ViTAL‑X: Video-Text Alignment with Cross‑Modal Temporal Edits

Video-text models adapted from image-text architectures(e.g., CLIP) frequently exhibit temporal blindness—the inability to per-ceive fundamental cues like order, direction, and motion dynamics. Stan-dard datasets mask this limitation by enabling models to exploit staticspatial shortcuts. To systematically evaluate this, we introduce XTE-Bench, a diagnostic probe revealing that even large-scale video-languagemodels struggle with basic temporal reasoning, indicating that param-eter scaling alone is insufficient to resolve this flaw. To address this,we propose Cross-Modal Temporal Edits (XTE), a self-supervisedframework that injects precise temporal supervision. By performing syn-chronized video-text transformations, XTE generates hard temporal neg-atives without manual annotation. We instantiate this with ViTAL-X,a lightweight model that equips frozen image-text backbones with tem-poral awareness while preserving their foundational spatial knowledge.Across six temporal benchmarks, ViTAL-X achieves state-of-the-art per-formance. Utilizing only 0.4B parameters and 1M training clips, ViTAL-X outperforms 7B-parameter models and surpasses baselines trained on600× more data. These results demonstrate that targeted, high-qualitytemporal alignment provides a highly efficient alternative to pure scaling.