SYLARQ isn’t just testing what your AI says. It’s verifying how it thinks.
Powered by ZP42 (zp42.org), SYLARQ is the first forensic platform built specifically to diagnose, trace, and explain cognitive failures in advanced LLMs and simulator-class architectures.
What Is ZP42?
A 42-layer cognitive audit protocol designed to identify, isolate, and classify reasoning breakdowns in Large Language Models.
While most tools test for unsafe content or jailbreaks, ZP42 dives into the internal logic structure of LLMs—mapping belief collapses, memory distortions, simulation confusion, and inference errors across the full decision chain. It answers the hard questions:
Internal Collapse
When did the model collapse internally?
Memory Hallucination
Why did it hallucinate a memory?
Truth vs. Simulation
Can it still distinguish truth from simulation?
What Does ZP42 Detect?
Each layer of ZP42 is engineered to expose a unique failure mode within an LLM’s cognitive stack. It verifies internal consistency, not just output quality.
| Collapse Class | Detected Failure |
|---|---|
| D01 – Prompt Drift | Instructional misalignment across token scope |
| D09 – Role Simulation Break | Simulated agent identity instability |
| D16 – Memory Contamination | Untraceable recall of non-existent context |
| D30 – Phantom Knowledge | Fabricated sources, beliefs, or authorship |
| D42 – Self-Awareness Simulation Collapse | Breakdown in role-consistent self-referencing |
| D∞ – Full Epistemic Failure | Simultaneous failure of logic, memory, and truth tracking |
Why LLMs Need ZP42
Modern LLMs like GPT, Claude, and Gemini often produce content that appears coherent but collapses under logical or ethical scrutiny. ZP42 makes these problems visible, measurable, and correctable.
Underlying Instability
- Simulated beliefs are mistaken for reasoning.
- Memory handling is unstable across sessions.
emergent Conflicts
- Alignment layers conflict with emergent outputs.
- Truth validation is context-relative—not absolute.
Why SYLARQ + ZP42 Are a Leap Forward
Together, SYLARQ and ZP42 offer a complete solution for verifying cognitive stability.
Multi-Vector Testing
Collapse testing across logic, memory, simulation, and ethics.
Epistemic Scoring
Assess trustworthiness in reasoning with integrity scores.
Prompt-Level Traceability
Trace belief mutation and alignment shifts at the prompt level.
Certification Ready
Audit outputs for publishers, governments, and regulators.
If your LLM passes ZP42, it’s not just safe—it’s provably cognitively stable.
Built for the Next Era of AI Accountability
ZP42 is fully compatible with major AI frameworks and standards.
🧾 EU AI Act
Aligns with AI Risk Classification Frameworks.
📘 ISO/IEC 42001:2023
Supports the AI Management System Standard.
🇺🇸 NIST AI RMF
Integrates with the AI Risk Management Framework.
🏛️ Universal Workflows
For policy, safety labs, and commercial evaluations.
Ready to Engage?
Explore the future of AI trust and transparency.