progress-quantification

name	progress-quantification
description	Track performance progress over time, quantify remaining headroom — 30 methods, 100 data points, 40 web searches budget
dependencies	{"tactics":["leaderboard-harvesting","progress-curve-construction"],"sops":["baseline-synthesis","headroom-estimation","progress-curve-fitting"]}

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

1. Arrange all extracted scores chronologically per dataset/metric
2. Use progress-curve-construction tactic for curve building
3. Identify paradigm shifts (method families that caused jumps)
4. Use headroom-estimation for ceiling analysis
5. Compare progress rates across different sub-tasks
6. Identify tasks approaching saturation vs. those with large headroom
7. 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"}]
    }
  ]
   
    
       
       
       
       
       
       
       
    
  
   
    
       
       
       
       
    
  

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