kanaria007/agi-structural-intelligence-protocols
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✅ Article highlight: *Long-Horizon Planning under SI-Core* (art-60-046, v0.1)
TL;DR:
Most discussions stop at the next Jump, the next rollout wave, or the next experiment. This article asks a harder question: how do you bind *30-second decisions* and *30-year plans* into the same structural story?
The answer here is *Plan Jumps*: long-horizon artifacts for infrastructure programs, policy trajectories, and institutional reforms, evaluated over scenario bundles, monitored with explicit replan triggers, and kept auditable through the same SIR / EVAL / SCover / SCI / CAS logic used at shorter horizons.
Read:
https://huggingface.co/datasets/kanaria007/agi-structural-intelligence-protocols/blob/main/article/60-supplements/art-60-046-long-horizon-planning-under-si-core.md
Why it matters:
• turns plans themselves into first-class, traceable objects instead of PDF promises
• connects operational Jumps, tactical adjustments, and decade-scale plans in one runtime story
• treats uncertainty, scenario comparison, and replanning as built-in structure, not afterthoughts
• keeps politics and governance explicit instead of pretending models should “choose the future”
What’s inside:
• *Plan Jumps* for 5–30 year horizons
• *scenario bundles* and long-horizon world models
• *Plan-GCS*, SCover / SCI / CAS over decades
• *policy-level Genius Replay* for reusable historical plan structure
• *PoLB + EVAL* for shadow / pilot / staged rollout of sub-policies
• *policy-to-goal contracts*, budget envelopes, and governance review cycles
• *uncertainty propagation*, confidence bands, and robust plan selection
• *replan triggers* for scheduled, threshold, event-driven, and learning-based revision
• *intergenerational equity* and future citizens as explicit principals
Key idea:
SI-Core should not only explain what happened this minute. It should also help humans steer what happens over the next 10–30 years — with plans that are structured, replayable, revisable, and politically inspectable. updated a dataset 3 days ago
kanaria007/agi-structural-intelligence-protocols posted an update 3 days ago
✅ Article highlight: *Incentives in Structured Intelligence* (art-60-045, v0.1)
TL;DR:
Most serious systems already run on incentives — budgets, tariffs, subsidies, penalties, and scarce-resource allocation. The problem is that these usually live outside the runtime as opaque spreadsheets, billing rules, or political defaults.
This article sketches how to make incentives *first-class inside SI-Core*: attach *BudgetSurface* and *CostSurface* to GoalSurface, run *ETH-aware tariff experiments* under PoLB, and treat pricing / allocation as auditable structured decisions rather than hidden knobs.
Read:
https://huggingface.co/datasets/kanaria007/agi-structural-intelligence-protocols/blob/main/article/60-supplements/art-60-045-incentives-in-structured-intelligence.md
Why it matters:
• makes economic trade-offs explicit instead of burying them in billing logic or policy spreadsheets
• prevents incentives from quietly fighting safety, fairness, or affordability goals
• lets tariff changes and budget-heavy actions be evaluated, simulated, and gated before rollout
• keeps pricing and allocation auditable with portable artifacts and normalized verdicts
What’s inside:
• *BudgetSurface / CostSurface* as typed attachments to GoalSurface
• *IncentiveLedger* for budgets, tariffs, exceptions, and compliance traces
• *PoLB modes for tariffs*: sandbox, shadow, and online rollout
• *ETH-aware A/B* for affordability and burden-by-income-band checks
• *Goal markets* for scarce resource allocation without reducing everything to tokens
• *Price discovery* as an E-Jump problem under welfare, fairness, and stability constraints
Key idea:
A serious intelligence runtime should not treat incentives as external afterthoughts. Budgets, tariffs, and price signals should be *observable, governable, and replayable* inside the same structure as safety and fairness.
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