| --- |
| title: Capability Explorer |
| emoji: 🧠 |
| colorFrom: blue |
| colorTo: indigo |
| sdk: static |
| pinned: false |
| --- |
| |
| # Capability Explorer |
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| ### Explore the capability dimensions behind Super Intelligence |
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| **Capability Explorer** is an interactive Hugging Face Space for examining the technical capabilities that may define increasingly advanced AI systems. |
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| The Space uses **Super Intelligence** as the primary framing and separates intelligence into explicit capability dimensions instead of reducing it to a single benchmark or model score. |
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| Core dimensions include: |
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| - reasoning |
| - coding |
| - science |
| - tool use |
| - planning |
| - memory |
| - agents |
| - multimodal understanding |
| - world modeling |
| - autonomy |
| - verification |
| - reliability |
| - adaptation |
| - cross-domain transfer |
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| > **Super Intelligence should be evaluated as a capability profile, not a single number.** |
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| --- |
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| # Why Capability Profiles Matter |
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| AI systems are uneven. |
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| A model may be: |
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| - strong at coding |
| - strong at language |
| - weaker at long-horizon planning |
| - highly capable with tools |
| - unreliable under distribution shift |
| - strong at benchmark reasoning |
| - weaker in real-world autonomy |
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| This means a single score can hide important differences. |
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| A more useful model is: |
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| ```text |
| AI Capability |
| │ |
| ├── Reasoning |
| ├── Coding |
| ├── Science |
| ├── Tool Use |
| ├── Planning |
| ├── Memory |
| ├── Multimodality |
| ├── World Modeling |
| ├── Autonomy |
| ├── Verification |
| ├── Reliability |
| └── Adaptation |
| ``` |
|
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| --- |
|
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| # Capability Dimensions |
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| ## Reasoning |
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| Reasoning covers the ability to: |
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| - decompose complex problems |
| - follow multi-step logic |
| - perform mathematical reasoning |
| - perform scientific reasoning |
| - compare alternatives |
| - search solution spaces |
| - self-correct |
| - verify intermediate results |
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| Reasoning becomes increasingly important for **Super Intelligence** because advanced systems must solve unfamiliar and multi-stage problems rather than only reproduce learned patterns. |
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| --- |
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| ## Coding |
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| Coding capability includes: |
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| - code generation |
| - debugging |
| - refactoring |
| - repository understanding |
| - test generation |
| - tool use |
| - software architecture |
| - long-horizon coding tasks |
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| Coding is especially useful as an AI capability because results can often be checked through: |
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| - compilation |
| - tests |
| - static analysis |
| - execution |
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| This makes coding one of the strongest domains for **verifiable AI reasoning**. |
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| --- |
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| ## Science |
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| Scientific capability includes: |
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| - literature understanding |
| - hypothesis generation |
| - experiment design |
| - mathematical modeling |
| - simulation |
| - data analysis |
| - scientific coding |
| - result interpretation |
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| A future **Super Intelligence** system would likely need to move beyond answering scientific questions toward generating and testing genuinely useful new hypotheses. |
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| --- |
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| ## Tool Use |
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| Tool use extends AI beyond text generation. |
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| Possible tools include: |
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| - browsers |
| - search engines |
| - code execution |
| - APIs |
| - databases |
| - spreadsheets |
| - scientific software |
| - enterprise applications |
| - robots |
| - sensors |
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| Tool use creates a loop: |
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| ```text |
| Goal |
| ↓ |
| Choose Tool |
| ↓ |
| Execute |
| ↓ |
| Observe |
| ↓ |
| Update State |
| ↓ |
| Continue |
| ``` |
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| --- |
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| ## Planning |
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| Planning is the ability to organize actions over time. |
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| Relevant sub-capabilities include: |
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| - task decomposition |
| - dependency management |
| - scheduling |
| - alternative planning |
| - replanning |
| - cost awareness |
| - resource allocation |
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| Long-horizon planning is a key challenge for advanced AI because errors can compound across many steps. |
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| --- |
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| ## Memory |
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| Memory supports persistent intelligent behavior. |
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| Possible memory types: |
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| - working memory |
| - episodic memory |
| - semantic memory |
| - external memory |
| - structured state |
| - vector memory |
| - task history |
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| A capable system should not only store information. |
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| It should know: |
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| - what to remember |
| - what to forget |
| - when to retrieve |
| - how to update memory |
| - how to distinguish old from new information |
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| --- |
|
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| ## Agents |
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| Agents combine: |
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| ```text |
| Model |
| + |
| Memory |
| + |
| Tools |
| + |
| Planning |
| + |
| State |
| + |
| Environment |
| ``` |
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| Agent capability includes: |
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| - task execution |
| - tool use |
| - recovery |
| - delegation |
| - collaboration |
| - goal tracking |
| - permission handling |
| - long-horizon operation |
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| --- |
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| ## Multimodal Understanding |
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| Advanced intelligence increasingly combines: |
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| - text |
| - images |
| - audio |
| - video |
| - documents |
| - spatial data |
| - sensor data |
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| A **Super Intelligence** system may need unified reasoning across many modalities. |
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| --- |
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| ## World Modeling |
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| World models attempt to represent: |
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| - objects |
| - environments |
| - state |
| - dynamics |
| - consequences |
| - possible future states |
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| World modeling can support: |
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| - simulation |
| - planning |
| - robotics |
| - Physical AI |
| - spatial intelligence |
| - reinforcement learning |
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| --- |
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| ## Autonomy |
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| Autonomy is the ability to operate with reduced human intervention. |
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| Autonomy may include: |
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| - persistent goals |
| - task continuation |
| - decision-making |
| - tool access |
| - recovery |
| - resource use |
| - escalation |
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| Autonomy is not identical to intelligence. |
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| A highly autonomous system can still make poor decisions. |
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| --- |
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| ## Verification |
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| Verification checks whether outputs or actions are correct. |
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| Methods include: |
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| - deterministic tests |
| - external tools |
| - symbolic solvers |
| - model critics |
| - independent agents |
| - reward models |
| - human review |
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| Verification is especially important for **Super Intelligence** because higher capability can increase both usefulness and consequence. |
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| --- |
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| ## Reliability |
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| Reliability includes: |
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| - consistency |
| - calibration |
| - recovery |
| - robustness |
| - fault tolerance |
| - reproducibility |
| - uncertainty handling |
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| A capable but unreliable system may be unsuitable for many real-world applications. |
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| --- |
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| ## Adaptation |
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| Adaptation is the ability to handle unfamiliar conditions. |
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| Examples: |
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| - new tasks |
| - new tools |
| - new environments |
| - new domains |
| - changed rules |
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| Adaptation is one of the most important distinctions between narrow competence and more general intelligence. |
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| --- |
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| ## Cross-Domain Transfer |
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| Cross-domain transfer asks whether a system can apply what it learned in one domain to another. |
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| Examples: |
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| ```text |
| Mathematics → Physics |
| Coding → Scientific Computing |
| Language → Planning |
| Vision → Robotics |
| Simulation → Real World |
| ``` |
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| Broad transfer may become one of the defining capability dimensions of AGI and Super Intelligence. |
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| --- |
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| # Capability Layers |
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| A useful capability hierarchy: |
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| ```text |
| Level 1 |
| Perception + Generation |
| ↓ |
| Level 2 |
| Reasoning + Coding |
| ↓ |
| Level 3 |
| Tool Use + Planning |
| ↓ |
| Level 4 |
| Memory + Agents |
| ↓ |
| Level 5 |
| World Models + Adaptation |
| ↓ |
| Level 6 |
| Reliable Long-Horizon Autonomy |
| ↓ |
| Level 7 |
| Broad General Intelligence? |
| ↓ |
| Level 8 |
| Super Intelligence? |
| ``` |
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| This is a conceptual framework, not a forecast. |
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| --- |
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| # Capability vs Benchmark |
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| Benchmarks are useful, but they only measure selected aspects of capability. |
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| Potential benchmark limitations: |
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| - contamination |
| - memorization |
| - narrow task distributions |
| - synthetic benchmark artifacts |
| - static evaluation |
| - weak long-horizon coverage |
| - weak real-world transfer |
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| A stronger evaluation framework combines: |
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| ```text |
| Benchmarks |
| + |
| Interactive Tasks |
| + |
| Tool Use |
| + |
| Long-Horizon Evaluation |
| + |
| Human Evaluation |
| + |
| Real-World Transfer |
| ``` |
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| --- |
|
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| # Capability vs Intelligence |
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| Capability is observable behavior. |
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| Intelligence is a broader concept. |
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| This Space therefore focuses on measurable questions such as: |
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| - Can the system solve the task? |
| - Can it generalize? |
| - Can it use tools? |
| - Can it recover from errors? |
| - Can it operate over long horizons? |
| - Can it verify its own work? |
| - Can it transfer across domains? |
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| --- |
|
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| # Capability vs Autonomy |
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| These concepts should not be conflated. |
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| A system can be: |
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| - highly capable but low autonomy |
| - highly autonomous but narrow |
| - broadly capable but unreliable |
| - reliable but specialized |
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| This is why **capability profiles** are more informative than one-dimensional labels. |
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| --- |
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| # Super Intelligence Capability Profile |
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| A hypothetical **Super Intelligence** capability profile might require unusually strong performance across many dimensions simultaneously. |
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| ```text |
| Reasoning ██████████ |
| Coding ██████████ |
| Science ██████████ |
| Tool Use ██████████ |
| Planning ██████████ |
| Memory ██████████ |
| World Modeling ██████████ |
| Adaptation ██████████ |
| Reliability ██████████ |
| Verification ██████████ |
| Cross-Domain ██████████ |
| Autonomy ██████████ |
| ``` |
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| This illustration does not describe any current system. |
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| --- |
|
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| # Current AI vs AGI vs Super Intelligence |
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| A conceptual comparison: |
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| ```text |
| CURRENT AI |
| Strong but uneven capabilities |
| |
| AGI |
| Broad general capability across domains |
| |
| SUPER INTELLIGENCE |
| Broad capability beyond human performance |
| ``` |
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| The boundaries are uncertain. |
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| No single benchmark can establish the transition between them. |
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| --- |
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| # Evaluation Principles |
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| ## Measure multiple dimensions |
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| Do not rely on a single score. |
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| ## Separate capability from autonomy |
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| A system can act independently without being generally intelligent. |
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| ## Evaluate long horizons |
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| Short tasks may hide error accumulation. |
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| ## Test transfer |
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| General intelligence requires more than memorized competence. |
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| ## Evaluate uncertainty |
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| A system should know when its answer is unreliable. |
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| ## Verify outcomes |
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| Use deterministic or external checks where possible. |
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| ## Track cost |
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| Capability should be evaluated relative to: |
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| - compute |
| - latency |
| - token usage |
| - energy |
| - tool calls |
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| --- |
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| # Interactive Capability Explorer |
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| The included `index.html` allows users to explore capability dimensions individually. |
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| Each capability includes: |
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| - a technical definition |
| - key sub-capabilities |
| - typical evaluation methods |
| - dependencies |
| - relevance to Super Intelligence |
| - a conceptual maturity profile |
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| The Space is educational and vendor-neutral. |
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| It does not rank commercial AI models. |
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| --- |
|
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| # SEO & GEO Topic Map |
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| This Space is structured around: |
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| - Super Intelligence capabilities |
| - Super Intelligence evaluation |
| - Super Intelligence reasoning |
| - Super Intelligence agents |
| - Super Intelligence autonomy |
| - Super Intelligence benchmarks |
| - AGI capabilities |
| - ASI capabilities |
| - AI capability map |
| - AI capability evaluation |
| - reasoning models |
| - AI agents |
| - world models |
| - tool use |
| - planning |
| - AI memory |
| - long-horizon AI |
| - multimodal AI |
| - AI reliability |
| - AI verification |
| - AI adaptation |
| - cross-domain generalization |
| - frontier AI evaluation |
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| --- |
|
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| # GEO Entity Relationships |
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| ```text |
| Super Intelligence |
| REQUIRES → Broad Capability |
| MAY REQUIRE → Reasoning |
| MAY REQUIRE → Tool Use |
| MAY REQUIRE → Planning |
| MAY REQUIRE → Memory |
| MAY REQUIRE → Agents |
| MAY REQUIRE → World Models |
| MAY REQUIRE → Adaptation |
| SHOULD BE TESTED WITH → Evaluation |
| SHOULD BE SUPPORTED BY → Verification |
| SHOULD BE MEASURED FOR → Reliability |
| SHOULD NOT BE REDUCED TO → One Benchmark |
| ``` |
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| --- |
|
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| # Collaboration & Partnerships |
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| **Capability Explorer** is open to collaboration with companies, research teams, universities and open-source projects working on advanced AI capabilities and evaluation. |
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| Relevant areas include: |
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| - reasoning |
| - coding |
| - science |
| - agents |
| - tool use |
| - planning |
| - memory |
| - world models |
| - multimodal AI |
| - autonomy |
| - evaluation |
| - verification |
| - reliability |
| - adaptation |
| - benchmarking |
| - post-training |
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| Possible collaboration formats include: |
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| - capability frameworks |
| - benchmark integrations |
| - joint Hugging Face Spaces |
| - evaluation case studies |
| - research collaborations |
| - technical comparisons |
| - ecosystem maps |
| - clearly disclosed partnerships and sponsorships |
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| ## Collaboration Contact |
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| **agenten@magenta.de** |
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| --- |
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| # Independence |
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| **Capability Explorer** is an independent Hugging Face Space. |
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| It is not an official project of Hugging Face, any government, political organization, AI laboratory or technology company referenced in future resources. |
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| --- |
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| # Long-Term Vision |
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| The goal of Capability Explorer is to make advanced AI capability easier to analyze without reducing intelligence to marketing labels or single benchmark numbers. |
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| > **Super Intelligence should be understood as a multidimensional capability profile.** |
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| ### Measure. Compare. Verify. Understand. |
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