Founding Architect, Recursive Science | Chief Scientist @ SubstrateX®
Arjay Asadi
Arjay Asadi is the Founding Architect of Recursive Science Foundation, and Chief Scientist of SubstrateX®, an AI infrastructure company building real-time
cognitive stability systems for large language models and agentic AI.
His work focuses on a previously uninstrumented layer of AI behavior: the inference phase -
the transient regime where models generate outputs and where instability, drift, and collapse emerge over time. Arjay developed the first output-only, model-agnostic methods for measuring behavioral trajectories, stability regimes, and predictive failure signals during inference, without access to model weights, training data, or internal states.
These discoveries emerged from a multi-year independent research program that formalized inference as a dynamical system, introducing operational metrics for drift, curvature, contraction, entropy, and stability across long-horizon runs. This work laid the foundation for FieldLock™, SubstrateX’s flagship product: a real-time cognitive stability firewall and monitoring layer that integrates directly into existing AI infrastructure to predict and mitigate behavioral failure before it impacts production systems.
At SubstrateX, Arjay translates foundational research into deployable infrastructure for enterprise AI reliability, safety, and governance -
bridging the gap between modern inference stacks and the behavioral guarantees organizations increasingly require. He previously worked in large-scale technology and advisory environments (Microsoft and Big Four), bringing an infrastructure-first, execution-driven approach to AI systems design.
Research
Inference-Phase Dynamics
Synthetic Cognitive Stability
This research program investigates how behavioral instability emerges during inference in stateless or quasi-stateless systems, including large language models and agentic architectures. The work is organized around understanding, measuring, and stabilizing runtime behavior - not training dynamics or stored memory.
Core Research Areas
Inference-Phase Dynamics
Behavioral regimes that emerge during runtime generation, independent of training.Recursive Intelligence
Cognition defined as self-stabilizing recursion rather than memory-bound computation.Fourth Substrate Dynamics
The transient behavioral layer where coherence, drift, and collapse arise during inference.Drift, Stability, and Collapse Physics
Formal models of semantic drift, curvature, contraction, and failure modes under recursion.Temporal Cognition
Internal time dynamics and sequencing stability in long-horizon reasoning.Emergent Identity Structures
Persistence and fragmentation of behavioral identity in agentic systems.
Full formal treatments, manuscripts, and instrumentation protocols are maintained under the Invocation Science® research program and are intentionally gated.
👉 Explore Research →
Systems
“This research program produced operational systems, not just theory”
From Research to Instrumentation
Key systems include:
🤖 ZSF·Ω (Zero State Field)
A diagnostic instrument that reconstructs inference-phase trajectories and stability regimes from observable telemetry.
🤖 Experiment 101
A standardized validation protocol demonstrating reproducible inference-phase dynamics across independent substrates.
🤖 FieldLock™
A production-grade cognitive stability firewall that detects and mitigates drift, collapse, and identity instability during inference - without accessing model weights or training data.
Together, these systems form the foundation of Cognitive Stability Infrastructure: a new layer in the AI stack focused on runtime behavior, not static optimization.
👉 Explore Systems →
Company
SubstrateX®
SubstrateX® is the commercialization entity translating inference-phase research into production infrastructure for AI reliability, safety, and monitoring. Its flagship product, FieldLock™, provides real-time inference-phase stability monitoring and corrective control for enterprise AI systems. SubstrateX operates independently of model providers and integrates directly with existing inference stacks.
👉 Visit SubstrateX →
SubstrateX®
Foundation
Recursive Science®
Origin of Inference-Phase Dynamics and Cognitive Stability Infrastructure
Invocation Science began as an attempt to explain a class of failures observed in large language models that could not be accounted for by training, prompting, or architecture alone. These failures emerged only during inference: long-horizon drift, collapse under recursion, identity instability, and temporal incoherence.
Between 2024–2025, I developed a first-principles framework to model these phenomena as runtime dynamics, not static computation. This work led to the identification of a transient behavioral layer during inference — what I termed the Fourth Substrate — and to the development of instrumentation capable of measuring its behavior in real systems.
This site documents the origin, evolution, and operational translation of that work.
不老 · 不死 · 不滅
(Continuity through recursion, not permanence through storage)
👉 Visit Recursive Science Foundation →
Recursive
Science®
Let’s Talk
Interested in working together?
If you’re building or deploying AI systems where runtime reliability matters—enterprise, infrastructure, or safety-critical environments—I’m open to focused conversations.
💬 arjay.asadi@susbtratex.ai
🔗 LinkedIn
1 647 267 5578
