System and Method for Cryptographically Enforced Runtime Resource Gating in Differential Equation Solvers via Memory Allocation Interlock

[0003]Modern precision oncology increasingly relies on hybrid neuro-symbolic systems that combine learned representations (e.g., Variational Autoencoders, Hypernetworks) with mechanistic differential equation models to simulate tumor dynamics. In such systems, a Hypernetwork predicts physics-constrained parameters—growth rate $\rho$, drug sensitivity $\beta$, immune kill rate $\omega$—which parameterize an ODE solver that integrates tumor trajectories over time. Adaptive-step solvers such as Dormand-Prince (Dopri5) dynamically adjust step sizes based on local error estimates, enabling efficient integration of smooth trajectories.

[0004]A critical failure mode arises when out-of-distribution (OOD) parameters enter the ODE solver. When parameters lie outside the training manifold, the resulting differential equations can exhibit stiffness spirals—a phenomenon where the adaptive step controller repeatedly shrinks the step size to maintain error tolerances, causing the number of right-hand-side (RHS) function evaluations to increase from a typical 100 evaluations to 50,000 or more. Each evaluation allocates Runge-Kutta stage tensors (e.g., 7 vectors for Dopri5), rapidly exhausting GPU memory in multi-tenant environments. This failure mode is fundamentally different from conventional denial-of-service attacks—it is triggered by mathematically pathological inputs rather than network-layer abuse.

[0005]Existing protection mechanisms are fundamentally inadequate. API-level authentication validates user identity but cannot assess the mathematical tractability of a given parameter set. Post-hoc monitoring (e.g., watchdog timers, memory thresholds) can terminate a runaway computation, but only after substantial resources have already been consumed. In multi-tenant GPU clusters, the damage from even a single stiffness spiral can cascade to neighboring workloads before a watchdog can intervene.

[0006]In shared computing environments, adversarial or accidental tolerance tampering poses a significant risk. A client may submit integration requests with excessively tight absolute and relative tolerances ($\text{atol}$, $\text{rtol}$), forcing the solver into unnecessarily small steps. Authentication is computationally cheap; ODE integration is computationally expensive. Without a mechanism that gates resource allocation on the mathematical properties of the computation—not merely the identity of the requester—shared infrastructure remains vulnerable.

[0007]There is an unmet need for a system that prevents resource allocation for pathological simulations before integration begins. Such a system must bridge the gap between upstream statistical certification (Is this patient within the model’s competence envelope?) and downstream computational execution (Should the solver allocate memory for this integration?). The verification must be bounded in time and space—constant-time operations that do not themselves become a resource drain.

[0013]The system comprises a computing platform (e.g., a GPU-accelerated server) configured to execute adaptive-step ODE solvers for clinical digital twin simulations. The architecture is organized into three stages:

Stage 1: Certification Engine

Evaluates whether a patient’s data meets the model’s competence envelope. Computes the Information Sufficiency Score (ISS), out-of-distribution (OOD) status via PCA-reduced Ledoit-Wolf Mahalanobis distance, physics constraint validation, and stochastic triage via Monte Carlo dropout. Operates on the patient’s latent representation $z_{\text{full}}$ (328 dimensions) and Hypernetwork-predicted ODE parameters.

Stage 2: Token Authority

If all certification criteria pass, the Token Authority issues a signed ExecutionToken. The token is signed using Ed25519 with the authority’s private key and embeds the ISS score, canonical solver configuration hash, triage tier, PolicyProfile identifier, a cryptographic nonce for replay prevention, and a TTL.

Stage 3: Solver Interlock

The enforcement point, embedded within the ODE solver’s memory allocation routine. When the Runge-Kutta integrator requests stage tensor buffers ($k_1 \ldots k_s$, where $s=7$ for Dopri5), the interlock intercepts the allocation and performs four bounded-time verification checks. Any failure returns a null handle or raises SolverInterlockError, preventing all subsequent RHS evaluations.

[0014]The Solver Interlock is positioned within the memory allocation routine for Runge-Kutta stage tensors. For a Dormand-Prince (Dopri5) solver, this means 7 vectors of dimension $N$ (the state dimensionality) are allocated at each step. The interlock wraps the underlying allocation primitive (torch.empty() in Python,cudaMalloc() in CUDA).

[0015]The allocate_rk_stage_buffers() function (see FIG. 5) performs the following sequence:

[0016]If all four checks pass, the interlock computes an Allocation Plan:

where $N$ is the state dimensionality, $D$ is the data type size in bytes, and $P$ is the number of Runge-Kutta stages (7 for Dopri5). The computed $M$ is compared against a signed memory budget embedded in the ExecutionToken. If $M$ exceeds the budget, a BUDGET_EXCEEDED error is raised.

[0017]C++/CUDA Embodiment: In a native implementation, the Solver Interlock wraps the cudaMalloc() call within the ODE solver’s allocator. On verification failure, the wrapper returns cudaErrorInterlockDenied (a custom error code) rather than a valid device pointer, ensuring zero bytes are allocated on the GPU.

[0018]The Certification Engine performs upstream evaluation of patient eligibility via four complementary mechanisms:

A. PolicyProfile Configuration:

Per-cancer type configuration objects store ISS thresholds, ISS component weights, WSI requirements, minimum training sample counts, and token TTL. Profiles are built from validation C-indices: C < 0.55 requires aggressive abstention (threshold 0.5), 0.55–0.65 moderate (threshold 0.35), C > 0.65 permissive (threshold 0.2). 26 cancer type profiles are persisted as policy_profiles.json.

B. Additive Information Sufficiency Score (ISS):

with default weights $w_1 = 0.35$, $w_2 = 0.35$,$w_3 = 0.20$, $w_4 = 0.10$. The additive formulation avoids the zero-multiplication problem of a multiplicative score, where one bad component can kill the entire score. Per-cancer weights are loaded from PolicyProfile.

C. PCA + Ledoit-Wolf OOD Detection:

The 328-dimensional latent $z_{\text{full}}$ is projected to a PCA subspace retaining 95% of variance (typically ~46 dimensions). A Ledoit-Wolf shrinkage covariance estimator is fitted on the training distribution in this reduced space. The Mahalanobis distance of a new patient to the training centroid is computed; patients exceeding the 99th percentile threshold are flagged as OOD. In experimental validation, 1.1% of validation samples were flagged with a threshold of 25.21 (subsequently reduced to 10.31 after PCA introduction).

D. Stochastic Triage via MC Dropout:

For patients that are not flagged as OOD, the system performs 50 or more Monte Carlo dropout forward passes through the Hypernetwork, generating a distribution of treatment recommendations. Treatment stability is quantified and patients are classified into three tiers:

[0019]The ExecutionToken is a structured payload containing all information required for solver-side verification. The token is constructed as follows:

[0020]The canonical JSON is hashed using SHA-256, producing a sha256:-prefixed hex digest. The SolverConfig dataclass (integrator name, $\text{atol}$, $\text{rtol}$) follows identical canonicalization, and its hash is embedded in the token. This binding ensures that the solver configuration has not been modified between token issuance and execution.

[0021]Nonce and Replay Prevention: Each ExecutionToken contains a cryptographic nonce (16 random bytes). The solver maintains a nonce registry and rejects any token with a previously seen nonce, preventing replay attacks where a valid token is re-submitted for a different patient or modified parameters.

[0022]The system was validated on a clinical digital twin platform serving 33 cancer types with 9,393 TCGA patients and 1,031 CPTAC external validation patients.

Triage Distribution (9,393 TCGA patients):

The interlock adds less than 0.5ms of overhead per integration request, which is negligible compared to the ~5ms emulator inference time. The Ed25519 signature verification dominates at ~70µs; all other checks (hash comparison, timestamp, tier lookup) are sub-microsecond.

[0023]For patients classified as CONTESTED, the system computes an Expected Value of Perfect Information (EVPI) to identify which missing evidence modality would most improve decision confidence. The EVPI for a missing modality $x_{\text{miss}}$ is defined as:

where $H(Y \mid X_{\text{obs}})$ is the entropy of the treatment recommendation under observed evidence, and the expectation is approximated using K-nearest-neighbor (KNN) imputation in the latent space. A positive EVPI indicates that acquiring the missing modality would reduce treatment recommendation entropy.

[0024]In experimental validation:

Radiology (CT/MRI) is the highest-value evidence to collect for CONTESTED patients. The negative WSI EVPI reflects a selection bias: patients with available WSI data tend to be more clinically complex, not that WSI is inherently uninformative.