kanaria007/agi-structural-intelligence-protocols
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posted
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update
about 13 hours ago
✅ New Article: *Evaluation as a Goal Surface* (v0.1)
Title:
🧪 Evaluation as a Goal Surface: Experiments, Learning Boundary, and ETH-Aware A/B
🔗 https://huggingface.co/blog/kanaria007/evaluation-as-a-goal-surface
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Summary:
Most “evaluation” quietly collapses into a single number—and then we optimize the wrong thing.
This article reframes evaluation as a *goal surface*: multi-objective, role-aware, and ethics-bounded.
In SI-Core terms, experiments become *first-class Jumps (E-Jumps)* with explicit contracts, traces, and gates—so you can run A/B tests, shadow evals, and adaptive rollouts *without violating ETH, confusing principals/roles, or learning from unsafe data*.
> Don’t optimize a metric.
> Optimize a goal surface—under explicit constraints.
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Why It Matters:
• Prevents Goodhart failures by treating evaluation as *multi-goal + constraints*, not a scalar leaderboard
• Makes experimentation auditable: *EvalTrace* answers “what changed, for whom, why, and under what policy”
• Enables *ETH-aware A/B*: assignment, exposure, and stopping rules respect safety/fairness boundaries
• Connects experiments to governance: *Learning Boundary (LB)* + rollout control (PoLB) instead of “ship and pray”
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What’s Inside:
• What EVAL is in SI-Core, and *who* is being evaluated (agents / roles / principals)
• “Experiments as Jumps”: *E-Jump request/draft* patterns and contracts
• *ETH-aware variant testing* (including ID/role constraints at assignment time)
• Shadow evaluation + off-policy evaluation (how to learn without unsafe intervention)
• Role & persona overlays for EVAL (role-aware scoring, persona-aware reporting)
• *EvalTrace* for audits + incident review, plus “evaluate the evaluators” test strategies
• Practical experiment design: power/sample size, early stopping, multi-objective bandits, causal inference
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📖 Structured Intelligence Engineering Series
this is the *how-to-design / how-to-run experiments safely* layer.
published
an
article
about 13 hours ago
Evaluation as a Goal Surface: Experiments, Learning Boundary, and ETH-Aware A/B
posted
an
update
2 days ago
✅ New Article: *Role & Persona Overlays* (v0.1)
Title:
🎭 Role & Persona Overlays: Multi-Agent Identity in SI-Core
🔗 https://huggingface.co/blog/kanaria007/role-and-persona-overlays
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Summary:
Early SI-Core diagrams often assume a single “user → Jump → effect” pipeline. Real deployments don’t: cities, schools, hospitals, OSS projects, and regulators all share the same runtime. This article introduces *Role & Persona Overlays*—a first-class identity layer that answers, for every Jump:
*Who is this for (principal), who is acting (agent), under what authority (role), and through which viewpoint (persona)?*
Roles constrain *what actions are allowed* (capabilities + goal-surface projections). Personas only change *how results are rendered*—they must never silently change the chosen action.
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Why It Matters:
• Prevents “ghost principals”: effects without a clear “on whose behalf” record
• Stops role drift: the system acting as ops/platform when it should act for a learner/citizen
• Makes audit queries trivial: *who decided what, for whom, under which delegation chain?*
• Enables multi-agent + human-in-the-loop coordination without losing accountability
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What’s Inside:
• The 4-part model: *principal / agent / role / persona*
• Role-projected *goal surface views* (global goals → per-role slices)
• Patterns: multi-agent cooperation, multi-principal conflicts, joint human+SI Jumps
• ETH/RML/MEM integration: capability enforcement + ID-aware traces
• Delegation records + chain verification (time-bounded, revocable authority)
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📖 Structured Intelligence Engineering Series
this is the practical “how to implement it safely” layer.
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