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kanaria007
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posted an update about 1 hour 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 about 4 hours ago
kanaria007/agi-structural-intelligence-protocols
posted an update 2 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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