research-brainstorming

Research Brainstorming

Structured methods for creative research ideation.

When to Use

• Starting a new research direction
• Generating paper ideas
• Exploring extensions of existing work
• Overcoming creative blocks
• Finding novel angles on problems

Brainstorming Principles

Diverge, Then Converge

1. Divergent phase: Generate many ideas without judgment
2. Convergent phase: Evaluate and select best ideas

Rules for Divergent Phase

• Quantity over quality initially
• No criticism or evaluation
• Build on others' ideas
• Wild ideas are welcome
• Combine and improve ideas

Rules for Convergent Phase

• Apply evaluation criteria
• Consider feasibility
• Rank by potential impact
• Identify quick wins vs. long-term bets

SCAMPER Method

SCAMPER is a checklist for transforming existing ideas:

S - Substitute

What can be replaced?

C - Combine

What can be merged?

A - Adapt

What can be borrowed from elsewhere?

M - Modify/Magnify/Minimize

What can be changed in scale or intensity?

P - Put to Other Uses

What else could this be used for?

E - Eliminate

What can be removed?

R - Reverse/Rearrange

What can be reordered or inverted?

Morphological Analysis

Systematically explore combinations of dimensions.

Step 1: Identify Dimensions

List key aspects of your research area:

Step 2: Generate Combinations

Create a matrix and explore intersections:

Task × Model × Data × Scale × Supervision
= Many possible combinations

Step 3: Evaluate Combinations

For each interesting combination:

• Is it novel?
• Is it feasible?
• Is it interesting?
• Does it address a gap?

Template

## Morphological Analysis: [Topic]

### Dimensions
1. [Dimension 1]: [Option A, Option B, Option C]
2. [Dimension 2]: [Option A, Option B, Option C]
3. [Dimension 3]: [Option A, Option B, Option C]

### Promising Combinations
| D1 | D2 | D3 | Why Interesting |
|----|----|----|-----------------|
| | | | |

### Selected Ideas
1. [Combination]: [Why pursue this]

Cross-Domain Analogies

Find inspiration from analogous problems in other fields.

Process

1. Abstract your problem: What is it fundamentally about?
2. Find analogies: What other fields face similar challenges?
3. Study solutions: How do they address it?
4. Transfer insights: How might their solutions apply?

Analogy Sources

Template

## Cross-Domain Analogy

### Our Problem
[Description of the challenge]

### Analogous Problem
**Field**: [Field name]
**Problem**: [Their version of the challenge]
**Solution**: [How they address it]

### Transfer Opportunity
[How their insight might apply to ML]

### Research Idea
[Concrete research direction]

Assumption Reversal

Challenge fundamental assumptions.

Process

1. List assumptions in current approaches
2. For each assumption, ask "What if the opposite were true?"
3. Explore implications of reversals

Template

## Assumption Reversal: [Topic]

### Current Assumptions
1. [Assumption 1]
2. [Assumption 2]
3. [Assumption 3]

### Reversals
| Assumption | Reversal | Implication |
|------------|----------|-------------|
| More data is better | Less data could be better | Data efficiency research |
| Bigger models are better | Smaller could be better | Efficient architectures |
| Pre-training helps | Training from scratch | Task-specific models |

Problem Reframing

View the problem from different angles.

Perspectives

Reframing Prompts

• "Instead of solving X, what if we solved Y?"
• "What problem are we actually trying to solve?"
• "Who else has this problem?"
• "What would make this problem go away?"
• "What would a 10x better solution look like?"

Idea Evaluation

After generating ideas, evaluate systematically.

Criteria

Quick Feasibility Check

• Do we have/can we get the data?
• Do we have the compute?
• Do we have the expertise?
• Can we do this in our timeframe?
• Is there a clear evaluation?

References

See references/ folder for:

• brainstorming_methods.md: Additional ideation techniques