Page StatusResponse
Edited today2.2k words
63
QualityGoodQuality: 63/100Human-assigned rating of overall page quality, considering depth, accuracy, and completeness.Structure suggests 10012
ImportancePeripheralImportance: 12/100How central this topic is to AI safety. Higher scores mean greater relevance to understanding or mitigating AI risk.15
Structure15/15Structure: 15/15Automated score based on measurable content features.Word count2/2Tables3/3Diagrams2/2Internal links2/2Citations3/3Prose ratio2/2Overview section1/124TablesData tables in the page7DiagramsCharts and visual diagrams24Internal LinksLinks to other wiki pages0FootnotesFootnote citations [^N] with sources13External LinksMarkdown links to outside URLs%4%Bullet RatioPercentage of content in bullet lists
Updated weeklyDue in 7 days
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.
Issues2
QualityRated 63 but structure suggests 100 (underrated by 37 points)
Links4 links could use <R> components
Longterm Wiki
Project
Longterm Wiki
Part of Tools & Platforms
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.
2.2k words
Quick Assessment
| Dimension | Assessment | Evidence |
|---|---|---|
| Scope | AI safety focused | ≈550 pages covering risks, interventions, organizations, cruxes |
| Content Model | Curated synthesis | Editorial control, quality scoring, not community wiki |
| Unique Value | Crux mapping | Explicit uncertainty tracking, worldview→priority linkages |
| Technical | Modern stack | Astro/Starlight, React, interactive causal diagrams |
| Open Source | Fully | MIT licensed, GitHub repository |
| Status | Active development | Launched 2025, ongoing content expansion |
Project Details
| Attribute | Details |
|---|---|
| Name | Longterm Wiki (LongtermWiki) |
| Organization | 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) |
| Lead | Ozzie Gooen |
| Launched | 2025 |
| Website | longtermwiki.com |
| GitHub | github.com/quantified-uncertainty/longterm-wiki |
| License | MIT |
| Platform | Static site (Astro/Starlight) |
Overview
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 |
| Slow information synthesis | Quality tracking, staleness monitoring, regular updates |
Design Philosophy
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:
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| Layer | Contents | Scale |
|---|---|---|
| Factual Foundation | Risks, interventions, organizations, people, concepts | ≈350 pages |
| Causal Models | Risk pathways, intervention effects, transition dynamics | ≈80 models |
| Uncertainty Structure | Key cruxes, expert positions, worldview clusters | ≈50 cruxes, ~30 experts |
| Strategic Implications | 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 | ≈80 | Factors, scenarios, quantitative estimates |
| Analytical Models | Risk models, cascade models, governance dynamics | ≈80 | Cascade models, dynamics models |
| Project | Vision, strategy, similar projects analysis | ≈10 | Vision, Similar Projects |
| Internal | Style guides, automation tools, technical docs | ≈30 | Automation Tools |
Content Types
| Type | Purpose | Quality Scored? | Validation |
|---|---|---|---|
| Content | Substantive knowledge base pages | Yes (0-100 scale) | Full (schema, links, escaping, quality) |
| Stub | Redirects, brief profiles | No | Minimal (compilation, links) |
| Documentation | Style guides, internal reference | No | Compilation only |
| Overview | Navigation hub pages (index.mdx) | No | Sidebar structure |
Key Features
Crux Mapping System
The wiki explicitly tracks key uncertainties that drive disagreement and prioritization:
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| Crux Category | Examples | Tracked In |
|---|---|---|
| Technical | P(deceptive alignment), interpretability tractability, mesa-optimization risk | cruxes.yaml |
| Timeline | Years to transformative AI, takeoff speed, capability jumps | estimates.yaml |
| Governance | Coordination tractability, warning shot likelihood, regulatory capacity | cruxes.yaml |
| Value | Weight on near-term vs. long-term, risk tolerance, discount rates | experts.yaml positions |
Expert positions on cruxes are tracked in structured YAML databases, enabling:
- Disagreement decomposition: Breaking "Is alignment hard?" into component empirical claims
- Worldview clustering: Grouping experts by crux positions (e.g., "short-timeline technical doomer")
- Priority implications: Showing how different assumptions lead to different resource allocations
Entity Cross-Linking
Stable ID-based linking system that survives URL reorganization:
The <EntityLink id="E274">scheming</EntityLink> risk relates to
<EntityLink id="E93">deceptive alignment</EntityLink>.
| Feature | Benefit |
|---|---|
| Stable IDs | Links survive path reorganization |
| Auto title lookup | Component fetches current title from database |
| Backlink tracking | Every page shows what links to it |
| CI validation | Broken links caught before deployment |
| Type icons | Visual distinction between risks, responses, orgs |
Interactive Causal Diagrams
ReactFlow-based cause-effect graphs for complex causal models:
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| Node Type | Purpose | Visual |
|---|---|---|
| Leaf | Input factors | Light blue |
| Intermediate | Mediating factors | White |
| Effect | Outcomes | Green/Red |
| Group | Clusters of related nodes | Dashed border |
Quality Scoring System
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Six-dimension rating system (0-10 scale, harsh—7+ is exceptional):
| Dimension | What It Measures | Weight by Page Type |
|---|---|---|
| Focus | Does it answer the title's promise? | All types |
| Novelty | Value beyond obvious sources | Higher for analysis |
| Rigor | Evidence quality and precision | Higher for reference |
| Completeness | Thorough coverage of claimed topic | Higher for reference |
| Concreteness | Specific vs. abstract recommendations | Higher for analysis |
| Actionability | Can readers make different decisions? | Higher for analysis |
Quality is set only through automated grading pipeline using Claude, never manually. The quality-source validation rule enforces this.
Data Layer
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Structured YAML databases enable data-aware components:
| Database | Contents | Size | Example Usage |
|---|---|---|---|
experts.yaml | AI safety researchers with positions on cruxes | ≈30 entries | <DataInfoBox expertId="paul-christiano" /> |
organizations.yaml | Labs, research orgs, funders | ≈15 entries | <DataInfoBox orgId="anthropic" /> |
cruxes.yaml | Key uncertainties with expert positions | ≈50 entries | <DataCrux dataId="deceptive-alignment-likelihood" /> |
estimates.yaml | Probability distributions for key variables | ≈35 entries | <DataEstimateBox dataId="p-doom" /> |
external-links.yaml | Curated external resources with metadata | ≈200 entries | <R id="hash">Link text</R> |
Technical Architecture
Stack
| Layer | Technology | Purpose |
|---|---|---|
| Framework | Astro 5 + Starlight | Static site generation, documentation theme |
| Components | React 19 | Interactive UI components |
| Styling | Tailwind CSS 4 | Utility-first styling |
| Type Safety | TypeScript + Zod | Compile-time and runtime validation |
| Graphs | ReactFlow + Dagre/ELK | Interactive causal diagrams |
| Diagrams | Mermaid 11 | Declarative flowcharts |
| Math | KaTeX | LaTeX rendering |
| Data | YAML → JSON | Structured entity databases |
| UI Components | shadcn/ui + Radix | Accessible component primitives |
Automation CLI
Unified CLI (crux) provides comprehensive tooling:
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
| Domain | Commands | Example |
|---|---|---|
| Validation | compile, frontmatter, entity-links, escaping, mermaid | crux validate compile --quick |
| Analysis | mentions, entity-links, quality, redundancy | crux analyze entity-links sam-altman |
| Content | grade, improve, regrade, create | crux content grade --page scheming |
| Fixes | escaping, entity-links, markdown | crux fix escaping |
| Generate | summaries, content, diagrams | crux generate summaries --batch 50 |
Validation Pipeline
| Rule | What It Checks | Failure Mode |
|---|---|---|
compile | MDX syntax and React components | Build failure |
frontmatter-schema | YAML frontmatter against Zod schema | CI failure |
dollar-signs | LaTeX escaping (\$100 not $100) | Rendering issues |
comparison-operators | JSX escaping (\<100ms not <100ms) | Build failure |
entitylink-ids | All EntityLink references exist in database | Broken links |
quality-source | Quality set by pipeline, not manually | Data integrity |
mermaid | Diagram syntax validation | Rendering issues |
markdown-lists | Consistent list formatting | Markdown errors |
Use Cases
For Funders
| Use Case | Wiki Feature | Example |
|---|---|---|
| Intervention comparison | Side-by-side pages with explicit uncertainty | 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 governanceParameterGovernance (Civ. Competence)This is a placeholder page with no actual content - only component imports that would render data from elsewhere in the system. Cannot assess importance or quality without the underlying content. 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 |
| Response options | Comprehensive intervention catalog | What governance tools exist? |
| Expert consensus | Crux tracking shows agreement/disagreement | Where do experts converge vs diverge? |
| Quick briefings | LLM summaries on every page | One-sentence takeaways for busy readers |
Comparison with Similar Projects
| Resource | Focus | Update Model | Longterm Wiki Differentiator |
|---|---|---|---|
| Wikipedia | General reference | Community edits | LW is opinionated about importance and uncertainty |
| LessWrong / AI Alignment Forum | Discussion | Continuous posts | LW is curated synthesis, not discussion platform |
| 80K Problem Profiles | Career guidance | Periodic updates | LW goes deeper on cruxes, covers more interventions |
| Stampy / AISafety.info | FAQ for newcomers | Community + fellowship | LW is strategic (prioritization), not educational |
| MIT AI Risk Repository | Academic taxonomy | Research updates | LW adds expert positions, crux mapping, intervention analysis |
| EA Forum Wiki | Integrated tags | Tag-based | LW is standalone with dedicated editorial |
Lessons from Similar Projects
The Similar Projects Analysis identified key success factors:
| Factor | Failed Projects | LW Approach |
|---|---|---|
| Ownership | Arbital (committee), EA Wiki v1 (volunteer) | Dedicated editorial lead |
| Funding | Most wikis (volunteer-only) | Paid core team |
| Scope | Arbital (everything) | AI safety prioritization only |
| Integration | Standalone wikis (low traffic) | Links to LW/EAF, doesn't replace |
| Updates | Survey papers (stale in 1-2 years) | Quality tracking, staleness monitoring |
Strengths and Limitations
Strengths
| Strength | Evidence | Impact |
|---|---|---|
| Crux-first design | 50+ tracked uncertainties, expert positions | Enables disagreement decomposition |
| Quality control | Automated grading, 6-dimension scoring | Consistent content standards |
| Cross-linking | 550+ pages with stable entity references | Knowledge graph navigation |
| Open source | MIT license, public GitHub | Transparency, reproducibility |
| Modern stack | Astro, React, TypeScript | Fast, maintainable, accessible |
| Interactive visualizations | ReactFlow graphs, Mermaid diagrams | Complex relationships made legible |
Limitations
| Limitation | Impact | Mitigation |
|---|---|---|
| Single editorial voice | May reflect particular worldview | Explicit about opinions, tracks disagreement |
| Staleness risk | Content can become outdated | Quality tracking, freshness monitoring, dated pages |
| Limited community contribution | Slower content growth | Focus on quality over volume, open for feedback |
| AI safety focus | Narrow scope | 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 | LW documents probabilistic concepts; Squiggle enables quantification |
| 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 |
Development Roadmap
Based on the Vision Document:
| Phase | Goals | Status |
|---|---|---|
| Foundation | Core knowledge base structure, 30+ high-quality pages, initial crux taxonomy | Complete |
| Depth | 80+ quality pages, full crux graph, worldview archetypes | In progress |
| Polish | Expert review, interactive worldview→priority tool, public launch | Planned |
| Maintenance | Quarterly review cycle, community contribution guidelines | Planned |
Contributing
The wiki is open source and welcomes contributions:
| Contribution Type | How to Help |
|---|---|
| Content feedback | Report issues or suggest improvements via GitHub Issues |
| Technical contributions | PRs welcome for components, validation, automation |
| Expert input | Contact for crux positions, disagreement mapping |
| Bug reports | File issues for broken links, rendering problems |
See Internal Documentation for:
- About This Wiki — Technical overview for contributors
- Style guides for content creation
- Automation tools for development workflows
- Page types for classification system