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updated
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about 6 hours ago
kanaria007/agi-structural-intelligence-protocols
posted
an
update
about 8 hours ago
✅ New Article: *Effectful Ops That Don’t Break the World* (v0.1)
Title:
🧾 Effectful Ops in SI-Core: RML and Compensator Patterns
🔗 https://huggingface.co/blog/kanaria007/effectful-ops-in-si-core
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Summary:
Structured Intelligence systems don’t just *think*—they *change the world* (payments, bookings, city actuators, learning/medical records). In distributed reality, partial failures and retries are normal, so “do it once” is a myth.
This article is a practical cookbook for making effectful operations *retry-safe, reversible (when possible), and auditable*, using *RML levels (1→3)*, *Sagas + compensators*, and “single storyline” effect traces—then measuring quality via *RBL / RIR / SCI*.
> A compensator is *another effect*, not a magical “undo”.
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Why It Matters:
• Prevents double-apply / half-committed states by defaulting to *idempotency + durable traces*
• Makes rollback *engineering-real*: compensators must be *idempotent*, monotone toward safety, and bounded to a durable terminal/pending state
• Handles “can’t undo” honestly: model *partial reversibility* + remaining risk + follow-up tasks
• Turns failure handling into metrics you can operate: *RBL (rollback latency), RIR (rollback integrity), SCI (structural inconsistencies)*
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What’s Inside:
• RML levels overview: *RML-1 (idempotent effects)* → *RML-2 (Sagas/compensators)* → *RML-3 (goal-native reversible flow graphs)*
• Compensator patterns: idempotent refunds, append-only “compensating logs”, corrective/restitution effects
• Cross-domain templates (payments / reservations / city / learning) + common pitfalls (ghost holds, out-of-order msgs)
• A full walkthrough: partial success → compensate → re-plan & re-apply as *one coherent conversation with the world*
• Implementation path: effect records → idempotency → mini-sagas → metrics → lift critical flows toward RML-3
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📖 Structured Intelligence Engineering Series
this is the *how-to-design / how-to-operate* layer for effectful systems.
posted
an
update
2 days ago
✅ New Article: *Designing Ethics Overlays* (v0.1)
Title:
🧩 Designing Ethics Overlays: Constraints, Appeals, and Sandboxes
🔗 https://huggingface.co/blog/kanaria007/designing-ethics-overlay
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Summary:
“ETH” isn’t a content filter, and it isn’t just prompt hygiene.
This article frames *ethics as runtime governance for effectful actions*: an overlay that can *allow / modify / hard-block / escalate*, while emitting a *traceable EthicsTrace* you can audit and explain.
The key move is to treat safety/rights as *hard constraints or tight ε-bounds*, not a soft “ethics score” that gets traded off against convenience.
> Safety / basic rights are never “weighted-summed” against speed.
> They’re enforced—then you optimize inside the safe set.
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Why It Matters:
• Prevents silent trade-offs (fairness/privacy/safety “lost in weights”)
• Makes “Why did it say no?” answerable via *machine-grade traces + human-grade explanations*
• Adds *appeals + controlled exceptions (break-glass)* so ETH doesn’t become unchallengeable authority
• Enables safe policy iteration with *ETH sandboxes* (replay/shadow/counterfactual), not blind prod tuning
• Gives operators real KPIs: block rate, appeal outcomes, false positives/negatives, fairness gaps, latency
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What’s Inside:
• How ETH sits in the runtime loop (OBS → candidates → ETH overlay → RML)
• A layered rule model: *baseline (“never”) / context (“allowed if…”) / grey (“escalate”)*
• Concrete flows: appeal records, exception tokens, SLA-based review loops
• ETH sandbox patterns + an evaluation loop for policy changes
• Performance + failure handling (“hot path”, fail-safe) and common anti-patterns to avoid
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📖 Structured Intelligence Engineering Series
this is the *how-to-design / how-to-operate* layer for ETH overlays that survive real-world governance.
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