# Belief-Space Control for Personalized Cancer Treatment via Active Inference

arXiv: https://arxiv.org/abs/2606.10376 · HTML: https://mlsanity.com/paper/2606.10376/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review](https://mlsanity.com/paper/1805.00909/) (2018)
- [Doubly Robust Off-policy Value Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1511.03722/) (2015)
- [Reinforcement Learning in Healthcare: A Survey](https://mlsanity.com/paper/1908.08796/) (2019)
- [Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits](https://mlsanity.com/paper/1402.0555/) (2014)
- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [A free energy principle for a particular physics](https://mlsanity.com/paper/1906.10184/) (2019)
- [The free energy principle made simpler but not too simple](https://mlsanity.com/paper/2201.06387/) (2022)
- [A multi-objective constrained POMDP model for breast cancer screening](https://mlsanity.com/paper/2206.05370/) (2022)

## Built on by (most cited first, 1 of 1)

- [Certified World Models as Sensing Clocks: Drift-Aware Deadlines for Active Perception](https://mlsanity.com/paper/2607.01537/) (2026)

## Abstract (arXiv)

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.