I build reliable AI systems across multimodal evaluation, browser automation, compiler/runtime infrastructure, inference reliability, and model tooling. I am especially interested in failures at system boundaries: unreliable event streams, model-artifact mismatches, unverified storage assumptions, ambiguous evidence, and real inputs that clean benchmarks miss.

My projects turn those failures into bounded, reproducible experiments with explicit trust assumptions and machine-checkable results.

ImpactDiff is a research lab for task-aware visual regression: whether a UI change breaks a user task or damages accessibility, rather than merely changing pixels. It captures canonical screenshots, accessibility trees, and bounded layout graphs around a real coordinate-driven task, while storing model-visible evidence separately from intervention metadata, execution traces, and labels.

The current milestone runs baseline and mutated candidates in distinct fresh BrowserContexts under one verified Chromium environment, compiles and rolls back typed palette and pointer interventions, derives and replays complete evidence records, and atomically publishes immutable visible/sealed release pairs. Node 22 and 24 CI exercises 233 tests across capture, provenance, recovery, and publication boundaries. A multi-application dataset, isolated feature runner, trained models, and benchmark result remain the next research milestone; the repository makes no accuracy claim today.

SSemaphore is a correctness-first local gateway for bounded, multi-tenant Chat Completions traffic. Its admission path combines estimated-service accounting with work-deficit round robin scheduling, explicit queue and body budgets, disconnect-aware cancellation, and a fixed upstream transport boundary.

The runnable gateway accepts a strict versioned policy from a private Linux file, consumes bearer credentials through one-shot environment references, and binds only to an exact numeric loopback address. End-to-end wire tests cover admission, authenticated forwarding, signal-owned shutdown, and listener cleanup; the release gate also exercises the race detector and 32-bit checked arithmetic.

TensorKiln is a dependency-free compiler for static f32 tensor graphs. Its current vertical slice includes checked rank 0-4 types, transactional graph construction, batched MatMul and broadcasting, an independent reference interpreter, dead-code elimination, and exact structural canonicalization with composable provenance.

The graph-to-arena boundary derives compute lifetimes, applies a deterministic 64-byte interval planner, and returns a storage projection only after an independently coded reverse verifier reconstructs and agrees with every mapping, request, statistic, and allocation. Seeded brute-force oracles, exact resource boundaries, GCC/Clang builds, and sanitizers exercise these invariants. The current milestone deliberately stops before layout lowering, kernels, and optimized execution.

KVCrucible is an offline conformance lab for reconstructing LLM KV-cache metadata from unreliable publisher streams. Its bounded Rust core validates canonical JSONL and tracks per-publisher Exact / Recovering / Unknown state. It materializes deterministic drop, duplicate, and reorder schedules, then compares fresh pristine and faulted folds without turning incomplete evidence into a false divergence claim.

The implementation includes opaque semantic fingerprints, atomic cache-view projection, property-tested fault materialization, a first-class per-stream convergence oracle, and a statically linked musl release gate.

peftlint moves LoRA adapter failures into a bounded preflight step without importing model code or allocating model tensors. The current vertical slice ships pure parsers for pinned PEFT adapter configurations and safetensors manifests, including duplicate-key rejection, explicit byte and JSON budgets, checked tensor-shape arithmetic, span validation, and payload-coverage proofs.

Its versioned compatibility contract distinguishes evidence that can justify a static result from cases that must remain Unknown until runtime validation is available.

OrthoDrift finds minimal grapheme-level changes that make a retriever lose the relevant document. Its first research lane is Azerbaijani retrieval, while its core text and provenance machinery remains language-agnostic.

The current implementation runs deterministic BM25 experiments, reduces rank failures to minimal counterexamples, and emits self-contained JSONL evidence that can be independently replayed and verified against its recorded runtime environment.

• State the trust boundary, uncertainty, and non-goals before making a claim.
• Bound untrusted input, retained state, and diagnostic work before execution.
• Prefer deterministic state machines, property tests, golden vectors, pinned toolchains, and machine-readable evidence over screenshot-only demos.