kanaria007/agi-structural-intelligence-protocols
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✅ New Article: *Observations, Under-Observation, and Repair Loops* (v0.1)
Title:
👁️ Observations, Under-Observation, and Repair Loops: The OBS Cookbook for SI-Core
🔗 https://huggingface.co/blog/kanaria007/observations-under-observation
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Summary:
SI-Core’s rule is simple: *No effectful Jump without PARSED observations.*
This article turns that slogan into an operational design: define *observation units* (sem_type/scope/status/confidence/backing_refs), detect *under-observation* (missing / degraded / biased), and run *repair loops* instead of “jumping in the dark.”
Key clarification: under-observed conditions may still run *read / eval_pre / jump-sandbox*, but must not commit or publish (sandbox: `publish_result=false`, `memory_writes=disabled`).
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Why It Matters:
• Prevents “we had logs, so we had context” failures: *logs ≠ observations* unless typed + contract-checked
• Makes safety real: even PARSED observations should be gated by *coverage/confidence minima* (declared thresholds)
• Turns OBS into something measurable: *SCover_obs + SInt* become “OBS health” and safe-mode triggers
• Links semantic compression to reality: distinguish *missing raw* vs *compression loss*, and fix the right thing
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What’s Inside:
• A practical observation-status taxonomy: `PARSED / DEGRADED / STUB / ESTIMATED / MISSING / REDACTED / INVALID` (+ mapping to core status)
• Per-jump *observation contracts* (required sem_types, allowed statuses, age/confidence limits) + explicit fallback actions
• Fallback patterns: *safe-mode / conservative default / sandbox-only / human-in-loop*
• Repair loops as first-class: ledgered `obs.repair_request`, PLB proposals, governance review for contract changes
• Testing OBS itself: property tests, chaos drills, golden-diff for observation streams
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📖 Structured Intelligence Engineering Series
this is the *“how to operate OBS”* layer—so the system can *know when it doesn’t know* and repair over time.
published
an
article
about 1 hour ago
Observations, Under-Observation, and Repair Loops
posted
an
update
2 days ago
✅ New Article: *Designing Goal-Native Algorithms* (v0.1)
Title:
🎯 Designing Goal-Native Algorithms: From Heuristics to GCS
🔗 https://huggingface.co/blog/kanaria007/designing-goal-native-algorithms
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Summary:
Most systems still run on “Inputs → model/heuristic → single score → action”.
But real deployments have multiple goals plus non-negotiable constraints (safety, ethics, legal).
This article is a design cookbook for migrating to goal-native control: make the goal surface explicit as a **GCS vector**, enforce **hard constraints first**, then trade off soft objectives inside the safe set.
> The primary object is a GCS vector + constraint status — not a naked scalar score.
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Why It Matters:
• Stops safety/fairness from becoming silently tradable via “mystery weights”
• Makes trade-offs auditable: “why this action now?” can be reconstructed via Effect Ledger logging
• Gives a repeatable build flow: goals → constraints → action space → GCS estimator → chooser
• Shows how to ship safely: shadow mode → thresholds → canary, with SI metrics (CAS/SCover/EAI/RIR)
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What’s Inside:
• A recommended GCS convention (higher=better, scales documented, weights only for soft goals)
• Chooser patterns: lexicographic tiers, Pareto frontier, context-weighted tie-breaks
• Practical patterns: rule-based+GCS wrapper, safe bandits, planning/scheduling, RL with guardrails
• Migration path from legacy heuristics + common anti-patterns (single-scalar collapse, no ledger, no PLB/RML)
• Performance tips: pruning, caching, hybrid estimators, parallel evaluation
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📖 Structured Intelligence Engineering Series
Formal contracts live in SI-Core / GCS specs and the eval packs; this is the *how-to-design / how-to-migrate* layer.
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