DNAI Biotech

Patent Pending

U.S. Provisional Application No. 64/029,329

System and Method for Stabilized Stochastic Inference and Fail-Closed Integrity Gating in Neural Networks

A system for runtime integrity preservation during stochastic inference in neural networks. The invention prevents state mutation of non-parameter buffers (such as Batch Normalization running statistics) through deterministic cryptographic verification per forward pass, automated atomic remediation, and fail-closed gating. The system controls execution authorization and transmission of inference result objects to remote endpoints, ensuring that Monte Carlo Dropout uncertainty quantification produces correct, reproducible uncertainty estimates without silent variance collapse.

20 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, and No. 63/991,263. All prior provisional applications are incorporated herein by reference in their entireties.

I.FIELD OF THE INVENTION

[0001]The present invention relates generally to computer reliability, memory integrity, and artificial intelligence. More specifically, it relates to systems and computer-implemented methods for runtime integrity preservation during stochastic inference in neural networks, preventing state mutation of non-parameter buffers through deterministic cryptographic verification per forward pass, automated atomic remediation, and fail-closed gating, with applications to controlling the execution authorization and transmission of inference result objects to remote endpoints.

II.BACKGROUND OF THE INVENTION

1.The Batch Normalization / Dropout Conflict

[0002]A critical technical failure in computer functionality occurs when applying Monte Carlo (MC) Dropout to modern neural network architectures utilizing Batch Normalization (BN) for single-sample inference (batch size = 1). When a model's dropout modules are set to “training mode” to enable stochastic sampling, the track_running_stats attribute of BN layers remains active. Consequently, the BN layers attempt to update their running mean and variance buffers based on the single input's statistics.

[0003]This exponential decay toward zero causes a posterior collapse of the predictive uncertainty. The phenomenon results in numerical instability and silently corrupts the model's internal state in memory, yielding silently incorrect uncertainty estimates. Standard MC Dropout implementations inadvertently enable this mutation during single-sample inference, causing silent variance collapse that is undetectable by parameter-only hashing.

2.Computational Overhead and Non-Determinism

[0004]When performing multi-objective stochastic evaluation of downstream candidate configurations using stochastic models, standard sampling introduces independent noise for each branch. Furthermore, stochastic dropout layers produce different random masks on each forward pass, making output verification across remediation retries impossible without strict deterministic seed management across both CPU and GPU devices.

III.SUMMARY OF THE INVENTION

[0005]The invention provides a computer-implemented system that: (1) freezes Batch Normalization running statistics during MC Dropout inference via a BNFreeze Module that snapshots and restores buffer state; (2) performs per-forward-pass SHA-256 integrity verification of all non-parameter buffers to detect silent mutation; (3) implementsautomated atomic remediation that restores corrupted buffers from a verified snapshot without requiring full model reload; and (4) enforces fail-closed gating that prevents transmission of inference results when integrity verification fails.

[0006]The system additionally provides deterministic seed management across CPU and GPU devices, enabling exact reproducibility of stochastic inference passes for regulatory audit trails (21 CFR Part 11 compliance). A cryptographically signed InferenceReceipt accompanies each result, binding the output to verified model state, seed values, and integrity check outcomes.

IV.CLAIMS

20 claims covering the BNFreeze module, per-pass SHA-256 buffer integrity verification, atomic remediation, fail-closed gating, deterministic seed management, and signed inference receipts for regulatory-grade stochastic inference in clinical digital twin platforms.

Full specification available upon request. Contact us for the complete patent application document.