May 25, 2026— curated & summarized by AI
Researchers created SciAtlas, a knowledge graph that organizes scientific research to help AI and humans find connections. It aims to solve the problem of too much fragmented academic information.
A new study shows how easily safety measures in AI models from Meta and Google can be bypassed. This raises concerns about the effectiveness of current AI safeguards.
Pope Leo XIV released an encyclical urging that AI should benefit all people, not just those in power. The document emphasizes ethical considerations and equitable access to AI technologies.
Researchers created a system to track how much energy AI agents use to complete real-world tasks. This could help make AI systems more efficient and transparent about their environmental impact.
Scientists have discovered why certain layers of AI language models closely match human brain responses to language. This breakthrough could lead to better AI-human communication and more intuitive AI systems. The study used a technique called sparse autoencoders to break down AI models into understandable parts, revealing that semantic features alone can predict brain activity with high accuracy.
Researchers have developed an AI system called RMA that can tackle advanced math problems requiring deep reasoning and literature review. This could revolutionize how mathematical research is conducted, making it faster and more accessible.
Researchers have developed a way for AI to create computer systems with mathematically proven guarantees of correctness. This could revolutionize fields where mistakes are unacceptable, like aviation or medical devices.
Researchers developed a new AI system called NeuroNL2LTL that translates human language into precise machine logic. This could make safety-critical systems like self-driving cars more reliable by ensuring human instructions are accurately understood by machines.
Researchers propose the Foundation Protocol to help AI agents work together more effectively. This could enable better collaboration between autonomous systems in the future.
Researchers introduced EVE-Agent, an AI system that learns by generating and answering its own questions, using only verifiable evidence. This approach aims to make AI more trustworthy by avoiding unsupported or unreliable information.
A new research paper identifies hard limits on what AI models can achieve, turning theoretical impossibilities into practical design rules. This could shape how we build and trust AI systems in the future.
AI tools are now identifying software flaws faster than developers can fix them. This shift is changing how companies approach cybersecurity. These tools can scan code for weaknesses in seconds, but fixing them still requires human time and effort.
Researchers are developing AI systems that could automate entire scientific research processes, from literature review to final reports. This shift could make scientific discovery faster and more accessible, though challenges remain.
Researchers developed a new AI system to better identify language patterns linked to autism. This could help diagnose social language disorders more accurately in the future.
Researchers found that AI models align more closely with human brains when trained on the same language. This suggests language-specific training data shapes how AI understands us, not an inherent property of any language.
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