The mathematics of
verifiable truth.
Modern foundation models are inherently probabilistic, prone to hallucinations, and susceptible to adversarial evasion. Zal Logic Inc. conducts foundational research at the intersection of causal structural models, automated SMT theorem proving, and split-conformal inference.
Interactive Epistemic Simulators
Direct mathematical instruments evaluating Pearlian counterfactual surgery and split-conformal coverage calibration in real-time.
Demonstrating do-calculus intervention and counterfactual isolation of generative artifacts.
Click any node in the causal diagram to inspect structural equation parameters.
Guaranteed finite-sample coverage certificates: P(Y ∈ C(X)) ≥ 1 - α, regardless of data distribution.
Every forensic dossier emitted by Zal Logic Inc. guarantees zero unbound false accusations under exchangeable sensor distributions.
Foundational Preprints & Treatises
Bicameral Neurosymbolic Synthesis for Real-Time Media Forensics Under Adversarial Perturbations
Chief Scientific Lead, Core Logic Engine Lead, Adversarial Defense Lead
We formalize the bicameral coupling of high-velocity deep perceptual manifolds (Q1 Neural Perception) with first-order axiomatic SMT satisfaction engines (Q3 Formal Verification). Under adversarial perturbations designed to evade convolutional and transformer discriminators, our bicameral architecture demonstrates an invariant failure detection guarantee with 0% false acceptance of synthetic artifacts.
Exact Causal DAG Surgeries and Counterfactual Pearlian Bounds for Synthetic Anomaly Attribution
Causal Explainability Engineer, Statistical Rigor Engineer
Addressing the confounding bias introduced by foundation model priors in deepfake detection, we construct a Pearlian Structural Causal Model (SCM). By executing do-calculus graph surgery, we isolate the direct causal effect of generative latent perturbations against physical sensor noise (PRNU, ELA, and rPPG), establishing defensible counterfactual attribution bounds.
Split-Conformal Prediction Sets for Formal Epistemic Guarantees in Perceptual Forensic Manifolds
Statistical Rigor Engineer, Metacognitive System Lead
Conventional deep neural networks emit overconfident uncalibrated softmax posteriors. We formulate split-conformal inference across high-dimensional forensic tensors, proving distribution-free finite-sample coverage guarantees P(Y in C(X)) >= 1 - alpha for any user-specified significance level alpha.