Derives a drift-aware sensing clock from certified world models that controls certificate violations on held-out data and outperforms expected-belief scheduling in a synthetic benchmark at matched sensing budget.
Belief-Space Control for Personalized Cancer Treatment via Active Inference
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abstract
Cancer treatment is at the core a sequential decision-making problem with partial observability, latent patient heterogeneity, and explicit constraints on the budget for medical measurements. Unlike standard Reinforcement Learning (RL) approaches that control state trajectories, cancer treatments permanently modify patients' transition dynamics, changing how states evolve over time. We model cancer treatment as a belief-space planning problem using active inference, deriving an expected free-energy objective that unifies goal-directed control and information acquisition under measurement budgets without. We implement this framework using real clinical cancer data from the AACR Project GENIE Biopharma Collaborative dataset. Results on clinical data demonstrate a simultaneous patient categorization and high treatment efficacy, under real measurement and treatment constraints.
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cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Certified World Models as Sensing Clocks: Drift-Aware Deadlines for Active Perception
Derives a drift-aware sensing clock from certified world models that controls certificate violations on held-out data and outperforms expected-belief scheduling in a synthetic benchmark at matched sensing budget.