Rapid Triage

Rapid coarse screening: when the number of gaps is very large (50+), first use binary filtering to quickly eliminate obviously unqualified gaps, then lightly score the survivors, compressing the candidate set down to a fine-rankable size.

When to Use

• The number of gaps is very large (50+), making full multi-dimensional scoring of each gap infeasible
• Time or compute resources are limited and a quick preliminary ranking is needed
• As a precursor step to multi-criteria-ranking or portfolio-optimization
• You need to quickly show a team "which gaps are not worth considering"

Thinking Framework

Core principle: don't finely rank garbage. Eliminate first, then fine-rank.

Two filtering rounds:

Round 1: binary filtering (Keep / Drop) Ask three yes/no questions about each gap:

• Is this gap within our research scope? (scope filter)
• Is this gap technically solvable (not a philosophical problem, not an unbounded problem)? (solvability filter)
• Is this gap already being adequately addressed by sufficient recent work? (novelty filter)

Any answer of "no" → Drop. Passing all three → advance to round 2.

Round 2: light scoring (1–3 points, two dimensions) Score surviving gaps on only two dimensions:

• Importance (1–3): a rough estimate of field impact
• Feasibility (1–3): whether progress can be made within 6 months with existing resources

Score = importance × feasibility (max 9 points). Take top-K (K = target fine-ranking count) into the next stage.

Key insight: the three round-1 questions must be answered quickly (no more than 30 seconds per gap); no deep analysis allowed. Speed is the core value of this strategy.

Budget Gate

| Tier | Input gap count | Round-1 retention rate | Round-2 output | Final output | |------|------------|------------|---------|---------| | S | 50–80 | ≤60% | top-15 | Candidate set + elimination-rationale summary | | M | 81–150 | ≤50% | top-20 | Candidate set + elimination-rationale summary | | L | 150+ | ≤40% | top-30 | Candidate set + elimination-rationale summary + category statistics |

Default Reference Flow

1. Call the gap-normalization SOP: normalize gap format, generate a one-sentence summary for each gap
2. Run round-1 binary filtering: answer the three yes/no questions for each gap, mark Keep / Drop
3. Record Drop rationale (out of scope / unsolvable / already adequately researched)
4. Call the importance-scoring SOP on the Keep set (1–3 coarse score)
5. Call the feasibility-scoring SOP on the Keep set (1–3 coarse score)
6. Call the scoring-matrix-construction tactic: build a light scoring matrix
7. Sort by importance × feasibility, take top-K
8. Call the priority-synthesis SOP: output the candidate set + elimination statistics

context-checkpoint

After each round, record:

• Round-1 filtering result (Keep/Drop counts + Drop-reason distribution)
• Round-2 scoring matrix (surviving gaps × 2 dimensions)
• Final candidate set (top-K gap list)
• Suggested next strategy (multi-criteria-ranking or portfolio-optimization)

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Available Tactics

Optional, no fixed order; the final leaf is always a sop.

| Tactic | When to use | | --- | --- | | hypothesis-formation-scoring-matrix-construction | Tactic: orchestrate multi-dimensional scoring SOPs to build a comprehensive assessment matrix for all gaps |

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

| SOP | When to use | | --- | --- | | gap-normalization | SOP: Unify gaps from different sources into the standard GapRecord format | | priority-synthesis | SOP: synthesize all scoring data into a final gap priority list and attack-path suggestions |

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