Large language models (LLMs) are rapidly becoming interactive scientific assistants. Most existing pipelines rely on specialized molecular encoders and alignment modules, while overlooking the importance of chemical hierarchy and traceable evidence for reliable reasoning and explanation. Thus, we propose MolGlass, a da...
Runqing Xu, Xiaotang Wang, Chunfeng Gao et al.· Proceedings of the 32nd ACM...· 0 citations
RePair is introduced, guided by three principles---Validity, Minimality, and Locality---which mines false positives bidirectionally, applies LLM-guided counterfactual editing, and trains with a local hard-pair contrastive objective.
Si-Yi Liu, Xiao-Rong Zhu, En-Jun Du et al.· 0 citations
Large vision-language models used as listwise rerankers must jointly process visual tokens from tens of candidates per query, making token pruning essential for practical deployment. Existing pruning methods retain tokens by attention saliency, yet we show that saliency is systematically misaligned with ranking contrib...
Si-Yi Liu, Han-Jun Yang, Chen-Chen Zhang et al.· 1 citation
Empirical evaluation and robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions and establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction.
Runqing Xu, Siyi Liu, Hao-Yang Li et al.· Proceedings of the 32nd ACM...· 0 citations
EviRank is recast multimodal image re-ranking as a semantic constraint satisfaction problem and proposed, which parses any query - text-only, image-only, or composed - into a unified evidence package: typed criteria across six semantic slots, each labelled required, forbidden, or ignorable.
Enjun Du, Si-Yi Liu, Zi-Rong Chen et al.· 2 citations
Empirical evaluation and robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions and establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction.
Runqing Xu, Siyi Liu, Haoyang Li et al.· Proceedings of the 32nd ACM...· 0 citations
MolGlass is proposed, a data-centric paradigm for visual molecular understanding in vision-language models (VLMs) that injects chemical priors directly into the visual input through chemical-aware visual augmentations, without modifying model architectures or training molecule-specific encoders.
Runqing Xu, Xiaotang Wang, Chunfeng Gao et al.· Proceedings of the 32nd ACM...· 0 citations
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