Tootfinder

PhysUniBench: An Undergraduate-Level Physics Reasoning Benchmark for Multimodal Models
Lintao Wang, Encheng Su, Jiaqi Liu, Pengze Li, Peng Xia, Jiabei Xiao, Wenlong Zhang, Xinnan Dai, Xi Chen, Yuan Meng, Mingyu Ding, Lei Bai, Wanli Ouyang, Shixiang Tang, Aoran Wang, Xinzhu Ma
https://arxiv.org/abs/2506.17667

PhysUniBench: An Undergraduate-Level Physics Reasoning Benchmark for Multimodal Models
Physics problem-solving is a challenging domain for large AI models, requiring integration of conceptual understanding, mathematical reasoning, and interpretation of physical diagrams. Current evaluation methodologies show notable limitations in capturing the breadth and complexity of undergraduate-level physics, underscoring the need for more rigorous assessments. To this end, we present PhysUniBench, a large-scale multimodal benchmark designed to evaluate and improve the reasoning capabilitie…

A Proof of the Hodge Conjecture for a Special Class of Calabi-Yau 5-Folds via Exceptional Group Constraints
Dongzhe Zheng
https://arxiv.org/abs/2506.17754 …

A Proof of the Hodge Conjecture for a Special Class of Calabi-Yau 5-Folds via Exceptional Group Constraints
This paper provides a complete proof of the (1,1)-type Hodge conjecture for a special class of 5-dimensional Calabi-Yau manifolds ($h^{1,1}=56$), while establishing and implementing a novel methodology based on exceptional group constraints. The core idea of this methodology is to replace the "geometric construction" that traditional methods rely on -- which becomes infeasible in high-dimensional cases -- with the "algebraic rigidity" of Lie algebra representation theory. We prove that by intro…

Exploring the Potential of LLMs for Serendipity Evaluation in Recommender Systems
Li Kang, Yuhan Zhao, Li Chen
https://arxiv.org/abs/2507.17290 https://arx…

Exploring the Potential of LLMs for Serendipity Evaluation in Recommender Systems
Serendipity plays a pivotal role in enhancing user satisfaction within recommender systems, yet its evaluation poses significant challenges due to its inherently subjective nature and conceptual ambiguity. Current algorithmic approaches predominantly rely on proxy metrics for indirect assessment, often failing to align with real user perceptions, thus creating a gap. With large language models (LLMs) increasingly revolutionizing evaluation methodologies across various human annotation tasks, we…

Investigating State-of-the-Art Planning Strategies for Electric Vehicle Charging Infrastructures in Coupled Transport and Power Networks: A Comprehensive Review
Jinhao Li, Arlena Chew, Hao Wang
https://arxiv.org/abs/2507.17277

Investigating State-of-the-Art Planning Strategies for Electric Vehicle Charging Infrastructures in Coupled Transport and Power Networks: A Comprehensive Review
Electric vehicles (EVs) have emerged as a pivotal solution to reduce greenhouse gas emissions paving a pathway to net zero. As the adoption of EVs continues to grow, countries are proactively formulating systematic plans for nationwide electric vehicle charging infrastructure (EVCI) to keep pace with the accelerating shift towards EVs. This comprehensive review aims to thoroughly examine current global practices in EVCI planning and explore state-of-the-art methodologies for designing EVCI plan…

Data-Driven Adaptive Gradient Recovery for Unstructured Finite Volume Computations
G. de Rom\'emont, F. Renac, F. Chinesta, J. Nunez, D. Gueyffier
https://arxiv.org/abs/2507.16571

Data-Driven Adaptive Gradient Recovery for Unstructured Finite Volume Computations
We present a novel data-driven approach for enhancing gradient reconstruction in unstructured finite volume methods for hyperbolic conservation laws, specifically for the 2D Euler equations. Our approach extends previous structured-grid methodologies to unstructured meshes through a modified DeepONet architecture that incorporates local geometry in the neural network. The architecture employs local mesh topology to ensure rotation invariance, while also ensuring first-order constraint on the le…

ETrace:Event-Driven Vulnerability Detection in Smart Contracts via LLM-Based Trace Analysis
Chenyang Peng, Haijun Wang, Yin Wu, Hao Wu, Ming Fan, Yitao Zhao, Ting Liu
https://arxiv.org/abs/2506.15790

ETrace:Event-Driven Vulnerability Detection in Smart Contracts via LLM-Based Trace Analysis
With the advance application of blockchain technology in various fields, ensuring the security and stability of smart contracts has emerged as a critical challenge. Current security analysis methodologies in vulnerability detection can be categorized into static analysis and dynamic analysis methods.However, these existing traditional vulnerability detection methods predominantly rely on analyzing original contract code, not all smart contracts provide accessible code.We present ETrace, a novel…

Skeletal Reaction Models for Gasoline Surrogate Combustion
Yinmin Liu, Hessam Babaee, Peyman Givi, Daniel Livescu, Arash Nouri
https://arxiv.org/abs/2506.18853

Skeletal Reaction Models for Gasoline Surrogate Combustion
Skeletal reaction models are derived for a four-component gasoline surrogate model via an instantaneous local sensitivity analysis technique. The sensitivities of the species mass fractions and the temperature with respect to the reaction rates are estimated by a reduced-order modeling (ROM) methodology. Termed "implicit time-dependent basis CUR (implicit TDB-CUR)," this methodology is based on the CUR matrix decomposition and incorporates implicit time integration for evolving the bases. The e…

LLM-Driven Self-Refinement for Embodied Drone Task Planning
Deyu Zhang, Xicheng Zhang, Jiahao Li, Tingting Long, Xunhua Dai, Yongjian Fu, Jinrui Zhang, Ju Ren, Yaoxue Zhang
https://arxiv.org/abs/2508.15501

LLM-Driven Self-Refinement for Embodied Drone Task Planning
We introduce SRDrone, a novel system designed for self-refinement task planning in industrial-grade embodied drones. SRDrone incorporates two key technical contributions: First, it employs a continuous state evaluation methodology to robustly and accurately determine task outcomes and provide explanatory feedback. This approach supersedes conventional reliance on single-frame final-state assessment for continuous, dynamic drone operations. Second, SRDrone implements a hierarchical Behavior Tree…

Wide-spectrum security of quantum key distribution
Hao Tan, Mikhail Petrov, Weiyang Zhang, Liying Han, Sheng-Kai Liao, Vadim Makarov, Feihu Xu, Jian-Wei Pan
https://arxiv.org/abs/2508.15136

Wide-spectrum security of quantum key distribution
Implementations of quantum key distribution (QKD) need vulnerability assessment against loopholes in their optical scheme. Most of the optical attacks involve injecting or receiving extraneous light via the communication channel. An eavesdropper can choose her attack wavelengths arbitrarily within the quantum channel passband to maximise the attack performance, exploiting spectral transparency windows of system components. Here we propose a wide-spectrum security evaluation methodology to achie…

Neumann series of Bessel functions in direct and inverse spherically symmetric transmission eigenvalue problems
Vladislav V. Kravchenko, L. Estefania Murcia-Lozano, Nikolaos Pallikarakis
https://arxiv.org/abs/2507.16554

Neumann series of Bessel functions in direct and inverse spherically symmetric transmission eigenvalue problems
The transmission eigenvalue problem (TEP) plays a central role in inverse scattering theory. Despite substantial theoretical progress, the numerical solution of direct and inverse TEP in spherically symmetric domains with variable refractive index covering real and complex eigenvalues remains challenging. This study introduces a novel Neumann Series of Bessel Functions (NSBF) methodology to address this challenge. After reformulating the TEP as a Sturm-Liouville equation via a Liouville transfo…