SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction
Does every cancer patient truly need a complete diagnostic workup for accurate survival prediction? In multimodal clinical oncology, diagnostic modalities follow a clinically mandated order of escalating burden -- from demographics collected at intake to genomic profiling requiring specialized tissue analysis. Current multimodal survival methods either assume all modalities are available or passively handle missing data, but none actively reason about whether acquiring the next modality is justified for a given patient along this ordered workflow. We formulate this as a sequential decision pro
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 62%TIP12 Validation of a Multimodal Artificial Intelligence Prognostic Model in Early-Stage HR+/HER2− Breast Cancer - CancerNetwork →
- PossiblePossibly related (embedding) · 46%EvolvingLMMs-Lab/LLaVA-OneVision-2 →
- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acq” ≈ “AgentCore-8B””
- LinkedLinked via arxiv author · 85%Chongyu Qu →
“SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction”
- LinkedLinked via arxiv author · 85%Can Cui →
“SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction”
- LinkedLinked via arxiv author · 85%Zhengyi Lu →
“SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction”
- LinkedLinked via arxiv author · 85%Junchao Zhu →
“SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction”
- LinkedLinked via arxiv author · 85%Tianyuan Yao →
“SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction”