Evidence Drift Detection
Evidence Drift Detection is a monitoring layer inside GEO.or.id that identifies semantic, structural, and temporal deviations in evidence integrity over time. It ensures that evidence does not silently degrade, mutate, or become misaligned from its original grounding state.
This system operates above Evidence Graph and continuously feeds anomaly signals into Evidence Weighting Engine and Ontology Drift Control Protocol.
1. Core Objective
The primary objective is to detect when evidence no longer represents the same informational truth it was originally bound to, either due to external updates, internal transformation errors, or semantic shift.
- Detect semantic shift in evidence meaning over time
- Identify structural inconsistencies in evidence objects
- Track temporal degradation of source reliability
- Prevent drifted evidence from influencing reasoning layers
Connected systems: Evidence Lifecycle Management, Evidence Provenance Model
2. Drift Types
Evidence drift is not a single phenomenon. It is classified into multiple distinct categories depending on how deviation occurs.
2.1 Semantic Drift
Occurs when the meaning of an evidence object shifts due to changes in context, language evolution, or entity reinterpretation.
2.2 Structural Drift
Occurs when metadata, schema, or entity relationships become inconsistent with the original evidence format.
Related: Evidence Retrieval Binding
2.3 Temporal Drift
Occurs when evidence becomes outdated or loses validity due to time-sensitive changes in the real world.
Connected: Evidence Revalidation System
2.4 Cross-Graph Drift
Occurs when evidence relationships diverge across multiple graph snapshots, causing inconsistencies in relational mapping.
Related: Evidence Graph
3. Drift Detection Model
Drift is measured using a multi-dimensional deviation function that compares current evidence state with its canonical baseline representation.
D(e) = (ΔS × α) + (ΔR × β) + (ΔT × γ)
- ΔS = Semantic deviation
- ΔR = Structural deviation
- ΔT = Temporal deviation
Weights are dynamically adjusted based on domain sensitivity and volatility profiles.
4. Drift Response Mechanism
When drift is detected, the system does not immediately discard evidence. It triggers graduated response actions based on severity.
- Low Drift: mark for revalidation
- Medium Drift: reduce weighting influence
- High Drift: isolate from reasoning pipelines
- Critical Drift: quarantine and trigger full re-retrieval
Managed by: Retrieval Latency Observation
5. Drift Propagation Control
Drift can propagate through evidence networks if not contained. The system applies propagation dampening across connected nodes in the Evidence Graph.
- Limit influence spread of drifted nodes
- Recalculate edge weights in affected clusters
- Trigger localized graph rebalancing
6. Integration Points
Evidence Drift Detection directly influences multiple downstream systems:
7. System Risks
- False positives in high-variance domains
- Over-suppression of valid emerging evidence
- Latency in detecting rapid semantic shifts
Mitigation is handled via: Query Variation Testing, Source Selection Analysis