Tootfinder

Avant sa rencontre avec Donald Trump, Xi Jinping a opté pour la manière forte
https://www.lemonde.fr/international/article/2025/10/29/avant-sa-rencontre-avec-donald-trump-xi-jinping-a-opte-p…

Avant sa rencontre avec Donald Trump, Xi Jinping a opté pour la manière forte
Le président chinois pense avoir appris de ses échecs face à son homologue américain, qu’il retrouvera, jeudi, en Corée du Sud. Il a notamment utilisé les terres rares, indispensables dans les nouvelles technologies, comme puissant levier de négociation.

Research in Medicine: Journal ranking vs. Interdisciplinarity
Anbang Du, Michael Head, Markus Brede
https://arxiv.org/abs/2509.24994 https://arxiv.org/pdf/…

Research in Medicine: Journal ranking vs. Interdisciplinarity
Interdisciplinary research is critical for innovation and addressing complex societal issues. We characterise the interdisciplinary knowledge structure of PubMed research articles in medicine as correlation networks of medical concepts and compare the interdisciplinarity of articles between high-ranking (impactful) and less high-ranking (less impactful) medical journals. We found that impactful medical journals tend to publish research that are less interdisciplinary than less impactful journal…

Ransomware 3.0: Self-Composing and LLM-Orchestrated
Md Raz, Meet Udeshi, P. V. Sai Charan, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri
https://arxiv.org/abs/2508.20444

Ransomware 3.0: Self-Composing and LLM-Orchestrated
Using automated reasoning, code synthesis, and contextual decision-making, we introduce a new threat that exploits large language models (LLMs) to autonomously plan, adapt, and execute the ransomware attack lifecycle. Ransomware 3.0 represents the first threat model and research prototype of LLM-orchestrated ransomware. Unlike conventional malware, the prototype only requires natural language prompts embedded in the binary; malicious code is synthesized dynamically by the LLM at runtime, yieldi…

Preference-Based Dynamic Ranking Structure Recognition
Nan Lu, Jian Shi, Xin-Yu Tian
https://arxiv.org/abs/2509.24493 https://arxiv.org/pdf/2509.24493

Preference-Based Dynamic Ranking Structure Recognition
Preference-based data often appear complex and noisy but may conceal underlying homogeneous structures. This paper introduces a novel framework of ranking structure recognition for preference-based data. We first develop an approach to identify dynamic ranking groups by incorporating temporal penalties into a spectral estimation for the celebrated Bradley-Terry model. To detect structural changes, we introduce an innovative objective function and present a practicable algorithm based on dynamic…

Reimagining Agent-based Modeling with Large Language Model Agents via Shachi
So Kuroki, Yingtao Tian, Kou Misaki, Takashi Ikegami, Takuya Akiba, Yujin Tang
https://arxiv.org/abs/2509.21862

Reimagining Agent-based Modeling with Large Language Model Agents via Shachi
The study of emergent behaviors in large language model (LLM)-driven multi-agent systems is a critical research challenge, yet progress is limited by a lack of principled methodologies for controlled experimentation. To address this, we introduce Shachi, a formal methodology and modular framework that decomposes an agent's policy into core cognitive components: Configuration for intrinsic traits, Memory for contextual persistence, and Tools for expanded capabilities, all orchestrated by an LLM …

Language-Enhanced Mobile Manipulation for Efficient Object Search in Indoor Environments
Liding Zhang, Zeqi Li, Kuanqi Cai, Qian Huang, Zhenshan Bing, Alois Knoll
https://arxiv.org/abs/2508.20899

Language-Enhanced Mobile Manipulation for Efficient Object Search in Indoor Environments
Enabling robots to efficiently search for and identify objects in complex, unstructured environments is critical for diverse applications ranging from household assistance to industrial automation. However, traditional scene representations typically capture only static semantics and lack interpretable contextual reasoning, limiting their ability to guide object search in completely unfamiliar settings. To address this challenge, we propose a language-enhanced hierarchical navigation framework …

Safety-Critical Input-Constrained Nonlinear Intercept Guidance in Multiple Engagement Zones
Praveen Kumar Ranjan, Abhinav Sinha, Yongcan Cao
https://arxiv.org/abs/2509.25053 htt…

Safety-Critical Input-Constrained Nonlinear Intercept Guidance in Multiple Engagement Zones
This paper presents an input-constrained nonlinear guidance law to address the problem of intercepting a stationary target in contested environments with multiple defending agents. Contrary to prior approaches that rely on explicit knowledge of defender strategies or utilize conservative safety conditions based on a defender's range, our work characterizes defender threats geometrically through engagement zones that delineate inevitable interception regions. Outside these engagement zones, the …

A Python Interface for Numerical Relativity Program AMSS-NCKU
Chen-Kai Qiao, Yi Zheng, Zhou-Jian Cao
https://arxiv.org/abs/2509.21652 https://arxiv.org/pdf…

A Python Interface for Numerical Relativity Program AMSS-NCKU
Numerical relativity has produced wide-ranging influences on modern astrophysics and gravitational-wave astronomy. In this work, we develop a Python interface for the numerical relativity program AMSS-NCKU, which enables the automation of initializing and starting the AMSS-NCKU simulations and the automatically generating visualizations of the output results. Numerical relativity simulations using this Python interface have been presented through two representative examples (binary back hole an…

🍛 From space science to dinner plates: Reimagining the future of farming indoors
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From space science to dinner plates: Reimagining the future of farming indoors
Extreme weather events, from heavy rainfall to heat waves and droughts, are increasingly threatening crop yields globally, so new solutions are needed for agriculture.

AWorld: Orchestrating the Training Recipe for Agentic AI
Chengyue Yu, Siyuan Lu, Chenyi Zhuang, Dong Wang, Qintong Wu, Zongyue Li, Runsheng Gan, Chunfeng Wang, Siqi Hou, Gaochi Huang, Wenlong Yan, Lifeng Hong, Aohui Xue, Yanfeng Wang, Jinjie Gu, David Tsai, Tao Lin
https://arxiv.org/abs/2508.20404 …

AWorld: Orchestrating the Training Recipe for Agentic AI
The learning from practice paradigm is crucial for developing capable Agentic AI systems, yet it is severely hampered by inefficient experience generation, a bottleneck especially pronounced in complex benchmarks like GAIA. To address this, we introduce AWorld, an open-source system engineered for large-scale agent-environment interaction. By distributing tasks across a cluster, AWorld accelerates experience collection by 14.6x compared to standard single-node, sequential execution. This critic…