Tootfinder

AIM 2025 Challenge on Real-World RAW Image Denoising
Feiran Li, Jiacheng Li, Marcos V. Conde, Beril Besbinar, Vlad Hosu, Daisuke Iso, Radu Timofte
https://arxiv.org/abs/2510.06601

AIM 2025 Challenge on Real-World RAW Image Denoising
We introduce the AIM 2025 Real-World RAW Image Denoising Challenge, aiming to advance efficient and effective denoising techniques grounded in data synthesis. The competition is built upon a newly established evaluation benchmark featuring challenging low-light noisy images captured in the wild using five different DSLR cameras. Participants are tasked with developing novel noise synthesis pipelines, network architectures, and training methodologies to achieve high performance across different …

FuelCast: Benchmarking Tabular and Temporal Models for Ship Fuel Consumption
Justus Viga, Penelope Mueck, Alexander L\"oser, Torben Weis
https://arxiv.org/abs/2510.08217 ht…

FuelCast: Benchmarking Tabular and Temporal Models for Ship Fuel Consumption
In the shipping industry, fuel consumption and emissions are critical factors due to their significant impact on economic efficiency and environmental sustainability. Accurate prediction of ship fuel consumption is essential for further optimization of maritime operations. However, heterogeneous methodologies and limited high-quality datasets hinder direct comparison of modeling approaches. This paper makes three key contributions: (1) we introduce and release a new dataset (https://huggingface…

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Tianshi Zheng, Kelvin Kiu-Wai Tam, Newt Hue-Nam K. Nguyen, Baixuan Xu, Zhaowei Wang, Jiayang Cheng, Hong Ting Tsang, Weiqi Wang, Jiaxin Bai, Tianqing Fang, Yangqiu Song, Ginny Y. Wong, Simon See
https://arxiv.org/abs/2510.07172

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Large language models are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science. However, existing benchmarks for this task suffer from a fundamental methodological trilemma, forcing a trade-off between scientific relevance, scalability, and resistance to memorization. Furthermore, they oversimplify discovery as static function fitting, failing to capture the authentic scientific process of uncovering embedded laws through the interactive explora…

NOWJ@COLIEE 2025: A Multi-stage Framework Integrating Embedding Models and Large Language Models for Legal Retrieval and Entailment
Hoang-Trung Nguyen, Tan-Minh Nguyen, Xuan-Bach Le, Tuan-Kiet Le, Khanh-Huyen Nguyen, Ha-Thanh Nguyen, Thi-Hai-Yen Vuong, Le-Minh Nguyen
https://arxiv.org/abs/2509.08025

NOWJ@COLIEE 2025: A Multi-stage Framework Integrating Embedding Models and Large Language Models for Legal Retrieval and Entailment
This paper presents the methodologies and results of the NOWJ team's participation across all five tasks at the COLIEE 2025 competition, emphasizing advancements in the Legal Case Entailment task (Task 2). Our comprehensive approach systematically integrates pre-ranking models (BM25, BERT, monoT5), embedding-based semantic representations (BGE-m3, LLM2Vec), and advanced Large Language Models (Qwen-2, QwQ-32B, DeepSeek-V3) for summarization, relevance scoring, and contextual re-ranking. Specific…

Collaborative Human-Robot Surgery for Mandibular Angle Split Osteotomy: Optical Tracking based Approach
Zhe Han, Huanyu Tian, Tom Vercauteren, Da Liu, Changsheng Li, Xingguang Duan
https://arxiv.org/abs/2507.07794

Collaborative Human-Robot Surgery for Mandibular Angle Split Osteotomy: Optical Tracking based Approach
Mandibular Angle Split Osteotomy (MASO) is a significant procedure in oral and maxillofacial surgery. Despite advances in technique and instrumentation, its success still relies heavily on the surgeon's experience. In this work, a human-robot collaborative system is proposed to perform MASO according to a preoperative plan and under guidance of a surgeon. A task decomposition methodology is used to divide the collaborative surgical procedure into three subtasks: (1) positional control and (2) o…

End-to-End Quantum Algorithm for Topology Optimization in Structural Mechanics
Leonhard H\"olscher, Oliver Ahrend, Lukas Karch, Carlotta L'Estocq, Marc Marfany Andreu, Tobias Stollenwerk, Frank K. Wilhelm, Julia Kowalski
https://arxiv.org/abs/2510.07280

End-to-End Quantum Algorithm for Topology Optimization in Structural Mechanics
Topology optimization is a key methodology in engineering design for finding efficient and robust structures. Due to the enormous size of the design space, evaluating all possible configurations is typically infeasible. In this work, we present an end-to-end, fault-tolerant quantum algorithm for topology optimization that operates on the exponential Hilbert space representing the design space. We demonstrate the algorithm on the two-dimensional Messerschmitt-Bölkow-Blohm (MBB) beam problem. By…

SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models
Hongxing Li, Dingming Li, Zixuan Wang, Yuchen Yan, Hang Wu, Wenqi Zhang, Yongliang Shen, Weiming Lu, Jun Xiao, Yueting Zhuang
https://arxiv.org/abs/2510.08531

SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models
Spatial reasoning remains a fundamental challenge for Vision-Language Models (VLMs), with current approaches struggling to achieve robust performance despite recent advances. We identify that this limitation stems from a critical gap: existing methods attempt to learn spatial reasoning directly without establishing the hierarchical foundations of perception and understanding. To address this challenge, we present a comprehensive methodology for building spatial intelligence progressively. We in…

Automated Creation of the Legal Knowledge Graph Addressing Legislation on Violence Against Women: Resource, Methodology and Lessons Learned
Claudia dAmato, Giuseppe Rubini, Francesco Didio, Donato Francioso, Fatima Zahra Amara, Nicola Fanizzi
https://arxiv.org/abs/2508.06368

Automated Creation of the Legal Knowledge Graph Addressing Legislation on Violence Against Women: Resource, Methodology and Lessons Learned
Legal decision-making process requires the availability of comprehensive and detailed legislative background knowledge and up-to-date information on legal cases and related sentences/decisions. Legal Knowledge Graphs (KGs) would be a valuable tool to facilitate access to legal information, to be queried and exploited for the purpose, and to enable advanced reasoning and machine learning applications. Indeed, legal KGs may act as knowledge intensive component to be used by pre-dictive machine le…

The Carbon Footprint Wizard: A Knowledge-Augmented AI Interface for Streamlining Food Carbon Footprint Analysis
Mustafa Kaan Aslan, Reinout Heijungs, Filip Ilievski
https://arxiv.org/abs/2509.07733

The Carbon Footprint Wizard: A Knowledge-Augmented AI Interface for Streamlining Food Carbon Footprint Analysis
Environmental sustainability, particularly in relation to climate change, is a key concern for consumers, producers, and policymakers. The carbon footprint, based on greenhouse gas emissions, is a standard metric for quantifying the contribution to climate change of activities and is often assessed using life cycle assessment (LCA). However, conducting LCA is complex due to opaque and global supply chains, as well as fragmented data. This paper presents a methodology that combines advances in L…

Multimodal Contrastive Pretraining of CBCT and IOS for Enhanced Tooth Segmentation
Moo Hyun Son, Juyoung Bae, Zelin Qiu, Jiale Peng, Kai Xin Li, Yifan Lin, Hao Chen
https://arxiv.org/abs/2509.07923

Multimodal Contrastive Pretraining of CBCT and IOS for Enhanced Tooth Segmentation
Digital dentistry represents a transformative shift in modern dental practice. The foundational step in this transformation is the accurate digital representation of the patient's dentition, which is obtained from segmented Cone-Beam Computed Tomography (CBCT) and Intraoral Scans (IOS). Despite the growing interest in digital dental technologies, existing segmentation methodologies frequently lack rigorous validation and demonstrate limited performance and clinical applicability. To the best of…