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This paper proposes a risk-averse online POMDP planning method that applies CVaR to the immediate cost over the belief at each step, keeping the standard expected cumulative return objective so existing expectation-based planners can be reused. The authors prove finite-time guarantees for policy evaluation, sparse sampling, and a bound on the gap between the particle belief MDP surrogate and the original POMDP, recovering standard planning in the risk-neutral limit.
This paper introduces Lorentz Encoding (LE), a physics-informed framework that uses implicit neural representations and physical constraints to reconstruct high-resolution CEST MRI from sparsely sampled data, achieving superior performance over existing methods.