Deterministic Inference Runtimes

What it is

A runtime architecture where identical inputs produce bit-perfect output parity across any hardware within defined constraints. It replaces the probabilistic nature of standard inference with strict system guarantees.

Why it matters

Regulated systems require reproducible evidence. Determinism enables replayable debugging, exact audit trails, and the removal of variance in safety-critical deployments.

What I do

I design the runtime mechanisms that enforce stability, including canonical serialization and fixed-point constraints to reduce numeric drift.

Cryptographic Execution Receipts

What it is

A structured, verifiable proof artifact that cryptographically binds model inputs, internal routing decisions, and output completions into a single, signed evidentiary block.

Why it matters

Verification shifts the burden of proof from "trusting the provider" to "auditing the receipt." This is essential for compliance in government and enterprise procurement.

What I do

I build receipt schemas using BLAKE3 hashing and HKDF-SHA256 derivation to ensure that every inference event leaves a tamper-evident trail.

Cryptographic receipt verification pipeline showing flow from inputs through hashing to final verification INPUTS • Prompt • Model ID • Params CANONICAL SERIALIZATION HKDF-SHA256 (seed derivation) BLAKE3 (content addressing) RECEIPT • Input hash • Output hash • Routing log • Timestamp VERIFICATION (replay + compare)

Regulated and Offline Deployment

What it is

The engineering of AI systems for high-security environments, favoring edge compute, air-gapped stability, and local provenance over cloud-dependent API calls.

Why it matters

Strategic autonomy requires models that function without external connectivity. Reliability in these environments is the primary hurdle for government-facing AI integration.

What I do

I implement localized runtimes and deployment narratives that satisfy strict audit requirements and security-cleared operational constraints.

Mixture-of-Experts and Adapter Accountability

What it is

The management of dynamic model architectures, specifically Mixture-of-Experts (MoE) routing and LoRA hot-swapping, within a unified, verifiable framework.

Why it matters

Flexible architectures often obscure decision-making. Accountability requires that every routing choice be recorded and replayable within a deterministic runtime.

What I do

I define the logging and verification standards for MoE gating to ensure that dynamic flexibility is balanced with absolute traceability.

Mixture-of-Experts routing showing how gating decisions are logged for deterministic replay Input Token GATING NETWORK (routing decision) Logged: • Which experts • Gating scores Expert 1 Expert 2 Expert N COMBINE Output Token

Audit Trail: Every gating decision is logged with deterministic scores, enabling full replay from the execution receipt.