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MOLAR: Learning Multimodal Molecular Representations from Noisy Labels

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arXiv:2606. 18390v1 Announce Type: new Abstract: Motivation: Noisy labels are a common challenge in molecular property prediction because molecular annotations are often obtained from assays, curated databases, or weak annotation pipelines rather than directly observed clean biological states.

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Authors: Yingxu Wang, Kunyu Zhang, Nan Yin, Yu Li, Eran Segal
Released: 2026-06-18 (21d ago)
Topic: Related to current project
Relation to my work: Read next because MOLAR: Learning Multimodal Molecular Representations from Noisy Labels overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, rect, alignment, line, rate, control, propagate, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.18390

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