Mistral AI
Jul 22, 2025
Position: AI Must Become Planet-Centered, Not Just Human-Centered
arXiv:2606. 13704v1 Announce Type: cross Abstract: This position paper argues that contemporary AI paradigms are insufficient for supporting complex global goals and introduces Planet-Centered AI (PCAI) as a design philosophy and research agenda that reorients AI toward planetary-scale socio-ecological systems and their long-term trajectories.
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arXiv Machine Learning
Jun 4
Geospatial Foundation Models to Enable Progress on Sustainable Development Goals
arXiv:2505. 24528v3 Announce Type: replace-cross Abstract: Foundation Models (FMs) are large-scale, pre-trained artificial intelligence (AI) systems that have revolutionized natural language processing and computer vision, and are now advancing geospatial analysis and Earth Observation (EO).
arXiv Machine Learning
Jun 2
Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race
arXiv:2502. 20016v2 Announce Type: replace Abstract: Sustainability encompasses three key facets: economic, environmental, and social.
arXiv AI
Jun 9
Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
arXiv:2604. 22748v2 Announce Type: replace Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a central bottleneck.
arXiv AI
Jun 12
From AGI to ASI
arXiv:2606. 12683v1 Announce Type: new Abstract: Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations.
arXiv AI
Aug 7
The Closing Window: How Governments Could Lose Their Ability to Restrain Advanced AI
arXiv:2608. 05173v1 Announce Type: cross Abstract: As AI capabilities advance, AI systems will pose greater risks to national security and potentially humanity as a whole.
arXiv AI
6d ago
Beyond the Last Truffula Tree: SustainAI - A Water-Aware, Closed-Loop Framework for Environmentally Accountable AI
SustainAI is a water‑aware, closed‑loop framework that embeds environmental accountability into AI deployment. It combines real‑time water metering, a hallucination‑aware penalty model, and a water‑aware routing algorithm that considers regional water stress. In tests with small language models, water footprints varied 11‑fold across data centers, and 1,335 inference runs consumed about 399 mL of water but yielded only 240 correct outputs, highlighting the resource cost of inaccurate responses.
arXiv AI
Jul 28
Physical AI Governance: From Theory to Practice Across Life Cycle
arXiv:2607. 22877v1 Announce Type: new Abstract: With the emergence of Physical AI, artificial intelligence is extending beyond screen-based applications to embodied systems that perceive, interact with, and act in the physical world.
arXiv AI
Aug 5
Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory
arXiv:2608. 03910v1 Announce Type: new Abstract: As AI systems are deployed across increasingly diverse social contexts, alignment can no longer be framed as the optimization of a single, unified set of values.
arXiv AI
Jul 9
Towards Agentic AI Governance: A Preliminary Assessment
arXiv:2607. 07612v1 Announce Type: cross Abstract: Artificial intelligence is rapidly evolving from generative systems to agentic AI capable of autonomously planning and executing tasks.
arXiv AI
Jul 3
Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond
arXiv:2607. 02197v1 Announce Type: cross Abstract: The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems.
arXiv AI
Jul 22
Reframing AI Loss of Control: What Control Is, How to Have It, How to Lose It
arXiv:2606. 12442v2 Announce Type: replace-cross Abstract: At present, loss of control risks have gained much prominence in public discussion, particularly in relation to AI, with extensive discourse present among academics, frontier labs, and even governments.