AI
Finding "the red mug on the top shelf" in a 3D scan isn’t just about matching pixels—it’s about reasoning. Key takeaways * 3D visual grounding = teaching AI to locate an object in a 3D scene from a natural-language description. * Most systems rely on huge, hand-labeled 3D
AI
New paper alert: Large Reasoning Models can lie about how they got an answer. Extending Chen et al. (2025), William Walden tests LRMs on multiple-choice questions that hide subtle hints in the prompt. The models often exploit the hints to pick the right option, but when asked how they reasoned,
AI
Smarter AI means knowing when to think harder LLMs can improve by thinking longer, but they rarely know how much thinking a task really needs. ROI-Reasoning trains models to plan their effort under a hard token budget, like students managing time on an exam. * Meta-Cognitive Fine-Tuning: Before answering, the model
NLP
Ever wish reviews told you exactly how people feel about the camera, battery, or price? That's aspect-based sentiment analysis (ABSA). Arctic-ABSA is a new family of reasoning-infused models built for real-world ABSA. Trained on a large blend of public and carefully generated synthetic data—about 20× the classic
NLP
Arctic-ABSA brings aspect-based sentiment analysis closer to real life. Instead of only “positive/negative/neutral,” it adds nuance, multilingual reach, and reasoning for better business decisions. * Five classes: positive, negative, neutral, mixed, and unknown. * Finds aspect-level opinions and the overall tone in one pass. * Multilingual: one model holds 87–91%
AI
LLMs can learn to reason—without task verifiers Many real-world problems don’t have automatic checkers to grade answers, even though we have lots of expert solutions. RARO (Relativistic Adversarial Reasoning Optimization) shows how to train reasoning skills from those examples alone. How it works: * A policy (the model) tries
AI
Can AI think creatively? Large language models are great at step-by-step logic, but real-world breakthroughs need more than routine problem-solving. This paper proposes a creative reasoning framework, rooted in cognitive science, and turns it into practice with "Universe of Thoughts" (UoT): methods that help LLMs search wider and