Agentic Volumetric CT Reconstruction via Physics-Grounded Hypothesis Search

Diffusion-based 3D CT reconstruction typically follows a single stochastic inference trajectory, limiting its ability to explore alternative volumetric solutions when measurements are incomplete. We introduce an agentic, physics-grounded framework that reformulates reconstruction as adaptive hypothesis search under a fixed test-time computation budget, without retraining the diffusion prior. A vision-language model (VLM) transfers cross-case reconstruction knowledge into a search prior that only prioritises which unexplored volumetric action should be tried first. Each action is realised by Hierarchical Physics-Compatible Evolution (HPCE), which combines shallow diffusion re-entry, full-volume data consistency, and physics-compatible innovation to generate structured 3D hypotheses whose diffusion-proposed structural changes remain constrained by the fit measurements. Realised hypotheses are evaluated using projection views held out from candidate formation, providing a case-specific, ground-truth-free physical state value. Physics-Grounded Monte Carlo Tree Search (PG-MCTS) uses this feedback to adaptively allocate the remaining computation budget across competing reconstruction trajectories. After search, we commit the selected action lineage rather than any search-stage volume, replay it using all acquired measurements, and accept each transition only when the full-measurement projection residual does not increase. Learning therefore prioritises where to search, held-out measurements determine what is worth pursuing, and full measurements govern final acceptance. Experiments on sparse-view and limited-angle CT, including cross-dataset evaluation, demonstrate consistent improvements over strong diffusion reconstruction baselines and matched-budget non-adaptive search, and a cross-task MRI study further shows gains over the diffusion baselines.