From 4d9628db8b1617be00b1aa7b86c3d5f124066c8c Mon Sep 17 00:00:00 2001 From: artale Date: Sat, 13 Jun 2026 11:31:12 +0200 Subject: [PATCH] add asi-pathway: map arXiv:2606.12683 four AGI-to-ASI pathways to our architecture --- src/examples/test-asi.ts | 19 +++++ src/upgrades/asi-pathway.ts | 151 ++++++++++++++++++++++++++++++++++++ 2 files changed, 170 insertions(+) create mode 100644 src/examples/test-asi.ts create mode 100644 src/upgrades/asi-pathway.ts diff --git a/src/examples/test-asi.ts b/src/examples/test-asi.ts new file mode 100644 index 0000000..2db316c --- /dev/null +++ b/src/examples/test-asi.ts @@ -0,0 +1,19 @@ +import { pathwayReportCard, assessASIPathways } from "../upgrades/asi-pathway.js"; + +function log(msg: string): void { process.stdout.write(msg + "\n"); } + +function main(): void { + log("\n" + pathwayReportCard()); + log("\n=== Gap Closure Needed ==="); + const assessment = assessASIPathways(); + for (const p of assessment.pathways) { + if (p.gaps.length > 0) { + log(`\n${p.name} gaps:`); + for (const g of p.gaps) log(` ○ ${g}`); + } + } + log("\nPathway 4 (Multi-Agent Collectives) is our architecture."); + log("Paper: Genewein et al. 2026. From AGI to ASI. arXiv:2606.12683.\n"); +} + +main(); diff --git a/src/upgrades/asi-pathway.ts b/src/upgrades/asi-pathway.ts new file mode 100644 index 0000000..4621070 --- /dev/null +++ b/src/upgrades/asi-pathway.ts @@ -0,0 +1,151 @@ +/** + * ASI Pathway Mapper — maps arXiv:2606.12683's four ASI pathways to our system. + * + * Paper: "From AGI to ASI" — Genewein, Franklin, Lerchner, Orseau, Albanie, + * Bales, Wyeth, Chan, Gabriel, Leibo, Dafoe, Hutter, Graepel, Legg + * (Google DeepMind, June 2026) + * + * Four pathways from AGI → ASI: + * 1. Scaling AGI — more compute, more data + * 2. AI Paradigm Shifts — new architectures, new algorithms + * 3. Recursive Improvement — AI that improves AI + * 4. Multi-Agent Collectives — large-scale agent coordination (OUR ARCHITECTURE) + * + * This module maps each pathway to our existing modules and identifies gaps. + */ + +export interface ASIPathway { + id: number; + name: string; + description: string; + ourCoverage: string[]; + gaps: string[]; + priority: "high" | "medium" | "low"; +} + +export interface ASIAssessment { + pathways: ASIPathway[]; + totalCoverage: number; + strongestPathway: string; + weakestPathway: string; + recommendedFocus: string; +} + +const PATHWAYS: ASIPathway[] = [ + { + id: 1, + name: "Scaling AGI", + description: "More compute, more data, larger models. The current paradigm.", + ourCoverage: [ + "SafetyBoundary — routes to best available model per task", + "CostTracker + CostCap — manages compute budget", + "BenchmarkRunner — measures if scaling improves compounding", + ], + gaps: [ + "No auto-scaling compute backend (cloud provisioning)", + "No model comparison harness (A/B test models on same task)", + ], + priority: "low", + }, + { + id: 2, + name: "AI Paradigm Shifts", + description: "New architectures, algorithms, or training methods.", + ourCoverage: [ + "13 executors across 5+ model families — adapts to any paradigm", + "PhaseExecutor interface — model-agnostic by design", + "CompositeExecutor — routes different phases to different architectures", + ], + gaps: [ + "No automated architecture discovery", + "No paradigm shift detection (when to switch model families)", + ], + priority: "low", + }, + { + id: 3, + name: "Recursive Improvement", + description: "AI systems that improve AI systems. Self-modifying feedback loops.", + ourCoverage: [ + "DreamingSystem — sleep → review → extract → codify → sharpen", + "RoutineEvolution — analyze execution history, auto-suggest improvements", + "SkillSharpener — meta-review, quality gate, auto-apply", + "FeedbackLoop — plan → execute → observe → reflect → refine", + "BenchmarkRunner — measures if improvements are compounding", + "HallucinationDetector — catches output degradation", + ], + gaps: [ + "No automated weight/architecture modification (not publicly available)", + "No meta-learning across skill evolutions (each evolution is independent)", + ], + priority: "medium", + }, + { + id: 4, + name: "Multi-Agent Collectives", + description: "Large-scale coordination of AI agents achieving collective superintelligence.", + ourCoverage: [ + "MultiAgentOrchestrator — orchestrator → specialists → collect → verify", + "AgentTeams — 3-tier: orchestrator → leads → workers", + "AgentChains — sequential pipeline: scout → plan → build → review", + "KimiExecutor — up to 300 parallel sub-agents natively", + "SkillRegistry — shared knowledge across the collective", + "PersistentMemory — episodic/semantic/procedural across agents", + "SafetyBoundary — routes sub-agents to appropriate models", + "DecompositionGuard — detects coordinated multi-agent attacks", + ], + gaps: [ + "No emergent behavior detection (when the collective acts unexpectedly)", + "No agent specialization optimization (auto-assign roles based on performance)", + "No inter-agent communication protocol (agents currently share state, not messages)", + ], + priority: "high", + }, +]; + +/** + * Assess our system's ASI pathway readiness. + * Pathway 4 (Multi-Agent Collectives) is our strongest — it's what we architected for. + */ +export function assessASIPathways(): ASIAssessment { + const sorted = [...PATHWAYS].sort((a, b) => b.ourCoverage.length - a.ourCoverage.length); + + return { + pathways: PATHWAYS, + totalCoverage: PATHWAYS.reduce((s, p) => s + p.ourCoverage.length, 0), + strongestPathway: sorted[0].name, + weakestPathway: sorted[sorted.length - 1].name, + recommendedFocus: sorted[0].name, + }; +} + +/** Generate a pathway report card */ +export function pathwayReportCard(): string { + const assessment = assessASIPathways(); + const lines: string[] = []; + + lines.push("╔══════════════════════════════════════════════╗"); + lines.push("║ ASI Pathway Readiness (arxiv:2606.12683) ║"); + lines.push("╚══════════════════════════════════════════════╝"); + lines.push(""); + lines.push(`Strongest: ${assessment.strongestPathway}`); + lines.push(`Weakest: ${assessment.weakestPathway}`); + lines.push(`Total modules: ${assessment.totalCoverage} across all pathways`); + lines.push(`Recommended focus: ${assessment.recommendedFocus}`); + lines.push(""); + + for (const p of assessment.pathways) { + const coverage = p.ourCoverage.length; + const gaps = p.gaps.length; + const bar = "▓".repeat(Math.min(coverage, 10)) + "░".repeat(Math.max(0, 10 - coverage)); + lines.push(`[${bar}] Pathway ${p.id}: ${p.name}`); + lines.push(` Coverage: ${coverage} modules, Gaps: ${gaps}`); + lines.push(""); + } + + lines.push("Citation: Genewein et al. (2026). From AGI to ASI. arXiv:2606.12683."); + lines.push("DeepMind's fourth pathway — Multi-Agent Collectives — is our architecture."); + lines.push(""); + + return lines.join("\n"); +}