yogsoth-ai Feasibility Scoring
Assess the attackability of a research gap, identify bottlenecks, and output a feasibility score.
HARD-GATE
Pipeline
- Pre-check: verify the completeness of the input GapRecord
- Method-availability assessment: is the existing methodological toolbox sufficient to attack this gap (1-5); reference: are there mature algorithms/frameworks to reuse
- Data-accessibility assessment: are the required datasets/corpora publicly available (1-5); reference: existence of public datasets, data-scale requirements
- Resource-requirement assessment: are compute/experiment resource needs within a reasonable range (1-5); reference: GPU needs, lab equipment, funding
- Time-horizon assessment: can it be completed within a typical PhD/project cycle (1-5); reference: expected workload, dependency-chain length
- Composite scoring: equal-weighted average of the four dimensions, kept to one decimal place; extract the main bottlenecks (dimensions with score ≤ 2)
- Output: return the FeasibilityScore object
Output Format
{
"gap_id": "gap_001",
"dimension_scores": {
"method_availability": { "score": 4, "rationale": "..." },
"data_accessibility": { "score": 2, "rationale": "..." },
"resource_requirements": { "score": 3, "rationale": "..." },
"time_horizon": { "score": 3, "rationale": "..." }
},
"composite_score": 3.0,
"bottlenecks": ["data_accessibility"],
"overall_rationale": "Composite rationale (2-4 sentences)"
}