Tootfinder

Quantum droplets in curved space
Antonino Flachi, Takahiro Tanaka
https://arxiv.org/abs/2507.13682 https://arxiv.org/pdf/2507.13682…

Quantum droplets in curved space
This paper investigates the formation of quantum droplets in curved spacetime, highlighting the significant influence of curvature on the formation and properties of these objects. While our computations encompass various dimensions, we primarily focus on 2-dimensions. Our findings reveal a novel class of curvature-driven quantum effects leading to the formation of quasi-stable liquid droplets, suggesting a feasible pathway for experimental observation, particularly in microgravity environments.

Gender Inclusivity Fairness Index (GIFI): A Multilevel Framework for Evaluating Gender Diversity in Large Language Models
Zhengyang Shan, Emily Ruth Diana, Jiawei Zhou
https://arxiv.org/abs/2506.15568

Gender Inclusivity Fairness Index (GIFI): A Multilevel Framework for Evaluating Gender Diversity in Large Language Models
We present a comprehensive evaluation of gender fairness in large language models (LLMs), focusing on their ability to handle both binary and non-binary genders. While previous studies primarily focus on binary gender distinctions, we introduce the Gender Inclusivity Fairness Index (GIFI), a novel and comprehensive metric that quantifies the diverse gender inclusivity of LLMs. GIFI consists of a wide range of evaluations at different levels, from simply probing the model with respect to provide…

From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
Xinyang Li, Siqi Liu, Bochao Zou, Jiansheng Chen, Huimin Ma
https://arxiv.org/abs/2506.14224

From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
As large language models evolve, there is growing anticipation that they will emulate human-like Theory of Mind (ToM) to assist with routine tasks. However, existing methods for evaluating machine ToM focus primarily on unimodal models and largely treat these models as black boxes, lacking an interpretative exploration of their internal mechanisms. In response, this study adopts an approach based on internal mechanisms to provide an interpretability-driven assessment of ToM in multimodal large …

RadioDiff-3D: A 3D$\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication
Xiucheng Wang, Qiming Zhang, Nan Cheng, Junting Chen, Zezhong Zhang, Zan Li, Shuguang Cui, Xuemin Shen
https://arxiv.org/abs/2507.12166

RadioDiff-3D: A 3D$\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication
Radio maps (RMs) serve as a critical foundation for enabling environment-aware wireless communication, as they provide the spatial distribution of wireless channel characteristics. Despite recent progress in RM construction using data-driven approaches, most existing methods focus solely on pathloss prediction in a fixed 2D plane, neglecting key parameters such as direction of arrival (DoA), time of arrival (ToA), and vertical spatial variations. Such a limitation is primarily due to the relian…

Heavy Quark Decays in the Bilepton Model
Gennaro Corcella, Claudio Corian\`o, Dario Melle, Paul H. Frampton
https://arxiv.org/abs/2507.12556 https://

Heavy Quark Decays in the Bilepton Model
Given the current absence of new physics signals at the LHC, it is increasingly important to investigate alternative scenarios beyond those commonly explored. In this work, we study a variant of the 331 model that predicts the existence of vector bileptons with electric charge and lepton number +/-2, as well as TeV-scale exotic quarks carrying charges +/- 5/3 and +/- 4/3. Specifically, we focus on the primary production of heavy quarks with charge +/- 5/3, which decay into a bottom quark and a …

MoCap2GT: A High-Precision Ground Truth Estimator for SLAM Benchmarking Based on Motion Capture and IMU Fusion
Zichao Shu, Shitao Bei, Jicheng Dai, Lijun Li, Zetao Chen
https://arxiv.org/abs/2507.12920

MoCap2GT: A High-Precision Ground Truth Estimator for SLAM Benchmarking Based on Motion Capture and IMU Fusion
Marker-based optical motion capture (MoCap) systems are widely used to provide ground truth (GT) trajectories for benchmarking SLAM algorithms. However, the accuracy of MoCap-based GT trajectories is mainly affected by two factors: spatiotemporal calibration errors between the MoCap system and the device under test (DUT), and inherent MoCap jitter. Consequently, existing benchmarks focus primarily on absolute translation error, as accurate assessment of rotation and inter-frame errors remains c…

Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
Hyeonseo Lee, Juhyun Park, Jihyong Oh, Chanho Eom
https://arxiv.org/abs/2506.12413

Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
Person Re-identification (ReID) aims to retrieve images of the same individual captured across non-overlapping camera views, making it a critical component of intelligent surveillance systems. Traditional ReID methods assume that the training and test domains share similar characteristics and primarily focus on learning discriminative features within a given domain. However, they often fail to generalize to unseen domains due to domain shifts caused by variations in viewpoint, background, and l…

Beyond One-Size-Fits-All: A Study of Neural and Behavioural Variability Across Different Recommendation Categories
Georgios Koutroumpas, Sebastian Idesis, Mireia Masias Bruns, Carlos Segura, Joemon M. Jose, Sergi Abadal, Ioannis Arapakis
https://arxiv.org/abs/2506.13409

Beyond One-Size-Fits-All: A Study of Neural and Behavioural Variability Across Different Recommendation Categories
Traditionally, Recommender Systems (RS) have primarily measured performance based on the accuracy and relevance of their recommendations. However, this algorithmic-centric approach overlooks how different types of recommendations impact user engagement and shape the overall quality of experience. In this paper, we shift the focus to the user and address for the first time the challenge of decoding the neural and behavioural variability across distinct recommendation categories, considering more…

Full distribution of the number of distinct sites visited by a random walker in dimension $d \ge 2$
Naftali R. Smith
https://arxiv.org/abs/2506.11235 https…

Full distribution of the number of distinct sites visited by a random walker in dimension $d \ge 2$
We study the full distribution $P_M(S)$ of the number of distinct sites $S$ visited by a random walker on a $d$-dimensional lattice after $M$ steps. We focus on the case $d \ge 2$, and we are interested in the long-time limit $M \gg 1$. Our primary interest is the behavior of the right and left tails of $P_M(S)$, corresponding to $S$ larger and smaller than its mean value, respectively. We present theoretical arguments that predict that in the right tail, a standard large-deviation principle (L…

AcademicBrowse: Benchmarking Academic Browse Ability of LLMs
Junting Zhou, Wang Li, Yiyan Liao, Nengyuan Zhang, Tingjia Miaoand Zhihui Qi, Yuhan Wu, Tong Yang
https://arxiv.org/abs/2506.13784

AcademicBrowse: Benchmarking Academic Browse Ability of LLMs
Large Language Models (LLMs)' search capabilities have garnered significant attention. Existing benchmarks, such as OpenAI's BrowseComp, primarily focus on general search scenarios and fail to adequately address the specific demands of academic search. These demands include deeper literature tracing and organization, professional support for academic databases, the ability to navigate long-tail academic knowledge, and ensuring academic rigor. Here, we proposed AcademicBrowse, the first dataset …