Post
33
✅ Article highlight: *Research Under SI-Core* (art-60-049, v0.1)
TL;DR:
Modern research already has the pieces of a governed intelligence system — instruments, logs, ethics review, analysis pipelines, lab notebooks, peer review, replication.
This article asks: what happens if we treat research itself as an *SI-Core domain*?
The answer here is: experiments become *SIR-backed research episodes*, analyses become *EvalTraces*, preregistration and replication become first-class workflows, and unusually strong protocols can be promoted into *Genius Traces* for reuse.
Read:
kanaria007/agi-structural-intelligence-protocols
Why it matters:
• makes research pipelines structurally traceable instead of script-and-spreadsheet folklore
• turns replication into a designed workflow, not an afterthought
• treats reproducibility as something measurable through *SCover / SCI / CAS*
• lets strong past experiments become reusable protocol templates, not lost anecdotes
What’s inside:
• *ResearchEvalSurface* for hypotheses, evidence, and reproducibility
• *E-Jumps* for experiment design under ethics, budget, and multi-principal goals
• *SIR + EvalTrace* as machine-readable lab notebooks
• *pre-registration* as a design-only SIR phase with adherence checking
• *replication protocols* and multi-site coordination
• *living meta-analysis* over streams of SIRs
• *Genius Traces* for promoting and reusing great experimental structure
Key idea:
SI-Core does not replace science. It makes research more *legible, replayable, and governable* — so replication, auditability, and institutional memory become defaults rather than heroic extra work.
TL;DR:
Modern research already has the pieces of a governed intelligence system — instruments, logs, ethics review, analysis pipelines, lab notebooks, peer review, replication.
This article asks: what happens if we treat research itself as an *SI-Core domain*?
The answer here is: experiments become *SIR-backed research episodes*, analyses become *EvalTraces*, preregistration and replication become first-class workflows, and unusually strong protocols can be promoted into *Genius Traces* for reuse.
Read:
kanaria007/agi-structural-intelligence-protocols
Why it matters:
• makes research pipelines structurally traceable instead of script-and-spreadsheet folklore
• turns replication into a designed workflow, not an afterthought
• treats reproducibility as something measurable through *SCover / SCI / CAS*
• lets strong past experiments become reusable protocol templates, not lost anecdotes
What’s inside:
• *ResearchEvalSurface* for hypotheses, evidence, and reproducibility
• *E-Jumps* for experiment design under ethics, budget, and multi-principal goals
• *SIR + EvalTrace* as machine-readable lab notebooks
• *pre-registration* as a design-only SIR phase with adherence checking
• *replication protocols* and multi-site coordination
• *living meta-analysis* over streams of SIRs
• *Genius Traces* for promoting and reusing great experimental structure
Key idea:
SI-Core does not replace science. It makes research more *legible, replayable, and governable* — so replication, auditability, and institutional memory become defaults rather than heroic extra work.