AI
AI agents powered by large language models can call tools, but they also hallucinate: picking the wrong tool, sending malformed parameters, or bypassing tools by simulating results. That breaks reliability, security, and audit trails in production. This study shows a lightweight way to catch those mistakes in real time. Instead
AI
Sometimes, vision-language AIs see what the prompt says, not what is in the picture. New research maps how that happens—and how to dial it down. Rudman and colleagues tested object counting with prompts that intentionally overstate what is in an image (for example, "describe four waterlilies" when
AI
Why do AI models sometimes sound sure while being wrong? This study spotlights a subtle culprit: spurious correlations—strong but misleading patterns in training data (like linking certain surnames to a nationality). * These shortcuts make LLMs produce confident, wrong answers. * Making models bigger doesn’t fix it. * Popular detectors—confidence
AI
Large language models can sound confident even when they are wrong. This study pinpoints a sneaky culprit: spurious correlations — statistical shortcuts like linking certain surnames to a nationality. Models absorb these patterns from data and then answer with high certainty, even when the link is false. What the researchers found