yogsoth-ai Headroom Estimation
Purpose
Quantify the remaining improvement potential for a task by estimating various performance ceilings and computing the gap between current SOTA and those ceilings. Distinguishes between theoretical limits (information-theoretic), practical limits (current paradigm), and human performance baselines.
Input Schema
| Field | Type | Description |
|---|---|---|
| task_name | string | The target task |
| current_sota | object | {method, score, metric, dataset, date} |
| human_performance | object | {score, conditions, source} or null |
| theoretical_bounds | object | {bound_type, value, derivation} or null |
Output Schema
{
"task": "string",
"dataset": "string",
"metric": "string",
"ceilings": {
"theoretical": {
"value": null,
"type": "information_theoretic|bayes_optimal|combinatorial",
"derivation": "string",
"confidence": "high|medium|low|speculative"
},
"human": {
"value": null,
"conditions": "string",
"source": "string",
"is_expert": true,
"confidence": "high|medium|low"
},
"practical": {
"value": null,
"assumptions": "string",
"based_on": "string",
"confidence": "medium|low|speculative"
}
},
"headroom": {
"vs_theoretical": null,
"vs_human": null,
"vs_practical": null,
"interpretation": "string"
},
"saturation_assessment": {
"status": "saturating|active_progress|early_stage|unknown",
"evidence": "string",
"years_to_human_parity": null
}
}