Edited today2.2k words5 backlinksUpdated weeklyDue in 7 days
63QualityGood •Quality: 63/100LLM-assigned rating of overall page quality, considering depth, accuracy, and completeness.Structure suggests 10020.5ImportancePeripheralImportance: 20.5/100How central this topic is to AI safety. Higher scores mean greater relevance to understanding or mitigating AI risk.18ResearchMinimalResearch Value: 18/100How much value deeper investigation of this topic could yield. Higher scores indicate under-explored topics with high insight potential.
Summary
A self-referential documentation page describing the Longterm Wiki platform itself—a strategic intelligence tool with ~550 pages, crux mapping of ~50 uncertainties, and quality scoring across 6 dimensions. Features include entity cross-linking, interactive causal diagrams, and structured YAML databases tracking expert positions on key AI safety cruxes.
Content8/13
LLM summaryLLM summaryBasic text summary used in search results, entity link tooltips, info boxes, and related page cards.ScheduleScheduleHow often the page should be refreshed. Drives the overdue tracking system.EntityEntityYAML entity definition with type, description, and related entries.Edit historyEdit historyTracked changes from improve pipeline runs and manual edits.crux edit-log view <id>OverviewOverviewA ## Overview heading section that orients readers. Helps with search and AI summaries.
Tables24/ ~9TablesData tables for structured comparisons and reference material.Diagrams7/ ~1DiagramsVisual content — Mermaid diagrams, charts, or Squiggle estimate models.Int. links23/ ~17Int. linksLinks to other wiki pages. More internal links = better graph connectivity.Ext. links13/ ~11Ext. linksLinks to external websites, papers, and resources outside the wiki.Footnotes0/ ~7FootnotesFootnote citations [^N] with source references at the bottom of the page.Add [^N] footnote citations–References4/ ~7ReferencesCurated external resources linked via <R> components or cited_by in YAML.Add <R> resource linksQuotes0QuotesSupporting quotes extracted from cited sources to back up page claims.crux citations extract-quotes <id>Accuracy0AccuracyCitations verified against their sources for factual accuracy.crux citations verify <id>RatingsN:2 R:7.5 A:1.5 C:9RatingsSub-quality ratings: Novelty, Rigor, Actionability, Completeness (0-10 scale).Backlinks5BacklinksNumber of other wiki pages that link to this page. Higher backlink count means better integration into the knowledge graph.
Issues2
QualityRated 63 but structure suggests 100 (underrated by 37 points)
A self-referential documentation page describing the Longterm Wiki platform itself—a strategic intelligence tool with ~550 pages, crux mapping of ~50 uncertainties, and quality scoring across 6 dimensions. Features include entity cross-linking, interactive causal diagrams, and structured YAML databases tracking expert positions on key AI safety cruxes.
QURIOrganizationQURI (Quantified Uncertainty Research Institute)QURI develops Squiggle (probabilistic programming language with native distribution types), SquiggleAI (Claude-powered model generation producing 100-500 line models), Metaforecast (aggregating 2,1...Quality: 48/100 (Quantified Uncertainty Research Institute)
The Longterm Wiki is a strategic intelligence platform for AI safety prioritization. Unlike general encyclopedias or community wikis, it serves as a decision-support tool for funders, researchers, and policymakers asking: "Where should the next marginal dollar or researcher-hour go?"
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The project addresses four problems in the AI safety field:
Problem
How the Wiki Addresses It
Fragmented knowledge
Consolidated, cross-linked knowledge base with ≈550 pages
Unclear cruxes
Explicit mapping of key uncertainties and expert disagreements
Poor prioritization legibility
Worldview → intervention mapping showing how assumptions lead to priorities
The wiki is deliberately opinionated about importance and uncertainty—it rates content quality, tracks expert positions on cruxes, and makes prioritization implications explicit. This distinguishes it from neutral reference works like Wikipedia or discussion platforms like LessWrong.
Content is editorially curated rather than community-contributed, ensuring consistency and quality control. Each page goes through a grading pipeline that scores novelty, rigor, actionability, and completeness.
Content Architecture
The wiki has four interconnected layers of content:
Priority rankings, robust interventions, high-VOI research
Derived from above
Major Sections
Section
Content
Page Count
Example Pages
Knowledge Base
Risks, interventions, organizations, people
≈350
Deceptive AlignmentRiskDeceptive AlignmentComprehensive analysis of deceptive alignment risk where AI systems appear aligned during training but pursue different goals when deployed. Expert probability estimates range 5-90%, with key empir...Quality: 75/100, AI Safety InstitutesPolicyAI Safety Institutes (AISIs)Analysis of government AI Safety Institutes finding they've achieved rapid institutional growth (UK: 0→100+ staff in 18 months) and secured pre-deployment access to frontier models, but face critic...Quality: 69/100
AI Transition Model
Comprehensive factor network with outcomes and scenarios
npm run crux -- --help # Show all domains
npm run crux -- validate # Run all validators
npm run crux -- analyze # Analysis and reporting
npm run crux -- fix # Auto-fix common issues
npm run crux -- content # Page management
npm run crux -- generate # Content generation
Compare interpretabilitySafety AgendaInterpretabilityMechanistic interpretability has extracted 34M+ interpretable features from Claude 3 Sonnet with 90% automated labeling accuracy and demonstrated 75-85% success in causal validation, though less th...Quality: 66/100 vs governance approaches
Crux identification
Crux mapping shows which uncertainties matter most
Which assumptions drive different funding priorities?
Expert landscape
Expert profiles with positions
Who believes what about deceptive alignment?
Gap analysis
Quality scores reveal under-developed areas
Which important topics lack quality coverage?
For Researchers
Use Case
Wiki Feature
Example
Literature synthesis
Consolidated coverage with citations
Find all sources on a specific risk
Gap identification
Coverage analysis, importance vs quality
What important topics need more research?
Position mapping
Disagreement visualization
Where do Yudkowsky and Christiano diverge?
Model building
Causal diagrams as starting points
Use wiki models as research scaffolding
For Policymakers
Use Case
Wiki Feature
Example
Risk taxonomy
Structured hierarchy with assessments
Navigate from high-level categories to specific risks
Intentional limitation, links to broader resources
Early stage
Incomplete coverage
Active development, prioritized expansion
No real-time data
Static forecasts
Links to MetaforecastProjectMetaforecastMetaforecast is a forecast aggregation platform combining 2,100+ questions from 10+ sources (Metaculus, Manifold, Polymarket, etc.) with daily updates via automated scraping. Created by QURI, it pr...Quality: 35/100 for live data
Relationship to QURI Ecosystem
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Tool
Relationship to Longterm Wiki
SquiggleProjectSquiggleSquiggle is a domain-specific probabilistic programming language optimized for intuition-driven estimation rather than data-driven inference, developed by QURI and adopted primarily in the EA commu...Quality: 41/100
SquiggleAIProjectSquiggleAISquiggleAI is an LLM tool (primarily Claude Sonnet 4.5) that generates probabilistic Squiggle models from natural language, using ~20K tokens of cached documentation to produce 100-500 line models ...Quality: 37/100
LW models could be converted to executable Squiggle estimates
MetaforecastProjectMetaforecastMetaforecast is a forecast aggregation platform combining 2,100+ questions from 10+ sources (Metaculus, Manifold, Polymarket, etc.) with daily updates via automated scraping. Created by QURI, it pr...Quality: 35/100
LW links to relevant forecasts as evidence for claims
Squiggle Hub
Potential future integration for interactive models embedded in pages
About This WikiE755Technical documentation for the Longterm Wiki platform covering content architecture (~550 MDX pages, ~100 entities), quality scoring system (6 dimensions on 0-10 scale), data layer (YAML databases...Quality: 55/100 — Technical overview for contributors
Style guidesE763Internal style guide for wiki content creation, emphasizing flexible hierarchical structure over rigid templates, integrated arguments over sparse sections, and selective use of visualizations. Pro...Quality: 34/100 for content creation
Automation toolsE757Comprehensive technical documentation for wiki maintenance automation, covering page improvement workflows (Q5 standards requiring 10+ citations, 800+ words), content grading via Claude API (~\$0.0...Quality: 41/100 for development workflows
Page typesE739Documents LongtermWiki's four-level page classification system (content, stub, documentation, overview) with explicit validation rules for each type, where content pages receive full quality gradin...Quality: 65/100 for classification system
InterpretabilitySafety AgendaInterpretabilityMechanistic interpretability has extracted 34M+ interpretable features from Claude 3 Sonnet with 90% automated labeling accuracy and demonstrated 75-85% success in causal validation, though less th...Quality: 66/100
Concepts
Page TypesPage TypesDocuments LongtermWiki's four-level page classification system (content, stub, documentation, overview) with explicit validation rules for each type, where content pages receive full quality gradin...Quality: 65/100Wikipedia and AI ContentConceptWikipedia and AI ContentWikipedia faces three-way AI pressure: being consumed as training data (47.9% of ChatGPT citations), infiltrated by AI content (~5% of new articles), and losing traffic to AI summaries (8% decline ...Quality: 56/100AI-Assisted Knowledge ManagementConceptAI-Assisted Knowledge ManagementA comprehensive survey of AI-assisted knowledge management tools (Obsidian plugins, Notion AI, NotebookLM, RAG frameworks) with specific cost figures (\$4-6/page for Longterm Wiki pipeline) and a c...Quality: 48/100Epistemic Tools Tools OverviewEpistemic Tools Tools OverviewA well-organized directory of epistemic tools (forecasting platforms, knowledge coordination systems, verification tools) relevant to AI safety research, noting that most have small user bases conc...Quality: 49/100Automation ToolsAutomation ToolsComprehensive technical documentation for wiki maintenance automation, covering page improvement workflows (Q5 standards requiring 10+ citations, 800+ words), content grading via Claude API (~\$0.0...Quality: 41/100Knowledge BaseKnowledge BaseInternal style guide for wiki content creation, emphasizing flexible hierarchical structure over rigid templates, integrated arguments over sparse sections, and selective use of visualizations. Pro...Quality: 34/100
Analysis
GrokipediaProjectGrokipediaGrokipedia is xAI's AI-generated encyclopedia that grew from 800K to 6M+ articles in three months (Oct 2025–Jan 2026), but was documented by multiple outlets (Wired, NBC, PolitiFact) to have right-...Quality: 50/100SquiggleProjectSquiggleSquiggle is a domain-specific probabilistic programming language optimized for intuition-driven estimation rather than data-driven inference, developed by QURI and adopted primarily in the EA commu...Quality: 41/100MetaforecastProjectMetaforecastMetaforecast is a forecast aggregation platform combining 2,100+ questions from 10+ sources (Metaculus, Manifold, Polymarket, etc.) with daily updates via automated scraping. Created by QURI, it pr...Quality: 35/100SquiggleAIProjectSquiggleAISquiggleAI is an LLM tool (primarily Claude Sonnet 4.5) that generates probabilistic Squiggle models from natural language, using ~20K tokens of cached documentation to produce 100-500 line models ...Quality: 37/100
Organizations
QURI (Quantified Uncertainty Research Institute)OrganizationQURI (Quantified Uncertainty Research Institute)QURI develops Squiggle (probabilistic programming language with native distribution types), SquiggleAI (Claude-powered model generation producing 100-500 line models), Metaforecast (aggregating 2,1...Quality: 48/100