Latent visual reasoning aims to perform multimodal reasoning through hidden-state computation rather than explicit textu…

Reasoning in large language models unfolds through diverse functional operations, such as problem formulation, goal deco…

Large Reasoning Models (LRMs) achieve superior problem-solving through extended Chain-of-Thought (CoT) generation, but t…

A reasoning model can improve from its own on-policy experience, but this inner loop is fragile: terminal verifiers prov…

Despite the remarkable prowess of Vision-Language Models (VLMs) in general multimodal tasks, they remain fundamentally `…

Visual fluency in generated video does not imply physical reliability, and a scalar quality score alone is incapable of …

Large Language Models demonstrate remarkable proficiency in static reasoning, yet training them as autonomous agents thr…

Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and …

Reinforcement learning with verifiable rewards (RLVR) substantially improves single-sample accuracy (pass@1) but causes …

Instance segmentation of overlapping cells in microscopy remains challenging due to semi-transparent structures that pro…

Audio-visual understanding remains challenging because models must jointly interpret spoken content, visual events, and …