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arXiv Machine Learning By Haowei Xu, Wanyi Fu, Hongbin Han, Zhaoheng Xie

NeuronSifter: An Agent-in-Twin for Prioritizing Central Nervous System Interventions with World Action Models

Read the original on arXiv Machine Learning →

NeuronSifter is a decision‑centric Agent‑in‑Twin framework that uses an evolving biological digital twin instead of a fixed predictive model to prioritize central nervous system interventions. It compiles interventions into state‑conditional target‑occupancy representations, propagates them through a mechanism‑grounded World Action Model, and selects informative measurements to reduce decision uncertainty. The system is evaluated on Alzheimer’s disease scenarios, demonstrating adaptive evidence acquisition, uncertainty‑aware planning, and robustness to model discrepancy, while continuously refining its decision‑relevant representations with externally verified evidence.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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