Text-Centric Visual Question Answering (TEC-VQA) in its proper format not only facilitates human-machine interaction in text-centric visual environments but also serves as a de facto gold proxy to ...

Visual grounding and language comprehension in robotics represent a rapidly evolving interdisciplinary field that integrates computer vision, natural language processing and robotic control systems.

Comprehenders can not only predict syntactic information of an upcoming word, but also of a larger unit, such as sentence structure. It is unclear whether such prediction effects are driven by an ...

Metacognitive skills in text comprehension are fundamental for students' learning, yet their development may differ depending on text genre (narrative vs. expository), question type (factual vs.

Stage 2 – Reinforcement Learning (RL): The model is trained using a token-level Markov decision process with bi-level QA-based rewards to encourage spontaneous reasoning and correction, optimizing via ...