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🔥 Armed groups, cattle ranchers drove 35% rise in Colombia’s deforestation in 2024
https://news.mongabay.com/2025/04/armed-groups-cattle-ranchers-drove-35-rise-in-colombias-deforestation-in-2024/

Armed groups, cattle ranchers drove 35% rise in Colombia’s deforestation in 2024
The prediction came true: deforestation in Colombia increased in 2024 after two years of decline, just as the environment ministry had warned since April last year. The ministry announced that Colombia lost 1,070 square kilometers (413 square miles) of forest in 2024, a 35% increase from 2023, when deforestation hit 793 km2 (306 mi2). Former […]

Dynamic Strategy Adaptation in Multi-Agent Environments with Large Language Models
Shaurya Mallampati, Rashed Shelim, Walid Saad, Naren Ramakrishnan
https://arxiv.org/abs/2507.02002

Dynamic Strategy Adaptation in Multi-Agent Environments with Large Language Models
Large language models (LLMs) demonstrate strong reasoning abilities across mathematical, strategic, and linguistic tasks, yet little is known about how well they reason in dynamic, real-time, multi-agent scenarios, such as collaborative environments in which agents continuously adapt to each other's behavior, as in cooperative gameplay settings. In this paper, we bridge this gap by combining LLM-driven agents with strategic reasoning and real-time adaptation in cooperative, multi-agent environm…

Safe and Socially Aware Multi-Robot Coordination in Multi-Human Social Care Settings
Ayodeji O. Abioye, Jayati Deshmukh, Athina Georgara, Dominic Price, Tuyen Nguyen, Aleksandra Landowska, Amel Bennaceur, Joel E. Fischer, Sarvapali D. Ramchurn
https://arxiv.org/abs/2507.02521

Safe and Socially Aware Multi-Robot Coordination in Multi-Human Social Care Settings
This research investigates strategies for multi-robot coordination in multi-human environments. It proposes a multi-objective learning-based coordination approach to addressing the problem of path planning, navigation, task scheduling, task allocation, and human-robot interaction in multi-human multi-robot (MHMR) settings.

A Sea of Cyber Threats: Maritime Cybersecurity from the Perspective of Mariners
Anna Raymaker, Akshaya Kumar, Miuyin Yong Wong, Ryan Pickren, Animesh Chhotaray, Frank Li, Saman Zonouz, Raheem Beyah
https://arxiv.org/abs/2506.15842

A Sea of Cyber Threats: Maritime Cybersecurity from the Perspective of Mariners
Maritime systems, including ships and ports, are critical components of global infrastructure, essential for transporting over 80% of the world's goods and supporting internet connectivity. However, these systems face growing cybersecurity threats, as shown by recent attacks disrupting Maersk, one of the world's largest shipping companies, causing widespread impacts on international trade. The unique challenges of the maritime environment--such as diverse operational conditions, extensive physi…

ODIN: The LAE Ly{\alpha} Luminosity Function over Cosmic Time and Environmental Density
Gautam Nagaraj, Robin Ciardullo, Caryl Gronwall, Vandana Ramakrishnan, Kyoung-Soo Lee, Eric Gawiser, Nicole M. Firestone, Govind Ramgopal, J. Aguilar, Steven Ahlen, Davide Bianchi, David Brooks, Francisco Javier Castander, Todd Claybaugh, Andrei Cuceu, Axel de la Macorra, Arjun Dey, Biprateep Dey, Peter Doel, Jaime Forero-Romero, Enrique Gaztanaga, Satya Gontcho A Gontcho, Gaston Gutierrez, Hiram K.…

ODIN: The LAE Lyα Luminosity Function over Cosmic Time and Environmental Density
The ubiquity and relative ease of discovery make $2\lesssim z\lesssim 5$ Ly$α$ emitting galaxies (LAEs) ideal tracers for cosmology. In addition, because Ly$α$ is a resonance line, but frequently observed at large equivalent width, it is potentially a probe of galaxy evolution. The LAE Ly$α$ luminosity function (LF) is an essential measurement for making progress on both of these aspects. Although several studies have computed the LAE LF, very few have delved into how the function varies wit…

Variational Learning Finds Flatter Solutions at the Edge of Stability
Avrajit Ghosh, Bai Cong, Rio Yokota, Saiprasad Ravishankar, Rongrong Wang, Molei Tao, Mohammad Emtiyaz Khan, Thomas M\"ollenhoff
https://arxiv.org/abs/2506.12903

Variational Learning Finds Flatter Solutions at the Edge of Stability
Variational Learning (VL) has recently gained popularity for training deep neural networks and is competitive to standard learning methods. Part of its empirical success can be explained by theories such as PAC-Bayes bounds, minimum description length and marginal likelihood, but there are few tools to unravel the implicit regularization in play. Here, we analyze the implicit regularization of VL through the Edge of Stability (EoS) framework. EoS has previously been used to show that gradient d…

Machine-Learned Potentials for Solvation Modeling
Roopshree Banchode, Surajit Das, Shampa Raghunathan, Raghunathan Ramakrishnan
https://arxiv.org/abs/2505.22402

Machine-Learned Potentials for Solvation Modeling
Solvent environments play a central role in determining molecular structure, energetics, reactivity, and interfacial phenomena. However, modeling solvation from first principles remains difficult due to the complex interplay of interactions and unfavorable computational scaling of first-principles treatment with system size. Machine-learned potentials (MLPs) have recently emerged as efficient surrogates for quantum chemistry methods, offering first-principles accuracy at greatly reduced computa…