3 accepted papers
Spatiotemporal forecasting is a fundamental task in areas such as traffic flow prediction, environmental sensing, and urban planning. Recent advances have shown that decomposing temporal signals into multiple frequencies and modeling them jointly with spatial structures can significantly enhance for
While Vision-Language Models (VLMs) have garnered increasing attention in the AI community due to their promising practical applications, they exhibit persistent hallucination issues, generating outputs misaligned with visual inputs. Recent studies attribute these hallucinations to VLMs
Real-world systems increasingly require coherent reasoning and generation over diverse data modalities simultaneously. Current generative frameworks rely on complex, multi-stage training, resulting in low efficiency due to iterative inference and high computational cost. They also struggle with unif