1193 stories curated by AInformed · page 12 of 50
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.
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.
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.
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.
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.
Scientists have developed a way to measure and adjust AI personalities using the same framework psychologists use for humans. This could help make AI more predictable and safer to interact with. Researchers trained AI models to amplify or suppress traits like openness and neuroticism, using low-rank adapters to fine-tune behavior.
Researchers propose a new system called Context Graphs to make AI agents proactive. This could help AI assistants anticipate needs and surface relevant, actionable information before workers even ask.
A new research paper outlines how large language models (LLMs) are being used in healthcare, bridging the gap between clinical needs and AI capabilities. The study proposes a framework to align medical training standards with AI's potential in patient care.
Researchers highlight risks of AI in mental health and propose a new framework to ensure safer, more effective support. The study calls for proactive measures to address long-term harms like dependency and distorted beliefs.
A new paper explores how humans and AI models can create misleading information when they interact. The research highlights how incentives can lead to strategic misinformation, beyond simple echo chambers.
A new paper introduces a method to improve how AI agents learn from human feedback, making them better at following human values. This could lead to AI systems that are more reliable and trustworthy in real-world applications.
Researchers have developed an AI tool that combines economic and environmental models to predict how disruptions like climate change or market shifts will affect food supply chains. This could help farmers and governments make better decisions to protect food security.
Researchers created Infinity-Parser2, an AI that can read and understand documents better than ever. It uses a special training method to handle all kinds of documents, from contracts to reports, in both English and Chinese. This could make document processing faster and more accurate for businesses and everyday users.
A new research paper explores how AI is changing actuarial work, especially in underwriting. It highlights the potential of advanced AI systems to handle complex, unstructured data and regulated workflows. This could make insurance processes faster and more accurate for everyone.
Researchers have developed a new AI model that learns from shopping habits to predict customer behavior. This could revolutionize personalized shopping experiences and marketing strategies.