying on listening tests or intrusive methods like HASPI. However, these methods require clean reference signals, which are often unavailable in real-world conditions, creating a gap between lab-based and real-world assessments. To address this, we propose a non-intrusive intelligibility prediction framework that leverages speech enhancers to provide a parall…

PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts
Mo Yu, Tsz Ting Chung, Chulun Zhou, Tong Li, Rui Lu, Jiangnan Li, Liyan Xu, Haoshu Lu, Ning Zhang, Jing Li, Jie Zhou
https://arxiv.org/abs/2508.09848

PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts
We introduce PRELUDE, a benchmark for evaluating long-context understanding through the task of determining whether a character's prequel story is consistent with the canonical narrative of the original book. Our task poses a stronger demand for global comprehension and deep reasoning than existing benchmarks -- as the prequels are not part of the original story, assessing their plausibility typically requires searching and integrating information that is only indirectly related. Empirically, 8…

Submarine Cable Deep-Ocean Observation of Mega-Thrust Earthquake and Tsunami with 44,000 100-m Spaced Sensors
Mikael Mazur, Nicolas K. Fontaine, Roland Ryf, Martin Karrenbach, Keith L. McLaughlin, Berry J. Sperry, Anuar G. Butler, Valey Kamalov, Lauren Dallachiesa, Ellsworth Burrows, David Winter, Haoshuo Chen, Jeewan Naik, Kishore Padmaraju, Ajay Mistry, David T. Neilson

Submarine Cable Deep-Ocean Observation of Mega-Thrust Earthquake and Tsunami with 44,000 100-m Spaced Sensors
We detect the recent M8.8 mega-earthquake in Eastern Russia, on a 4400km long active telecom cable in the Pacific Ocean. The resolution achieved 100m represents the highest spatial resolution, the largest number of ocean-bottom sensors, and the first fiber-optic deep-ocean observation of a tsunami wave.

MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer
Yanghao Li, Rui Qian, Bowen Pan, Haotian Zhang, Haoshuo Huang, Bowen Zhang, Jialing Tong, Haoxuan You, Xianzhi Du, Zhe Gan, Hyunjik Kim, Chao Jia, Zhenbang Wang, Yinfei Yang, Mingfei Gao, Zi-Yi Dou, Wenze Hu, Chang Gao, Dongxu Li, Philipp Dufter, Zirui Wang, Guoli Yin, Zhengdong Zhang, Chen Chen, Yang Zhao, Ruoming Pang, Zhifeng Chen

MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer
Unified multimodal Large Language Models (LLMs) that can both understand and generate visual content hold immense potential. However, existing open-source models often suffer from a performance trade-off between these capabilities. We present Manzano, a simple and scalable unified framework that substantially reduces this tension by coupling a hybrid image tokenizer with a well-curated training recipe. A single shared vision encoder feeds two lightweight adapters that produce continuous embeddi…

Learning Mean-Field Games through Mean-Field Actor-Critic Flow
Mo Zhou, Haosheng Zhou, Ruimeng Hu
https://arxiv.org/abs/2510.12180 https://arxiv.org/pdf/25…

Learning Mean-Field Games through Mean-Field Actor-Critic Flow
We propose the Mean-Field Actor-Critic (MFAC) flow, a continuous-time learning dynamics for solving mean-field games (MFGs), combining techniques from reinforcement learning and optimal transport. The MFAC framework jointly evolves the control (actor), value function (critic), and distribution components through coupled gradient-based updates governed by partial differential equations (PDEs). A central innovation is the Optimal Transport Geodesic Picard (OTGP) flow, which drives the distributio…