Tootfinder

Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning
Haoran Luo, Haihong E, Guanting Chen, Qika Lin, Yikai Guo, Fangzhi Xu, Zemin Kuang, Meina Song, Xiaobao Wu, Yifan Zhu, Luu Anh Tuan
https://arxiv.org/abs/2507.21892

Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning
Retrieval-Augmented Generation (RAG) mitigates hallucination in LLMs by incorporating external knowledge, but relies on chunk-based retrieval that lacks structural semantics. GraphRAG methods improve RAG by modeling knowledge as entity-relation graphs, but still face challenges in high construction cost, fixed one-time retrieval, and reliance on long-context reasoning and prompt design. To address these challenges, we propose Graph-R1, an agentic GraphRAG framework via end-to-end reinforcement …

Joint Contact Planning for Navigation and Communication in GNSS-Libration Point Systems
Huan Yan, Juan A. Fraire, Ziqi Yang, Kanglian Zhao, Wenfeng Li, Xiyun Hou, Haohan Li, Yuxuan Miao, Jinjun Zheng, Chengbin Kang, Huichao Zhou, Xinuo Chang, Lu Wang, Linshan Xue
https://arxiv.org/abs/2508.20479

Joint Contact Planning for Navigation and Communication in GNSS-Libration Point Systems
Deploying satellites at Earth-Moon Libration Points (LPs) addresses the inherent deep-space coverage gaps of low-altitude GNSS constellations. Integrating LP satellites with GNSS into a joint constellation enables a more robust and comprehensive Positioning, Navigation, and Timing (PNT) system, while also extending navigation and communication services to spacecraft operating in cislunar space (i.e., users). However, the long propagation delays between LP satellites, users, and GNSS satellites …

GeoJSEval: An Automated Evaluation Framework for Large Language Models on JavaScript-Based Geospatial Computation and Visualization Code Generation
Guanyu Chen, Haoyue Jiao, Shuyang Hou, Ziqi Liu, Lutong Xie, Shaowen Wu, Huayi Wu, Xuefeng Guan, Zhipeng Gui
https://arxiv.org/abs/2507.20553

GeoJSEval: An Automated Evaluation Framework for Large Language Models on JavaScript-Based Geospatial Computation and Visualization Code Generation
With the widespread adoption of large language models (LLMs) in code generation tasks, geospatial code generation has emerged as a critical frontier in the integration of artificial intelligence and geoscientific analysis. This trend underscores the urgent need for systematic evaluation methodologies to assess LLMs generation capabilities in geospatial contexts. In particular, geospatial computation and visualization tasks in JavaScript environments rely heavily on orchestrating diverse fronten…

X-ray Emission Spectropolarimetry of Strongly Anisotropic Single Crystal Systems using a Rowland Circle Geometry
Jared E. Abramson, Charles A. Cardot, Josh J. Kas, John J. Rehr, Werner Kaminsky, Herwig Michor, Marta Roman, Petra Becker, Gerald T. Seidler
https://arxiv.org/abs/2507.18839

X-ray Emission Spectropolarimetry of Strongly Anisotropic Single Crystal Systems using a Rowland Circle Geometry
Polarization dependence has historically seen extensive use in x-ray spectroscopy to determine magnetic and local geometric properties, but more broadly as a way to gain extra sensitivity to electronic structure at the level of individual magnetic orbitals. This is often done in the context of x-ray absorption through techniques like x-ray magnetic circular dichroism or x-ray linear dichroism, but it has seen little application to x-ray emission. Here we explore the information contained in the…

The NIKA2 cosmological legacy survey at 2 mm: catalogs, colors, redshift distributions, and implications for deep surveys
M. B\'ethermin, G. Lagache, C. Carvajal-Bohorquez, R. Adam, P. Ade, H. Ajeddig, S. Amarantidis, P. Andr\'e, H. Aussel, A. Beelen, A. Beno\^it, S. Berta, L. J. Bing, A. Bongiovanni, J. Bounmy, O. Bourrion, M. Calvo, A. Catalano, D. Ch\'erouvrier, M. De Petris, F. -X. D\'esert, S. Doyle, E. F. C. Driessen, G. Ejlali, A. Ferragamo, A. Gomez, J. Goupy, C…

The NIKA2 cosmological legacy survey at 2 mm: catalogs, colors, redshift distributions, and implications for deep surveys
Millimeter galaxy surveys are particularly effective in detecting dusty star-forming galaxies at high redshift. While such observations are typically conducted at ~1mm, studies suggest that 2mm may be better suited for selecting sources at even higher redshifts. We use the unprecedented 2mm data from the N2CLS, together with the SIDES simulation, to study and interpret the statistical properties of 2mm-selected galaxies. We use the N2CLS robust sample at 2mm, which contains 25 sources in the de…

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Reza Yousefi Maragheh, Pratheek Vadla, Priyank Gupta, Kai Zhao, Aysenur Inan, Kehui Yao, Jianpeng Xu, Praveen Kanumala, Jason Cho, Sushant Kumar
https://arxiv.org/abs/2506.21931

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Retrieval-Augmented Generation (RAG) has shown promise in enhancing recommendation systems by incorporating external context into large language model prompts. However, existing RAG-based approaches often rely on static retrieval heuristics and fail to capture nuanced user preferences in dynamic recommendation scenarios. In this work, we introduce ARAG, an Agentic Retrieval-Augmented Generation framework for Personalized Recommendation, which integrates a multi-agent collaboration mechanism int…

Knot-Driven Spin Selectivity: Topological Chirality-Induced Robust Spin Polarization in Molecular Knots
Xi Sun, Kai-Yuan Zhang, Shu-Zheng Zhou, Hua-Hua Fu
https://arxiv.org/abs/2507.20172

Knot-Driven Spin Selectivity: Topological Chirality-Induced Robust Spin Polarization in Molecular Knots
Compared to traditional structural chiral materials (e.g., DNA, helicene), topological chirality in trefoil knot molecules has demonstrated multiple remarkable advantages in chirality-induced spin selectivity (CISS), including ultra-high spin polarization of nearly 90%, conductivity increased by two orders of magnitude, and high-temperature stability (up to 350$^{\circ}$C). However, the underlying physical mechanism remains elusive. This work establishes, for the first time, a fundamental theor…

The Double-edged Sword of LLM-based Data Reconstruction: Understanding and Mitigating Contextual Vulnerability in Word-level Differential Privacy Text Sanitization
Stephen Meisenbacher, Alexandra Klymenko, Andreea-Elena Bodea, Florian Matthes
https://arxiv.org/abs/2508.18976

The Double-edged Sword of LLM-based Data Reconstruction: Understanding and Mitigating Contextual Vulnerability in Word-level Differential Privacy Text Sanitization
Differentially private text sanitization refers to the process of privatizing texts under the framework of Differential Privacy (DP), providing provable privacy guarantees while also empirically defending against adversaries seeking to harm privacy. Despite their simplicity, DP text sanitization methods operating at the word level exhibit a number of shortcomings, among them the tendency to leave contextual clues from the original texts due to randomization during sanitization $\unicode{x2013}$…

ChartMark: A Structured Grammar for Chart Annotation
Yiyu Chen, Yifan Wu, Shuyu Shen, Yupeng Xie, Leixian Shen, Hui Xiong, Yuyu Luo
https://arxiv.org/abs/2507.21810 https://

ChartMark: A Structured Grammar for Chart Annotation
Chart annotations enhance visualization accessibility but suffer from fragmented, non-standardized representations that limit cross-platform reuse. We propose ChartMark, a structured grammar that separates annotation semantics from visualization implementations. ChartMark features a hierarchical framework mapping onto annotation dimensions (e.g., task, chart context), supporting both abstract intents and precise visual details. Our toolkit demonstrates converting ChartMark specifications into V…

Predicting Maintenance Cessation of Open Source Software Repositories with An Integrated Feature Framework
Yiming Xu, Runzhi He, Hengzhi Ye, Minghui Zhou, Huaimin Wang
https://arxiv.org/abs/2507.21678 …

Predicting Maintenance Cessation of Open Source Software Repositories with An Integrated Feature Framework
The maintenance risks of open source software (OSS) projects pose significant threats to the quality, security, and resilience of modern software supply chains. While prior research has proposed diverse approaches for predicting OSS maintenance risk -- leveraging signals ranging from surface features (e.g., stars, commits) to social network analyses and behavioral patterns -- existing methods often suffer from ambiguous operational definitions, limited interpretability, and datasets of insuffic…