1193 stories curated by AInformed · page 18 of 50
Researchers developed an AI-powered pipeline to compare governance structures of decentralized and corporate AI protocols. The system analyzes over 4,300 governance discussions to uncover power dynamics in AI agent interoperability standards.
Researchers created a contamination-aware, multi-zone benchmark called Know2Guess to evaluate when large language models should answer questions versus abstain. It has 1,200 items across five domains with explicit abstention expectations and contamination-risk metadata.
Researchers found that AI models like GPT-5.1, Gemini 3 Pro, and DeepSeek-V3.2 have distinct tendencies when suggesting research methods. The study highlights how these models might influence scientific research differently.
Researchers introduced COrigami, an AI pipeline that co-designs origami crease patterns which are both mathematically flat-foldable and visually recognizable. This bridges the gap between rigid geometric constraints and subjective aesthetics, potentially making computational origami design more accessible.
Researchers introduced ContextForge, a system that helps large language models maintain relevant information across long conversations by recycling context instead of replaying entire histories. This could slash token usage and improve multiturn reasoning.
Researchers developed an AI system that combines official drug data with patient experiences to provide safer, more accurate medication information. This could help people make better decisions about their mental health treatments.
Researchers have developed AlgoEvolve, an AI system that uses large language models to evolve and improve algorithmic trading strategies. This could make trading more accessible and efficient for everyday investors.
Researchers evaluated AI agents on complex energy market tasks, including live data retrieval, regulatory knowledge, and multi-step quantitative reasoning. The study fills a critical gap, with 243 expert-designed tasks showing how tool-augmented LLMs could make energy systems more efficient and responsive.
Researchers introduced TrustMem, a framework designed to improve long-term memory in AI agents. It addresses the problem of AI systems forgetting important information or generating hallucinated content that becomes permanently stored. This could make AI assistants more reliable over extended interactions.
A new AI model called iLLaDA uses a different approach to understand language, potentially making it more efficient and accurate. This could lead to better AI assistants and tools that understand context more naturally.
AI agents often make mistakes that snowball over time, especially in tasks like persuasion. Researchers found a key reason—semantic leakage in standard RAG—and developed a method called Taxonomic Strategy Retrieval to prevent these compounding errors.
A new practitioner's reference, 'The Hitchhiker's Guide to Agentic AI,' covers the full stack of building autonomous AI systems—from transformer architecture and GPU systems to fine-tuning, model compression, and production deployment.
Researchers developed an AI technique to automatically create highlights for academic papers. This could make scientific research easier to digest for non-experts and improve literature searches.
Researchers developed Heuresis, a framework to help AI agents explore high-quality, diverse, and novel ideas in machine learning. This could speed up scientific progress by making AI research more efficient and creative.
A small group of Wikipedia editors has shown that targeted edits can influence how AI models discuss animal welfare. Their work highlights the power of curated information in shaping AI behavior.
Researchers developed a method using large language models to automatically generate challenging test problems for AI reasoning. This could lead to better evaluations of AI's ability to apply knowledge to new, harder tasks.
AI systems are now authorized to prescribe medications independently in the U.S. This shift raises critical questions about trust, liability, and how to handle uncertainty in medical decisions. Researchers highlight gaps in current regulations that could impact patient safety.
Researchers introduced RIFT-Bench, a dynamic red-teaming benchmark that uses a graph-based methodology to evaluate security vulnerabilities across diverse agentic AI systems. Unlike static tests, it simulates adaptive attacks to find weaknesses before real hackers do, aiming to make autonomous AI safer.
Researchers developed OmniPath, an AI system that audits paths for wheelchair accessibility. It combines OpenStreetMap data with high-precision aerial LiDAR scans to show the real feel of travel routes. This could transform how cities plan for accessibility.
A new study explores whether reinforcement learning (RL) on beneficial behavior can help AI systems generalize alignment beyond their training data, addressing risks like reward hacking and deception in high-stakes settings.
Researchers introduced a new way to align AI models with user preferences without expensive fine-tuning. This method uses simple instructions and a few examples to create natural-language prompts that guide AI behavior.
Researchers developed a neuro-symbolic framework that trains vision-language-action driving models using rule-grounded reasoning traces from classical planners, making autonomous driving decisions more transparent and causally connected to planned motion.
A new study found that AI chatbots fail to provide consistent safety for most mental health conditions. Only suicide and self-harm have reliable safeguards in place. Researchers call for better standards and transparency.
Researchers developed SciLens, an AI system that can verify scientific claims by analyzing both text and images. This tool could make it easier for scientists and the public to check the accuracy of research findings.