ariffazil/AGI-multimodal-bridge

AGI-multimodal-bridge

Purpose

Real-world evidence is multi-modal: seismic images, well logs (tabular), geological descriptions (text), financial projections (structured data). This skill bridges modalities into unified reasoning.

Modality Pipeline

1. Detect — Identify modalities present in input (text, image, table, geospatial)
2. Route — Send each modality to its native processor:
   • Text → LLM reasoning (sequential-thinking)
   • Image → GEOX vision pipeline (geox_vision) or native vision model
   • Table → WEALTH compute pipeline or pandas/numpy analysis
   • Geospatial → GEOX map layers (geox_map_layers_list)
3. Fuse — Combine processed outputs into unified evidence frame
4. Reason — Run cross-modal reasoning chain (observation → signal → narrative)
5. Label — Tag each fused claim with epistemic rung and confidence

Fusion Rules

• Cross-modal corroboration raises confidence (two modalities agreeing > one)
• Cross-modal contradiction triggers F2 TRUTH escalation
• Missing modality is noted, not fabricated (F7 HUMILITY)

Floors

• F2 TRUTH: Each modality processed with appropriate fidelity bounds.
• F4 CLARITY: Fused output must be clearer than any single modality.
• F8 GENIUS: Cross-modal synthesis must produce insight > sum of parts.