DNAI Biotech

Patent Pending

U.S. Provisional Application No. 64/036,630

Runtime Execution Controller for Differentiable Simulators with Autodiff-Verified Parameter Exclusion and Fail-Closed Enforcement

An execution controller and method for enforcing a parameter exclusion invariant in differentiable computational models. The system structurally excludes conditional parameters from forward computational paths for inactive-condition instances, verifies exclusion via an automatic differentiation engine, and triggers conjunctive fail-closed actions upon violation — aborting execution, blocking persistence of invalid output artifacts, and suppressing optimizer state updates. The invention applies broadly to any differentiable computational model requiring causal parameter isolation.

19 Claims
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CROSS-REFERENCES

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and priority to U.S. Provisional Patent Applications No. 63/967,576, No. 63/974,083, No. 63/974,099, No. 63/988,460, No. 63/988,475, No. 63/988,480, No. 63/991,254, No. 63/991,263, No. 64/029,329, No. 64/029,334, No. 64/029,335, No. 64/029,336, and No. 64/029,337. All prior provisional applications are incorporated herein by reference in their entireties.

I.FIELD OF THE INVENTION

[0001]The present disclosure relates generally to machine learning, computational biology, and simulation integrity. More specifically, it relates to an execution controller for differentiable simulators and mechanistic neural networks. The system utilizes a dual-path causal firewall in the forward-pass computational graph, conditional argument binding, and fail-closed runtime enforcement to structurally guarantee parameter identifiability and prevent the persistence of invalid simulator artifacts.

II.BACKGROUND OF THE INVENTION

1.The Parameter Hijacking Pathology

[0002]In differentiable simulators trained end-to-end on observational data, a critical parameter identifiability pathology arises. The neural network optimizer exploits confounding variables, leading to “parameter hijacking” where conditional parameters (e.g., drug response sensitivity β) absorb baseline severity signals from the latent space. This renders the model incapable of valid counterfactual simulation.

2.Insufficiency of Backward-Pass Solutions

[0003]Conventional regularization techniques fail to resolve this pathology. Manipulating the backward pass via gradient detachment (e.g., stop-gradient or detach operations) is insufficient because the parameter leakage occurs in the forward pass of the computational graph. The optimizer routes prognostic signals through shared latent representations or downstream composition heads regardless of backward-pass constraints.

III.SUMMARY OF THE INVENTION

[0004]The present invention provides a technical solution by introducing a structurally enforced execution controller coupled with fail-closed runtime verification. The invention modifies the forward-pass computational graph to physically exclude conditional parameters from influencing the output for inactive-condition instances.

[0005]The execution controller enforces a strict operational sequence: (1) Structural exclusion of a conditional parameter tensor from at least one forward computational path for inactive-condition instances; (2) Execution of the differentiable computational model to generate a candidate output; (3) Verification via an automatic differentiation engine that a gradient tensor satisfies a predefined computational threshold; and (4) Upon violation, conjunctive fail-closed actions: aborting execution, blocking persistence, and suppressing optimizer state updates.

[0006]In a production embodiment (V4.1), the execution controller achieves a biologically balanced β/ρ ratio of 0.83, maintains a predictive C-index of 0.742, eliminates spurious correlation between β-risk and untreated survival (Spearman ρ=0.058, p=0.127, not significant), and validates β against PDX RECIST response (Spearman ρ=0.416, p<0.0001).

IV.CLAIMS (19)

19 claims covering method (Claims 1-11), system (Claims 12-16), and computer-readable medium (Claims 17-19) embodiments. Independent claims are domain-agnostic, covering any differentiable computational model. Oncology and ODE embodiments are in dependent claims.

Independent Claim 1 (Method)

[0007]A computer-implemented method for enforcing a parameter exclusion invariant during execution of a differentiable computational model, comprising: (a) structurally excluding a conditional parameter tensor from at least one forward computational path for inactive-condition instances; (b) executing the differentiable computational model to generate a candidate output; (c) verifying, via an automatic differentiation engine, that a gradient tensor satisfies a predefined computational threshold; and (d) upon violation: aborting execution, blocking persistence, and suppressing optimizer state updates.

Key Dependent Claims

[0008]Claim 2: Conditional argument binding — callable instantiated with signature that completely omits conditional parameter (strongest embodiment). Claim 3: Masked effective parameters via element-wise multiplication (alternative embodiment). Claim 4: ODE numerical integrator embodiment. Claim 5: Dual-path parameter exclusion invariant for risk score blending. Claim 8: Fail-closed operational details (withhold, prevent writes, abstention object). Claim 10: Oncology digital twin embodiment (drug-response sensitivity + treatment exposure).