{"ID":2843496,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08399","arxiv_id":"2511.08399","title":"Aligning by Misaligning: Boundary-aware Curriculum Learning for Multimodal Alignment","abstract":"Most multimodal models treat every negative pair alike, ignoring the ambiguous negatives that differ from the positive by only a small detail. We propose Boundary-Aware Curriculum with Local Attention (BACL), a lightweight add-on that turns these borderline cases into a curriculum signal. A Boundary-aware Negative Sampler gradually raises difficulty, while a Contrastive Local Attention loss highlights where the mismatch occurs. The two modules are fully differentiable and work with any off-the-shelf dual encoder. Theory predicts a fast O(1/n) error rate; practice shows up to +32% R@1 over CLIP and new SOTA on four large-scale benchmarks, all without extra labels.","short_abstract":"Most multimodal models treat every negative pair alike, ignoring the ambiguous negatives that differ from the positive by only a small detail. We propose Boundary-Aware Curriculum with Local Attention (BACL), a lightweight add-on that turns these borderline cases into a curriculum signal. A Boundary-aware Negative Samp...","url_abs":"https://arxiv.org/abs/2511.08399","url_pdf":"https://arxiv.org/pdf/2511.08399v2","authors":"[\"Hua Ye\",\"Hang Ding\",\"Siyuan Chen\",\"Yiyang Jiang\",\"Changyuan Zhang\",\"Xuan Zhang\"]","published":"2025-11-11T16:15:15Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CV\"]","methods":"[]","has_code":false}