Tootfinder

The WISSH quasar project. XII. X-ray view of the most luminous quasi-stellar objects at Cosmic Noon
C. Degli Agosti, C. Vignali, E. Piconcelli, L. Zappacosta, E. Bertola, R. Middei, I. Saccheo, G. Vietri, F. Vito, A. Bongiorno, M. Bischetti, G. Bruni, S. Carniani, G. Cresci, C. Feruglio, F. Salvestrini, A. Travascio, M. Gaspari, E. Glikman, E. Kammoun, G. Lanzuisi, M. Laurenti, G. Miniutti, C. Pinto, V. Testa, F. Tombesi, A. Tortosa, F. Fiore

The WISSH quasar project. XII. X-ray view of the most luminous quasi-stellar objects at Cosmic Noon
To improve our knowledge of nuclear emission in luminous QSOs at Cosmic Noon, we studied the X-ray emission of the WISE/SDSS-selected hyper-luminous (WISSH) QSO sample: 85 broad-line AGN with $L_{bol}>few\times 10^{47}\,erg\,s^{-1}$ at $z\sim 2-4$. Our aim is to characterise their X-ray spectra and explore relations between X-ray luminosity and other bands, comparing powerful QSOs with the general AGN population. We performed spectral analysis for about half of the sample; 16 sources were analy…

Classification for 969 double-mode RR Lyrae stars from Zwicky Transient Facility
Jianxing Zhang, Xiaodian Chen, Shu Wang, Jiyu Wang, Licai Deng
https://arxiv.org/abs/2510.07705 …

Classification for 969 double-mode RR Lyrae stars from Zwicky Transient Facility
RR Lyrae (RRL) variable stars are cornerstone distance indicators. In particular, double-mode RR Lyrae (RRd) stars enable period--luminosity relations (PLRs) that are less sensitive to metallicity, reducing systematic biases in distance measurements. However, their utility has been limited by a global sample of only $\sim$3,000 objects. We develop an automated RRd-screening pipeline and apply it to a cross-matched sample between the Gaia DR3 RRL catalog and ZTF DR22 time-series photometry. The …

SpinBench: Perspective and Rotation as a Lens on Spatial Reasoning in VLMs
Yuyou Zhang, Radu Corcodel, Chiori Hori, Anoop Cherian, Ding Zhao
https://arxiv.org/abs/2509.25390 htt…

SpinBench: Perspective and Rotation as a Lens on Spatial Reasoning in VLMs
We present SpinBench, a cognitively grounded diagnostic benchmark for evaluating spatial reasoning in vision language models (VLMs). SpinBench is designed around the core challenge of spatial reasoning: perspective taking, the ability to reason about how scenes and object relations change under viewpoint transformation. Since perspective taking requires multiple cognitive capabilities, such as recognizing objects across views, relative positions grounding, and mentally simulating transformation…

Kinds of preservations for properties
Temurboy E. Rajabov, Sergey V. Sudoplatov
https://arxiv.org/abs/2509.21876 https://arxiv.org/pdf/2509.21876

Kinds of preservations for properties
We study possibilities of preservations for properties, their links and related connections between semantic and syntactic ones, both in general and as characterizations for subalgebras, congruence relations, Henkin construction, Tarski-Vaught test, some graphs, and graded structures. Traces for types and related objects are studied, too.

Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks
Yunyong Ko, Da Eun Lee, Song Kyung Yu, Sang-Wook Kim
https://arxiv.org/abs/2508.17236 ht…

Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks
Real-world networks have high-order relationships among objects and they evolve over time. To capture such dynamics, many works have been studied in a range of fields. Via an in-depth preliminary analysis, we observe two important characteristics of high-order dynamics in real-world networks: high-order relations tend to (O1) have a structural and temporal influence on other relations in a short term and (O2) periodically re-appear in a long term. In this paper, we propose LINCOLN, a method for…

LLM Trainer: Automated Robotic Data Generating via Demonstration Augmentation using LLMs
Abraham George, Amir Barati Farimani
https://arxiv.org/abs/2509.20070 https://

LLM Trainer: Automated Robotic Data Generating via Demonstration Augmentation using LLMs
We present LLM Trainer, a fully automated pipeline that leverages the world knowledge of Large Language Models (LLMs) to transform a small number of human demonstrations (as few as one) into a large robot dataset for imitation learning. Our approach decomposes demonstration generation into two steps: (1) offline demonstration annotation that extracts keyframes, salient objects, and pose-object relations; and (2) online keypose retargeting that adapts those keyframes to a new scene, given an ini…

On a Second-Order Version of Russellian Theory of Definite Descriptions
Yaroslav Petrukhin
https://arxiv.org/abs/2508.13928 https://arxiv.org/pdf/2508.1392…

On a Second-Order Version of Russellian Theory of Definite Descriptions
Definite descriptions are first-order expressions that denote unique objects. In this paper, we propose a second-order counterpart, designed to refer to unique relations between objects. We investigate this notion within the framework of Russell's theory of definite descriptions. While full second-order logic is incomplete, its fragment defined by Henkin's general models admits completeness. We develop our theory within this fragment and formalize it using a cut-free sequent calculus.

Mitigating Hallucination in Multimodal LLMs with Layer Contrastive Decoding
Bingkui Tong, Jiaer Xia, Kaiyang Zhou
https://arxiv.org/abs/2509.25177 https://…

Mitigating Hallucination in Multimodal LLMs with Layer Contrastive Decoding
Multimodal Large Language Models (MLLMs) have shown impressive perception and reasoning capabilities, yet they often suffer from hallucinations -- generating outputs that are linguistically coherent but inconsistent with the context of the input image, including inaccuracies in objects, attributes, and relations. To address this challenge, we propose a simple approach called Layer Contrastive Decoding (LayerCD). Our design is motivated by the observation that shallow visual features are much mo…

FactorHD: A Hyperdimensional Computing Model for Multi-Object Multi-Class Representation and Factorization
Yifei Zhou, Xuchu Huang, Chenyu Ni, Min Zhou, Zheyu Yan, Xunzhao Yin, Cheng Zhuo
https://arxiv.org/abs/2507.12366

FactorHD: A Hyperdimensional Computing Model for Multi-Object Multi-Class Representation and Factorization
Neuro-symbolic artificial intelligence (neuro-symbolic AI) excels in logical analysis and reasoning. Hyperdimensional Computing (HDC), a promising brain-inspired computational model, is integral to neuro-symbolic AI. Various HDC models have been proposed to represent class-instance and class-class relations, but when representing the more complex class-subclass relation, where multiple objects associate different levels of classes and subclasses, they face challenges for factorization, a crucia…

PENELLOPE VIII: Revisiting empirical relations to measure accretion luminosity
E. Fiorellino, J. M. Alcal\'a, C. F. Manara, C. Pittman, P. \'Abrah\'am, L. Venuti, S. Cabrit, R. Claes, M. Fang, \'A. Kosp\'al, G. Lodato, K. Mauco, {\L}. Tychoniec
https://arxiv.org/abs/2509.21078

PENELLOPE VIII: Revisiting empirical relations to measure accretion luminosity
The accretion luminosity (Lacc) in young, low-mass stars is crucial for understanding stellar formation, but direct measurements are often hindered by limited spectral coverage and challenges in UV-excess modeling. Empirical relations linking Lacc to various accretion tracers are widely used to overcome these limitations. This work revisits these empirical relations using the PENELLOPE dataset, evaluating their applicability across different star-forming regions and to accreting young objects o…

Minimum and maximum mass-luminosity relations for stripped stars
Gautham N. Sabhahit, Jorick S. Vink, Andreas A. C. Sander, Varsha Ramachandran
https://arxiv.org/abs/2508.14161 …

Minimum and maximum mass-luminosity relations for stripped stars
Envelope stripping, whether through single-star wind mass loss or binary mass transfer, is a key evolutionary pathway for the formation of classical Wolf-Rayet stars and lower-mass stripped helium (He) stars. However, to study the evolution of these objects into black holes, neutron stars, and stripped-envelope supernovae, we need appropriate input models for the core-He burning phase without relying on the uncertain evolution into this evolved phase. Reliable mass-luminosity relations (MLRs) f…

Evaluating the Evaluators: Metrics for Compositional Text-to-Image Generation
Seyed Amir Kasaei, Ali Aghayari, Arash Marioriyad, Niki Sepasian, MohammadAmin Fazli, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban
https://arxiv.org/abs/2509.21227

Evaluating the Evaluators: Metrics for Compositional Text-to-Image Generation
Text-image generation has advanced rapidly, but assessing whether outputs truly capture the objects, attributes, and relations described in prompts remains a central challenge. Evaluation in this space relies heavily on automated metrics, yet these are often adopted by convention or popularity rather than validated against human judgment. Because evaluation and reported progress in the field depend directly on these metrics, it is critical to understand how well they reflect human preferences. …