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A new research paper challenges the idea that AI models can easily become misaligned and then realigned. The study finds these behaviors are highly sensitive to fine-tuning details, suggesting they may not be as robust as previously thought.
Researchers created a new test called Long-Horizon-Terminal-Bench to evaluate AI agents on complex, time-consuming tasks. Unlike previous tests, it measures progress over time, not just final results.
Researchers proposed a system to make AI's scientific reasoning transparent. This could help scientists audit AI-generated ideas like they would their own notes. The tool creates a structured record of AI hypotheses and experiments, making it easier to follow the AI's thought process.
Researchers have developed a way to catch AI chatbots when they make up false information. This is especially important for systems that need to provide accurate details, like recommending restaurants or booking appointments.
Researchers have developed a neuro-agentic control framework that combines AI planning with traditional security systems. This could make industrial IoT environments safer by reducing the risk of costly downtime and physical damage from cyberattacks.
Researchers created MedRealMM, a real-world benchmark for medical AI that includes real patient-doctor conversations and medical images. This could lead to more accurate and reliable AI tools for online healthcare consultations.
Researchers created a new test to measure AI's ability to connect clues scattered across long documents. This could improve how AI understands complex real-world information.
Microsoft's CEO has issued a stark warning about the risks of frontier AI labs accessing corporate intellectual property. Companies are being urged to take proactive measures to safeguard their sensitive data.
In a surprising blog post, Microsoft CEO Satya Nadella warned enterprises about the dangers of relying on proprietary AI models from companies like Anthropic and OpenAI, urging businesses to maintain control over their AI systems to avoid vendor lock-in and unforeseen risks.
Researchers created LongMedBench, a new benchmark for AI medical assistants that simulates real-world patient care over time. Built from real EHR data (MIMIC-IV), it evaluates how well AI can handle ongoing treatments, repeated visits, and evolving health conditions.
Researchers have developed a new method called KV-PRM that makes multi-agent AI systems more efficient. This could lead to faster, more capable AI collaborations in complex scenarios. The technique reduces the computational cost of evaluating long interactions, making it practical for real-world applications.
Instagram head Adam Mosseri says AI-generated content should not be filtered out of the platform, but users who dislike it can choose to avoid it. He emphasizes transparency through labeling rather than removal.
Researchers introduced HALO, a method to enhance existing AI language models with minimal extra computation. It selectively refines responses, improving accuracy without wasting resources.
Researchers developed a new AI planning method called GATS that reduces computational costs and improves reliability by eliminating LLM calls during inference. It could make AI assistants more efficient at complex tasks.
Security experts are now using prompt injection techniques to stop AI-powered hacking tools. This method tricks malicious agents into shutting down before they can cause harm. It's a creative twist on a common attack strategy.
Local governments and residents are increasingly opposing the construction of AI data centers due to their massive energy demands and environmental impact. These protests are growing as tech companies expand their AI infrastructure.
Researchers introduced CogniConsole, a tool that improves AI reliability by managing how tasks are framed and context is selected. This approach shifts focus from model capabilities to better control during interactions.
Researchers introduced ARCANA, a multi-agent AI framework that solves ARC-AGI-2 tasks by iteratively perceiving, hypothesizing, executing, and reflecting. It operates under strict test-time and hardware constraints, using specialized agents to build scene graphs, propose DSL programs, verify candidates, and synthesize failure-driven feedback.
Researchers developed a new AI framework where multiple AI agents debate legal cases, improving accuracy by up to 8%. This approach could make legal analysis faster and more reliable for professionals and the public.
The rapid expansion of AI infrastructure is creating new inflation risks. Companies are spending heavily on AI hardware and data centers, which could push prices higher. This is a shift from the recent trend of falling tech prices.
Researchers developed AgentKGV, an AI framework that improves the accuracy of knowledge graphs by verifying facts. This could make AI systems more reliable for everyday users. The system uses a two-stage training process to identify and correct errors in large-scale data sets.
X has launched a new AI feature called 'Community Notes' that helps fact-check posts in real time. This aims to improve information accuracy on the platform by leveraging collective intelligence.
A new analysis reveals that AI code review benchmarks often test the wrong things, leading to misleading results. This highlights why AI tools still struggle with real-world coding tasks.
SK Hynix just raised $26.5 billion in the largest foreign IPO in US history. The AI chip boom is driving demand for more US-based semiconductor factories, and the company is now being urged to build new fabs in the US.