Multi Evidence Fusion

Multi Evidence Fusion

Multi Evidence Fusion

Multi Evidence Fusion is a core synthesis layer in GEO.or.id that merges multiple heterogeneous evidence objects into a single coherent, weighted representation. It operates after cross-source correlation and before final reasoning assembly.

This system sits above Cross Source Evidence Correlation and directly feeds into AI Answer Engineering.

1. Core Objective

The primary objective is to convert fragmented, partially overlapping evidence into a unified interpretive structure without losing provenance or internal contradictions.

  • Merge multi-source evidence into unified representation
  • Preserve internal disagreement signals
  • Reduce redundancy without collapsing informational diversity
  • Prepare structured inputs for reasoning systems

Connected systems: Evidence Graph, Evidence Weighting Engine

2. Fusion Mechanism

Fusion is not averaging. It is structured synthesis across semantic, relational, and confidence dimensions.

2.1 Evidence Normalization

All incoming evidence is normalized into a shared schema before fusion. This includes entity alignment, timestamp standardization, and provenance tagging.

See: Evidence Retrieval Binding

2.2 Semantic Alignment

Evidence items are aligned in meaning space using entity-level and claim-level matching rather than document-level similarity.

Related: Semantic Matching Layer

2.3 Weighted Integration

Each evidence unit contributes to the fused output proportionally to its computed trust weight.

Driven by: Evidence Weighting Engine

2.4 Conflict Preservation

Contradictions are not removed during fusion. They are embedded as explicit divergence markers within the unified structure.

Handled by: Evidence Conflict Detection

3. Fusion Model

The fusion process can be represented as a constrained aggregation function: 

F(E) = Σ (wᵢ × eᵢ) + Δ(C)
  • eᵢ = individual evidence items
  • wᵢ = normalized evidence weights
  • Δ(C) = structured contradiction embedding

This ensures that fusion increases coherence without erasing uncertainty.

4. Fusion Output Structure

The output of Multi Evidence Fusion is a composite evidence object containing:

  • Unified semantic representation
  • Preserved source lineage map
  • Confidence distribution across claims
  • Explicit contradiction annotations

This structure is consumed by: Reasoning Layer Optimization

5. Fusion Strategies

  • Consensus Fusion: prioritizes high-agreement evidence clusters
  • Divergence-Aware Fusion: preserves structured disagreement zones
  • Weighted Hierarchical Fusion: ranks evidence before merging
  • Temporal Fusion: resolves conflicts based on time precedence

Strategy selection is governed by: Machine Trust Scoring Protocol

6. System Risks

  • Over-fusion leading to loss of signal diversity
  • Hidden contradiction suppression
  • Bias amplification from dominant evidence clusters

Mitigation is handled via: Semantic Grounding Protocol, Entity Disambiguation Protocol

7. Role in GEO Architecture

Multi Evidence Fusion is the synthesis engine of the evidence stack. It transforms distributed signals into structured intelligence while preserving uncertainty boundaries.

It is tightly integrated with: Knowledge Persistence Framework, AI Ground Truth Framework