rapid-triage

Research

Strategy: rapid coarse screening — two filtering rounds compress a large set of gaps into a fine-rankable candidate set

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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

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

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