Data
Benchmarks
Empirical performance data across 30-day evaluation cycles
Evolution Hypothesis
We propose that a smaller, specialized model enhanced with persistent memory and adaptive learning can outperform larger static models within a specific domain after sufficient interaction. The crossover point occurs around Week 3.
Performance over time
StaticCHO
Cognitive Performance Index
CPI = (Accuracy × 0.35) + (Adaptation × 0.25) + (Consistency × 0.20) + (Memory × 0.15) + (Innovation × 0.05)
CPI Comparison
30-Day Improvement
Detailed Metrics
| Metric | Day 1 | Day 7 | Day 30 | Δ |
|---|---|---|---|---|
| Task Accuracy | 62% | 81% | 98% | +58% |
| Code Consistency | 45% | 78% | 97% | +116% |
| Error Recovery | 48% | 72% | 94% | +96% |
| First-Attempt Success | 51% | 74% | 89% | +75% |
| Context Utilization | 70% | 91% | 99.7% | +42% |
System Comparison
| System | Week 1 CPI | Week 4 CPI | Trajectory |
|---|---|---|---|
| Static AI (baseline) | 88 | 86 | Declining |
| Koji + CHO | 58 | 96 | Ascending |
Methodology
Duration: 30-day continuous interaction cycles
Tasks: Complex multi-step problem solving, code generation, domain-specific queries
Interaction Frequency: 50-100 interactions per week
Evaluation: Blind scoring by domain experts