Tootfinder

MST-Distill: Mixture of Specialized Teachers for Cross-Modal Knowledge Distillation
Hui Li, Pengfei Yang, Juanyang Chen, Le Dong, Yanxin Chen, Quan Wang
https://arxiv.org/abs/2507.07015

MST-Distill: Mixture of Specialized Teachers for Cross-Modal Knowledge Distillation
Knowledge distillation as an efficient knowledge transfer technique, has achieved remarkable success in unimodal scenarios. However, in cross-modal settings, conventional distillation methods encounter significant challenges due to data and statistical heterogeneities, failing to leverage the complementary prior knowledge embedded in cross-modal teacher models. This paper empirically reveals two critical issues in existing approaches: distillation path selection and knowledge drift. To address …

Dual Knowledge-Enhanced Two-Stage Reasoner for Multimodal Dialog Systems
Xiaolin Chen, Xuemeng Song, Haokun Wen, Weili Guan, Xiangyu Zhao, Liqiang Nie
https://arxiv.org/abs/2509.07817

Dual Knowledge-Enhanced Two-Stage Reasoner for Multimodal Dialog Systems
Textual response generation is pivotal for multimodal \mbox{task-oriented} dialog systems, which aims to generate proper textual responses based on the multimodal context. While existing efforts have demonstrated remarkable progress, there still exist the following limitations: 1) \textit{neglect of unstructured review knowledge} and 2) \textit{underutilization of large language models (LLMs)}. Inspired by this, we aim to fully utilize dual knowledge (\textit{i.e., } structured attribute and un…

An expanded evaluation matrix for the entropy-weight TODIM method to reduce the rank reversal probability and its application in selecting energy storage technology
Lisheng Jiang, Tianyu Zhang, Shiyu Yan, Ran Fang
https://arxiv.org/abs/2509.08236

An expanded evaluation matrix for the entropy-weight TODIM method to reduce the rank reversal probability and its application in selecting energy storage technology
The TODIM method (an acronym in Portuguese for interactive and multiple criteria decision-making) with entropy weights is influenced by rank reversal, a phenomenon where the order of two alternatives changes following the addition of another alternative. Research on rank reversal has predominantly focused on single decision-making methods. To the best of our knowledge, the reduction of rank reversal probability in hybrid methods, such as the entropy-weight TODIM method, remains an unresolved ch…

R2GenKG: Hierarchical Multi-modal Knowledge Graph for LLM-based Radiology Report Generation
Futian Wang, Yuhan Qiao, Xiao Wang, Fuling Wang, Yuxiang Zhang, Dengdi Sun
https://arxiv.org/abs/2508.03426

R2GenKG: Hierarchical Multi-modal Knowledge Graph for LLM-based Radiology Report Generation
X-ray medical report generation is one of the important applications of artificial intelligence in healthcare. With the support of large foundation models, the quality of medical report generation has significantly improved. However, challenges such as hallucination and weak disease diagnostic capability still persist. In this paper, we first construct a large-scale multi-modal medical knowledge graph (termed M3KG) based on the ground truth medical report using the GPT-4o. It contains 2477 enti…

Geomorphometric atlas of ice-free Antarctic areas: Problem statement, concept, and key principles
Igor V. Florinsky
https://arxiv.org/abs/2508.02846 https://

Geomorphometric atlas of ice-free Antarctic areas: Problem statement, concept, and key principles
In this article, we rationalize the need to develop a geomorphometric atlas of ice-free Antarctic areas. Geomorphometric mapping of such landscapes is necessary to obtain new knowledge about quantitative topographic characteristics of these unique objects and the further use of morphometric information in the geosciences. First, we present a concept, structure, and content of the atlas including a list of 194 areas to be modeled and mapped. For each area, the atlas will include the following ma…

CAI Fluency: A Framework for Cybersecurity AI Fluency
V\'ictor Mayoral-Vilches, Jasmin Wachter, Crist\'obal R. J. Veas Chavez, Cathrin Schachner, Luis Javier Navarrete-Lozano, Mar\'ia Sanz-G\'omez
https://arxiv.org/abs/2508.13588

CAI Fluency: A Framework for Cybersecurity AI Fluency
This work introduces CAI Fluency, an an educational platform of the Cybersecurity AI (CAI) framework dedicated to democratizing the knowledge and application of cybersecurity AI tools in the global security community. The main objective of the CAI framework is to accelerate the widespread adoption and effective use of artificial intelligence-based cybersecurity solutions, pathing the way to vibe-hacking, the cybersecurity analogon to vibe-coding. CAI Fluency builds upon the Framework for AI F…

RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models
Zhaoyan Gong, Juan Li, Zhiqiang Liu, Lei Liang, Huajun Chen, Wen Zhang
https://arxiv.org/abs/2509.03995

RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models
Current temporal knowledge graph question answering (TKGQA) methods primarily focus on implicit temporal constraints, lacking the capability of handling more complex temporal queries, and struggle with limited reasoning abilities and error propagation in decomposition frameworks. We propose RTQA, a novel framework to address these challenges by enhancing reasoning over TKGs without requiring training. Following recursive thinking, RTQA recursively decomposes questions into sub-problems, solves …

LHS in LHS: A new expansion strategy for Latin hypercube sampling in simulation design
Matteo Boschini, Davide Gerosa, Alessandro Crespi, Matteo Falcone
https://arxiv.org/abs/2509.00159

LHS in LHS: A new expansion strategy for Latin hypercube sampling in simulation design
Latin Hypercube Sampling (LHS) is a prominent tool in simulation design, with a variety of applications in high-dimensional and computationally expensive problems. LHS allows for various optimization strategies, most notably to ensure space-filling properties. However, LHS is a single-stage algorithm that requires a priori knowledge of the targeted sample size. In this work, we present LHS in LHS, a new expansion algorithm for LHS that enables the addition of new samples to an existing LHS-dist…

Sources: ByteDance is working on a lightweight MR device that resembles goggles, tethered to a compute puck, and has a team working on the device's custom chips (The Information)
https://www.theinformation.com/articles/bytedance-developin…

ByteDance Developing Mixed Reality Goggles in Challenge to Meta
Competition in mixed reality devices is heating up. TikTok’s parent, ByteDance, is working on a lightweight mixed reality device that resembles a pair of goggles, according to three people with direct knowledge of the project. It’s following in the footsteps of Meta Platforms, which has been ...

A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Ming Hu, Chenglong Ma, Wei Li, Wanghan Xu, Jiamin Wu, Jucheng Hu, Tianbin Li, Guohang Zhuang, Jiaqi Liu, Yingzhou Lu, Ying Chen, Chaoyang Zhang, Cheng Tan, Jie Ying, Guocheng Wu, Shujian Gao, Pengcheng Chen, Jiashi Lin, Haitao Wu, Lulu Chen, Fengxiang Wang, Yuanyuan Zhang, Xiangyu Zhao, Feilong Tang, Encheng Su, Junzhi Ning, Xinyao Liu, Ye Du, Changkai Ji, Cheng Tang, Huihui Xu, Ziyang Chen, Ziyan Hu…

A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, yet their progress is shaped by the complex nature of scientific data. This survey presents a comprehensive, data-centric synthesis that reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate. We formulate a unified taxonomy of scientific data and a hierarchical model of scientific knowledge, emphasizing the…