Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs

ArXi:2506.07180v3 Announce Type: replace-cross As video large language models (Video-LLMs) become increasingly integrated into real-world applications that demand grounded multimodal reasoning, ensuring their factual consistency and reliability is of critical importance. However, sycophancy, the tendency of these models to align with user input even when it contradicts the visual evidence, undermines their trustworthiness in such contexts.