UniNS: A Neuro‐Symbolic Framework for Structure‐Regularized Causal Modeling of Catalytic Nanorobot Swarms in Confined Tumor Microenvironments

ABSTRACT Simulating the behavior of 100 nm catalytic nanorobot swarms in the tumor microenvironment from a multiphysics perspective is extremely challenging: traditional continuum models fail because agent dimensions mirror interstitial pore throats ( Kn ~ ), and distinguishing genuine causal feedback loops from biological confounders and extracellular matrix remodeling remains an open problem. This paper proposes UniNS, a hybrid neuro‐symbolic framework that couples a truncated Rotne‐Prager‐Yamakawa tensor with the Stokes‐Brinkman equations to capture near‐field hydrodynamic interactions beyond standard Darcy‐Brinkman assumptions, while Michaelis‐Menten kinetics bound active driving forces to preserve energy conservation. To counter the tendency of physics‐informed neural networks to learn spurious correlations, the framework combines continuous structure regularization with backdoor‐criterion marginalization, dynamically refining a structural DAG hypothesis from an expert‐knowledge prior; the resulting structure is treated as a statistically reasonable association model rather than an absolute causal claim. Fast multipole acceleration extends the approach to synthetic pore networks with up to N = 10 4 agents. The discovery phase converges to a structural Hamming distance of 3 from the expert prior ( p < 0.01, permutation test), and the refined structure reduces biochemical prediction errors by 27.3% relative to fixed‐graph baselines. Together, these results establish a thermodynamically consistent, statistically regularized in silico testbed for nanoscale swarms—one that may help reduce reliance on costly in vitro experimentation and offers a promising direction for sustainable engineering practice in precision medicine.