Xiangyu Li, Jincheng Li, Tianle Du, Yanheng Li, Haoxuan Che, Yuxin Sun, Long Bai, Liangliang Liu 等 9 位作者
A question about a whole-slide image rarely names every morphological finding needed to answer it. Question-guided patch selection can therefore overlook supporting morphology, while slide-level compression can dilute focal evidence. Inspired by how pathologists combine broad slide review, question-directed examination, and integration of their findings, we introduce BEYONDPATH, a Preserve-Focus-Fuse framework for whole-slide image question answering. Preserve learns a question-agnostic Morphology Bank through answer supervision across multiple questions about each training slide. Focus retrieves questionspecific evidence from all foreground patches, including regions outside the Bank. Fuse integrates the preserved morphology at the retrieved evidence positions through Morphology-on-Visual-Evidence Fusion, producing a fixed 96-token visual prefix. On WSI-Bench, BEYONDPATH achieves an Avg-9 of 0.769 and leads all eight reported report-generation metrics using the same checkpoint. It also achieves 0.639 zero-shot overall accuracy on SlideBench-BCNB. Controlled ablations show complementary benefits from morphology preservation and question-directed retrieval, and support cross-question supervision and fusion at the evidence positions. These results support looking beyond the question's explicit cues: preserving broader slide morphology while retaining the ability to seek evidence for each question.
Haoran Sun, Natthawut Kertkeidkachorn Japan Advanced Institute of Science and Technology, Tokyo Institute of Technology, Kiyoaki Shirai Japan Advanced Institute of Science and Technology, Tokyo Institute of Technology
Automatic soccer commentary generation aims to bridge the gap between raw visual content and professional, tactical commentary. However, existing datasets tend to produce incomplete commentary that lacks semantic richness and fails to convey the full visual information present in standard video clips. To address these limitations, we propose two manually curated datasets: SN-Short, which enhances scene-level semantic descriptions, and SN-Long, which captures event continuity for context-aware commentary.Based on these, we design a commentary augmentation pipeline that transforms incomplete annotations into MatchText, a semantically complete and structurally standardized dataset. Leveraging this supervision, we introduce MatchAware, a generation model that incorporates contextual cues from previous events to produce coherent commentary aligned with the visual flow of the game. Experimental results show that proposed approach significantly outperforms existing baselines on the constructed datasets.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表