GGBound: Genome-Grounded Reasoning for Life-Boundary Phenotype Prediction

Predicting microbial life-boundary phenotypes from genomic sequence offers a scalable alternative to costly experimental screening, but remains challenging under sparse genomic observations and diverse phenotype types. Existing approaches either rely on predefined genomic features or perform direct genome-to-language prediction, leaving complementary predictive signals and intermediate phenotype proposals underused. In this paper, we formulate the task as \textit{proposal-conditioned phenotype refinement}, where genomic evidence is used to refine the proposals. We characterize proposal-conditioned genomic utility, showing that effective genomic representations should retain phenotype-relevant information while suppressing irrelevant variation. Guided by this analysis, we introduce GGBound, which learns phenotype-specific genomic representations and grounds them in a language decoder through phenotype-oriented reasoning supervision, yielding interpretable rationales alongside predictions. We additionally introduce Sufficiency-to-Decision GRPO to strengthen the use of genomic evidence beyond auxiliary proposals during refinement. We further construct a strain-centric benchmark spanning (17) microbial life-boundary phenotypes from IJSEM strains and BacDive annotations. Experiments show that GGBound consistently outperforms genome-based baselines across phenotype categories and even surpasses frontier LLMs using strain names to leverage organism-specific knowledge on most phenotype groups.