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Trump abruptly cancels EO signing event after top AI firm CEOs declined to go
President Donald Trump abruptly cancelled an event scheduled for Thursday to sign an executive order allowing government testing of frontier AI models. The cancellation followed the news that several top AI firm CEOs declined to attend the signing, despite Trump having given them only 24 hours' notice. Reports suggest that Elon Musk and Mark Zuckerberg were involved in urging Trump to abandon the executive order.
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Agentic AI for Remote Sensing: Technical Challenges and Research Directions
arXiv:2604.24919v3 Announce Type: replace Abstract: Earth Observation (EO) is moving beyond static prediction toward multi-step analytical workflows that require coordinated reasoning over data, tools, and geospatial state. While foundation models and vision-language models have advanced representation learning and language-grounded interaction in remote sensing, and agentic AI has shown strong potential for long-horizon reasoning and tool use, EO is not a straightforward extension of...
Edge of Stability Selectively Shapes Learning Across the Data Distribution
new Abstract: Existing analyses of the edge of stability (EoS) treat it as a global property of optimization. We show that it is also selective: the stability constraint redistributes learning across subsets of the training distribution, amplifying progress on some groups while suppressing progress on others. Using a branching intervention that enters or exits the EoS regime from the same training state, we causally demonstrate this trade-off and identify two necessary conditions for a group...
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models
Announce Type: new Abstract: Earth Observation (EO) has fundamentally transformed the monitoring of environmental processes and human activities up to planetary scale. Recent advances in self-supervised learning have given rise to Earth Observation Foundation Models (EOFMs), which leverage petabyte-scale unlabeled EO data to learn transferable representations across a wide range of downstream geospatial tasks. Despite these advances, current EOFMs remain largely confined to raster...
Trump's AI E-(I)-O could let feds pick winners and losers
After postponing a planned signing last month for an executive order addressing advanced cybersecurity AI models, President Trump has signed a largely similar version that’s just as questionably effective. The EO, signed in a private ceremony on Tuesday, directs various government agencies to take steps to protect their systems and data, as well as those of agencies they support, from cyber threats, while also facilitating access to advanced AI models that could help agencies bolster their...
Trump plan to test AI models has a problem—US security teams were gutted by DOGE
On Tuesday, Donald Trump finally signed his executive order expanding the government's efforts to conduct voluntary safety testing of frontier AI models. Now, critics are warning that the order may be short-sighted, offering only performative reassurances that the government is actively monitoring for AI risks, while changing very little about how and when models are deployed. Last month, Trump abruptly canceled a signing event, where he had hoped to launch an earlier version of the EO with...
HADT: A Heterogeneous Multi-Agent Differential Transformer for Autonomous Earth Observation Satellite Cluster
arXiv:2605.31023v1 Announce Type: new Abstract: This work addresses the problem of autonomous resource management in heterogeneous satellite cluster conducting Earth Observation (EO) missions including optical and Synthetic Aperture Radar (SAR) satellites. In autonomous operation mode, satellites are equipped with intelligent capabilities enabling real-time decision-making based on the latest conditions, while requiring minimal interaction with ground operators. Traditional scheduling...
CangLing-KnowFlow: A Unified Knowledge-and-Flow-fused Agent for Comprehensive Remote Sensing Applications
arXiv:2512.15231v3 Announce Type: replace Abstract: The automated and intelligent processing of massive remote sensing (RS) datasets is critical in Earth observation (EO). Existing automated systems are normally task-specific, lacking a unified framework to manage diverse, end-to-end workflows--from data preprocessing to advanced interpretation--across diverse RS applications. To address this gap, this paper introduces CangLing-KnowFlow, a unified intelligent agent framework that integrates...
FAF-CD: Frequency-Aware Fusion for Change Detection under Imperfect Multimodal Remote Sensing
arXiv:2606.03114v1 Announce Type: new Abstract: Remote sensing change detection for real-world monitoring often relies on imperfect heterogeneous observations, where pre- and post-event images may be asynchronous, cross-sensor, or affected by illumination, seasonal, and modality shifts. This setting is especially challenging for EO-SAR disaster mapping, where nuisance variation can resemble structural damage. We propose FAF-CD, a frequency-aware hybrid framework with a DINOv3-pretrained...