3 accepted papers
Real-world data visualization (DV) requires native environmental grounding, cross-platform evolution, and proactive intent alignment. Yet, existing benchmarks often suffer from code-sandbox confinement, single-language creation-only tasks, and assumption of perfect intent. To bridge these gaps, we i…
A clean map of the surrounding environment is essential for autonomous driving systems to ensure reliable localization and safe path planning. However, the existence of dynamic objects introduces ghost traces into the map, significantly degrading its quality. To address this issue, we propose Enhanc…
Recent advances in multimodal large language models (MLLMs) have demonstrated substantial potential in video understanding. However, existing benchmarks fail to comprehensively evaluate synergistic reasoning capabilities across audio and visual modalities, often neglecting either one of the modaliti…