yogsoth-ai Progress Quantification
Purpose
Construct temporal performance trajectories for the target task. Fit progress curves, identify inflection points (paradigm shifts), estimate theoretical/practical ceilings, and quantify remaining headroom. Produces actionable intelligence about where the field is plateauing and where breakthroughs are needed.
Budget
| Resource | Floor | Target |
|---|---|---|
| Methods tracked | 20 | 30 |
| Historical data points | 70 | 100 |
| Web searches | 25 | 40 |
| Time span covered (years) | 3 | 5+ |
State Ledger
<HARD-GATE>
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Methods tracked | 0 | 30 | BLOCKED |
| Historical data points | 0 | 100 | BLOCKED |
| Web searches used | 0 | 40 | — |
| Progress curves built | 0 | 3 | — |
| Headroom estimates | 0 | 3 | — |
| Inflection points identified | 0 | 2 | — |
</HARD-GATE>
Cannot exit until historical_data_points >= 80 (80% of target).
Available Tactics
- progress-curve-construction — Build temporal performance curves with inflection detection
- leaderboard-harvesting — Historical data collection
Available SOPs
- progress-curve-fitting — Fit curves and extract trend parameters
- headroom-estimation — Estimate ceiling vs current SOTA gap
- baseline-synthesis — Produce final integrated report
Execution Guidance
- Arrange all extracted scores chronologically per dataset/metric
- Use progress-curve-construction tactic for curve building
- Identify paradigm shifts (method families that caused jumps)
- Use headroom-estimation for ceiling analysis
- Compare progress rates across different sub-tasks
- Identify tasks approaching saturation vs. those with large headroom
- Produce baseline-synthesis as the final integrated deliverable
Output Format
{
"progress_curves": [
{
"dataset": "string",
"metric": "string",
"time_series": [{"date": "string", "method": "string", "score": 0.0}],
"trend_type": "logarithmic|linear|sigmoid|stepped",
"annual_improvement_rate": 0.0,
"inflection_points": [{"date": "string", "method": "string", "cause": "string"}]
}
],
"headroom_analysis": [
{
"dataset": "string",
"metric": "string",
"current_sota": 0.0,
"human_performance": 0.0,
"theoretical_ceiling": 0.0,
"remaining_headroom_pct": 0.0,
"saturation_status": "saturating|active_progress|early_stage"
}
],
"paradigm_shifts": [
{
"year": 2024,
"method_family": "string",
"improvement_magnitude": 0.0,
"description": "string"
}
]
}
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| leaderboard-harvesting | Systematically collect performance data from platforms and papers |
| progress-curve-construction | Build performance-over-time progress curves with inflection detection |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| baseline-synthesis | Produce final structured baseline report integrating all analysis results |
| headroom-estimation | Estimate theoretical/practical ceiling vs current SOTA gap |
| progress-curve-fitting | Construct performance-over-time visualization data |