🧩 Glossary
Core Concepts in Inference-Phase Dynamics
Terms
Inference Phase
The period when an AI system is actively generating outputs - answering, reasoning, planning, looping, or acting as an agent.
Unlike training, the inference phase is where behavior emerges and evolves over time.
Inference-Phase AI focuses on what happens during this runtime window.
Drift is the gradual change in an AI system’s behavior over time during inference.
It may appear as:
shifting tone or stance
changing interpretation of goals
slow loss of coherence
gradual deviation from initial intent
Drift is not random error.
It is a directional movement in behavior that can often be measured and predicted.
Curvature
Curvature describes how the context and structure of an interaction shape the “path” an AI system takes during inference.
In practice, curvature reflects:
how strongly certain interpretations pull the system
how easily behavior bends toward specific styles, roles, or conclusions
how some directions feel “natural” while others resist change
Curvature helps explain why small prompt changes can sometimes cause large behavioral shifts — and why others barely matter.
Stability Regimes
A stability regime is a distinct mode of behavior that an AI system enters during inference.
Common regimes include:
Drift - exploratory, unstable, changing behavior
Coherent / Stabilized - consistent, adaptive reasoning
Phase-locked - highly stable but potentially rigid behavior
Turbulent - competing patterns, oscillation, instability
Collapsed - degraded or brittle behavior that persists
Inference-Phase AI studies how systems enter, remain in, and transition between these regimes.
Collapse is a sharp transition from coherent behavior into persistent failure.
It can appear as:
contradiction cascades
repetitive or frozen responses
hallucination spikes
refusal lock-in
loss of recoverability even after correction
Collapse is not just “bad output.”
It is a regime change, often triggered when stability thresholds are crossed during inference.
Stability
Stability refers to a system’s ability to maintain coherent, adaptive behavior over time during inference.
A stable system:
remains consistent without becoming rigid
adapts to new input without losing structure
recovers from perturbations
avoids collapse under long-horizon operation
Stability is a runtime property, not a training metric.
Why These Terms Matter
These concepts allow us to talk about AI behavior without relying on internals like weights or training data.
They make it possible to:
detect failure before it impacts production
understand agent behavior over long horizons
design systems that are robust, not just accurate
Together, they form the foundation of Inference-Phase AI and Cognitive Stability Infrastructure.
🧩 Where to go next
If you’re new
🧭 What Is Inference-Phase AI
What inference is, why it matters, and why it constitutes a new scientific domain.
🧠 Primer in 10 Minutes
A fast, structured introduction to Recursive Science and inference-phase dynamics.
📘 Glossary
Canonical definitions for regimes, drift, curvature, worldlines, and invariants.
If you’re exploring the science
🏛 About Recursive Science
Field definition, stewardship, standards, and scientific scope.
🏫 Recursive Intelligence Institute
Institutional research body advancing Recursive Science across formal phases.
↳ Research programs, canon, publications, and thesis structure.
📚 Research & Publications
Manuscripts, frameworks, and the Recursive Series forming the Phase I canon.
If you’re technical or validating claims
🔬 Recursive Dynamics Lab
Instrumentation, experiments, and validation pathways.
🧪 Operational Validation (ZSF)
Substrate-independent validation of inference-phase field dynamics.
📊 Inference-Phase Stability Trial (IPS)
Standardized, output-only protocol for regime transitions and predictive lead-time.
📐 Observables & Invariants
The measurement vocabulary of Recursive Science.
🧭 Instrumentation
Φ / Ψ / Ω instruments for inference-phase and substrate dynamics.
📏 Evaluation Rubric
The regime-based standard used to classify stability, drift, collapse, and recovery.
If you’re industry or applied
🛡 AI Stability Firewall
High-level overview of inference-phase stability and monitoring.
🏗 SubstrateX
Applied infrastructure derived from validated research.
📄Industry Preview White Paper
How inference-phase stability reshapes AI deployment in critical environments