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posted an update 1 day ago
✅ Article highlight: *Adversarial SI* (art-60-050, v0.1)
TL;DR:
If SI-Core is meant for real deployment, it cannot assume benevolent actors. This article looks at *adversarial SI*: malicious Jumps, malicious RML calls, poisoned Genius Traces, metric gaming, compromised peers, and policy-plane artifacts as attack surfaces.
The core claim is simple: *OBS / ID / MEM / ETH / EVAL / PoLB are not just governance layers — they are also a defensive fabric.*
Read:
https://huggingface.co/datasets/kanaria007/agi-structural-intelligence-protocols/blob/main/article/60-supplements/art-60-050-adversarial-si.md
Why it matters:
• treats SI-Core invariants as security invariants, not just safety abstractions
• makes abuse structurally expensive through traceability, fail-closed ETH, and scoped capabilities
• reuses *SCover / SCI / CAS* as security and forensics signals
• treats red-teaming as structured experimentation, not ad hoc chaos
What’s inside:
• an SI-native threat taxonomy: malicious Jumps, RML abuse, peer spoofing, metric gaming, policy-plane tampering
• defensive uses of *ID / OBS / MEM / ETH / EVAL / PoLB*
• malicious Genius Traces and how to vet or quarantine them
• *incident response as an SIR-native process*
• federated trust, revocation, quarantine, and graceful degradation
• red-team EvalSurfaces and abuse-resistant PoLB recipes
Key idea:
The goal is not invincibility. It is to make abuse *hard to execute, easy to detect, and easy to learn from* using the same structural language as the rest of SI-Core.
updated a dataset 1 day ago
kanaria007/agi-structural-intelligence-protocols posted an update 3 days ago
✅ 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:
https://huggingface.co/datasets/kanaria007/agi-structural-intelligence-protocols/blob/main/article/60-supplements/art-60-049-research-under-si-core.md
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.
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