AI RESEARCH
Overcoming the Modality Gap in Context-Aided Forecasting
arXiv CS.LG
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ArXi:2603.12451v1 Announce Type: new Context-aided forecasting (CAF) holds promise for integrating domain knowledge and forward-looking information, enabling AI systems to surpass traditional statistical methods. However, recent empirical studies reveal a puzzling gap: multimodal models often fail to outperform their unimodal counterparts. We hypothesize that this underperformance stems from poor context quality in existing datasets, as verification is challenging. To address these limitations, we