yogsoth-ai Concept Blending
Fauconnier-Turner 4-space model: Generic + Input1 + Input2 → Blended Space. Produce novel concepts by selectively projecting structure from two input mental spaces into a blended space that develops emergent structure.
State Ledger
| Resource | Target | Current | % |
|---|---|---|---|
| web-search | 25 | 0 | 0% |
| web-research | 10 | 0 | 0% |
| paper-overview | 30 | 0 | 0% |
| paper-search | 20 | 0 | 0% |
| paper-research | 10 | 0 | 0% |
HARD-GATE
Cannot exit strategy until ≥80% of each budget line is consumed OR yield targets are met with justification for remaining budget.
Available Tactics
| Tactic | Role |
|---|---|
| combination-mapping | Enumerate blend dimensions and viable combinations |
| blend-construction | Construct complete 4-space blends with emergent structure |
| emergence-detection | Detect emergent properties in completed blends |
Available SOPs
| SOP | Role |
|---|---|
| input-space-construction | Build input spaces for source concepts |
| generic-space-extraction | Extract shared abstract structure |
| blend-composition | Compose new connections in blended space |
| blend-completion | Complete blend with background knowledge |
| blend-elaboration | Run blend as mental simulation |
| vital-relation-mapping | Map vital relations between concepts |
| combinatorial-synthesis | Synthesize blending outputs |
Execution Guidance
- Select Input Concepts: Choose two concepts with rich internal structure
- Build Input Spaces: Use input-space-construction to elaborate each concept's elements, relations, and attributes
- Extract Generic Space: Use generic-space-extraction to find shared abstract structure
- Map Vital Relations: Use vital-relation-mapping to identify compression opportunities
- Compose Blend: Use blend-composition to selectively project and create new connections
- Complete Blend: Use blend-completion to recruit background knowledge
- Elaborate Blend: Use blend-elaboration to run the blend as simulation and discover emergent structure