Precision systems for adaptive intelligence.
Research-grade AI safety tools for detecting instability, behavioral drift, and unsafe escalation before failure appears in model outputs.
LLM Evaluation · Inference-Time Stability · RAG & Agentic Systems · MCP Tooling
Research depth. Production readiness.
TwoQuarks is organized as a portfolio of AI safety research, instruments, framework design, and development work. Each layer points to evidence, code, or operational tooling.
Research
Preprints, empirical validation, cross-architecture findings, statistical controls, and reproducible safety probes.
→ ToolingInstruments
Molecule, PyPI package, MCP integration, provider adapters, and operational instability analysis.
→ ArchitectureFramework
The internal TwoQuarks structure: PfV, ΔL3, six flavors, inference-time control, and behavioral regime mapping.
→ Live InteractivePlayground
Run the safety probes yourself: live Molecule-style response divergence across the C1–C5 behavioral regimes.
→ ProfileAbout & Development
Independent AI safety research, engineering background, open-source work, resume, GitHub, and contact.
↓About & Development
Independent researcher and builder focused on AI safety, behavioral stability, inference-time instrumentation, and practical LLM evaluation systems.
What this is
TwoQuarks is not only a theory page. It is a working research portfolio: preprints, evaluation methods, a Python package, an MCP-facing instrument, and a playground for making the safety signal visible.
The goal is not to replace model training or policy design. The goal is to add a measurable inference-time layer for detecting drift, instability, and unsafe escalation before deployment failures reach users.