Structured frameworks for discovering the next research idea. This skill provides ten complementary ideation lenses that help researchers move from vague curiosity to concrete, defensible research proposals. Each framework targets a different cognitive mode—use them individually or combine them for comprehensive exploration.
When to Use This Skill
Starting a new research direction and need structured exploration
Feeling stuck on a current project and want fresh angles
Evaluating whether a half-formed idea has real potential
Preparing for a brainstorming session with collaborators
Transitioning between research areas and seeking high-leverage entry points
Reviewing a field and looking for underexplored gaps
Do NOT use this skill when
:
You already have a well-defined research question and need execution guidance
You need help with experimental design or methodology (use domain-specific skills)
You want a literature review (use
scientific-skills:literature-review
)
Core Ideation Frameworks
1. Problem-First vs. Solution-First Thinking
Research ideas originate from two distinct modes. Knowing which mode you are in prevents a common failure: building solutions that lack real problems, or chasing problems without feasible approaches.
Problem-First
(pain point → method):
Start with a concrete failure, bottleneck, or unmet need
Naturally yields impactful work because the motivation is intrinsic
Risk: may converge on incremental fixes rather than paradigm shifts
Solution-First
(new capability → application):
Start with a new tool, insight, or technique seeking application
Often drives breakthroughs by unlocking previously impossible approaches
Risk: "hammer looking for a nail"—solution may lack genuine demand
Workflow
:
Write down your idea in one sentence
Classify it: Is this problem-first or solution-first?
If problem-first → verify the problem matters (who suffers? how much?)
If solution-first → identify at least two genuine problems it addresses
For either mode, articulate the gap: what cannot be done today that this enables?
Self-Check
:
Can I name a specific person or community who needs this?
Is the problem I am solving actually unsolved (not just under-marketed)?
If solution-first, does the solution create new capability or just replicate existing ones?
2. The Abstraction Ladder
Every research problem sits at a particular level of abstraction. Deliberately moving up or down the ladder reveals ideas invisible at your current level.
Direction
Action
Outcome
Move Up
(generalize)
Turn a specific result into a broader principle
Framework papers, theoretical contributions
Move Down
(instantiate)
Test a general paradigm under concrete constraints
Empirical papers, surprising failure analyses
Move Sideways
(analogize)
Apply same abstraction level to adjacent domain
Cross-pollination, transfer papers
Workflow
:
State your current research focus in one sentence
Move UP: What is the general principle behind this? What class of problems does this belong to?
Move DOWN: What is the most specific, constrained instance of this? What happens at the extreme?
Move SIDEWAYS: Where else does this pattern appear in a different field?
For each new level, ask: Is this a publishable contribution on its own?
Example
:
Current
"Improving retrieval accuracy for RAG systems"
Up
"What makes context selection effective for any augmented generation system?"
Down
"How does retrieval accuracy degrade when documents are adversarially perturbed?"
Sideways
"Database query optimization uses similar relevance ranking—what can we borrow?"
3. Tension and Contradiction Hunting
Breakthroughs often come from resolving tensions between widely accepted but seemingly conflicting goals. These contradictions are not bugs—they are the research opportunity.
Common Research Tensions
:
Tension Pair
Research Opportunity
Performance ↔ Efficiency
Can we match SOTA with 10x less compute?
Privacy ↔ Utility
Can federated/encrypted methods close the accuracy gap?
Generality ↔ Specialization
When does fine-tuning beat prompting, and why?
Safety ↔ Capability
Can alignment improve rather than tax capability?
Interpretability ↔ Performance
Do mechanistic insights enable better architectures?
Scale ↔ Accessibility
Can small models replicate emergent behaviors?
Workflow
:
Pick your research area
List the top 3-5 desiderata (things everyone wants)
Identify pairs that are commonly treated as trade-offs
For each pair, ask: Is this trade-off fundamental or an artifact of current methods?
If artifact → the reconciliation IS your research contribution
If fundamental → characterizing the Pareto frontier is itself valuable
Self-Check
:
Have I confirmed this tension is real (not just assumed)?
Can I point to papers that optimize for each side independently?
Is my proposed reconciliation technically plausible, not just aspirational?
4. Cross-Pollination (Analogy Transfer)
Borrowing structural ideas from other disciplines is one of the most generative research heuristics. Many foundational techniques emerged this way—attention mechanisms draw from cognitive science, genetic algorithms from biology, adversarial training from game theory.
Requirements for a Valid Analogy
:
Structural fidelity
The mapping must hold at the level of underlying mechanisms, not just surface similarity
Describe your problem in domain-agnostic language (strip the jargon)
Ask: What other field solves a structurally similar problem?
Study that field's solution at the mechanism level
Map the solution back to your domain, preserving structural relationships
Generate testable predictions from the analogy
Validate: Does the borrowed idea actually improve outcomes?
5. The "What Changed?" Principle
Strong ideas often come from revisiting old problems under new conditions. Advances in hardware, scale, data availability, or regulations can invalidate prior assumptions and make previously impractical approaches viable.
Categories of Change to Monitor
:
Change Type
Example
Research Implication
Compute
GPUs 10x faster
Methods dismissed as too expensive become feasible
Scale
Trillion-token datasets
Statistical arguments that failed at small scale may now hold
Regulation
EU AI Act, GDPR
Creates demand for compliant alternatives
Tooling
New frameworks, APIs
Reduces implementation barrier for complex methods
Failure
High-profile system failures
Exposes gaps in existing approaches
Cultural
New user behaviors
Shifts what problems matter most
Workflow
:
Pick a well-known negative result or abandoned approach (3-10 years old)
List the assumptions that led to its rejection
For each assumption, ask: Is this still true today?
If any assumption has been invalidated → re-run the idea under new conditions
Frame the contribution: "X was previously impractical because Y, but Z has changed"
6. Failure Analysis and Boundary Probing
Understanding where a method breaks is often as valuable as showing where it works. Boundary probing systematically exposes the conditions under which accepted techniques fail.
Types of Boundaries to Probe
:
Distributional
What happens with out-of-distribution inputs?
Scale
Does the method degrade at 10x or 0.1x the typical scale?
Adversarial
Can the method be deliberately broken?
Compositional
Does performance hold when combining multiple capabilities?
Temporal
Does the method degrade over time (concept drift)?
Workflow
:
Select a widely-used method with strong reported results
Identify the implicit assumptions in its evaluation (dataset, scale, domain)
Systematically violate each assumption
Document where and how the method breaks
Diagnose the root cause of each failure
Propose a fix or explain why the failure is fundamental
Self-Check
:
Am I probing genuine boundaries, not just confirming known limitations?
Can I explain WHY the method fails, not just THAT it fails?
Does my analysis suggest a constructive path forward?
7. The Simplicity Test
Before accepting complexity, ask whether a simpler approach suffices. Fields sometimes over-index on elaborate solutions when a streamlined baseline performs competitively.
Warning Signs of Unnecessary Complexity
:
The method has many hyperparameters with narrow optimal ranges
Ablations show most components contribute marginally
A simple baseline was never properly tuned or evaluated
The improvement over baselines is within noise on most benchmarks
Workflow
:
Identify the current SOTA method for your problem
Strip it to its simplest possible core (what is the one key idea?)
Build that minimal version with careful engineering
Compare fairly: same compute budget, same tuning effort
If the gap is small → the contribution is the simplicity itself
If the gap is large → you now understand what the complexity buys
Contribution Framing
:
"We show that [simple method] with [one modification] matches [complex SOTA]"
"We identify [specific component] as the critical driver, not [other components]"
8. Stakeholder Rotation
Viewing a system from multiple perspectives reveals distinct classes of research questions. Each stakeholder sees different friction, risk, and opportunity.
Stakeholder Perspectives
:
Stakeholder
Key Questions
End User
Is this usable? What errors are unacceptable? What is the latency tolerance?
Developer
Is this debuggable? What is the maintenance burden? How does it compose?
Theorist
Why does this work? What are the formal guarantees? Where are the gaps?
Adversary
How can this be exploited? What are the attack surfaces?
Ethicist
Who is harmed? What biases are embedded? Who is excluded?
Regulator
Is this auditable? Can decisions be explained? Is there accountability?
Operator
What is the cost? How does it scale? What is the failure mode?
Workflow
:
Describe your system or method in one paragraph
Assume each stakeholder perspective in turn (spend 5 minutes per role)
For each perspective, list the top 3 concerns or questions
Identify which concerns are unaddressed by existing work
The unaddressed concern with the broadest impact is your research question
9. Composition and Decomposition
Novelty often emerges from recombination or modularization. Innovation frequently lies not in new primitives, but in how components are arranged or separated.
Composition
(combining existing techniques):
Identify two methods that solve complementary subproblems
Ask: What emergent capability arises from combining them?
Identify a complex system with entangled components
Ask: Which component is the actual bottleneck?
Example: Decomposing "fine-tuning" into data selection, optimization, and regularization reveals that data selection often matters most
Workflow
:
List the 5-10 key components or techniques in your area
Compose
Pick pairs and ask what happens when you combine them
Decompose
Pick a complex method and isolate each component's contribution
For compositions: Does the combination create emergent capabilities?
For decompositions: Does isolation reveal a dominant or redundant component?
10. The "Explain It to Someone" Test
A strong research idea should be defensible in two sentences to a smart non-expert. This test enforces clarity of purpose and sharpens the value proposition.
The Two-Sentence Template
:
Sentence 1
(Problem): "[Domain] currently struggles with [specific problem], which matters because [concrete consequence]."
Sentence 2
(Insight): "We [approach] by [key mechanism], which works because [reason]."
If You Cannot Fill This Template
:
The problem may not be well-defined yet → return to Framework 1
The insight may not be clear yet → return to Framework 7 (simplify)
The significance may not be established → return to Framework 3 (find the tension)
Calibration Questions
:
Would a smart colleague outside your subfield understand why this matters?
Does the explanation stand without jargon?
Can you predict what a skeptic's first objection would be?
Integrated Brainstorming Workflow
Use this end-to-end workflow to go from blank page to ranked research ideas.
Phase 1: Diverge (Generate Candidates)
Goal
Produce 10-20 candidate ideas without filtering.
Scan for tensions
(Framework 3): List 5 trade-offs in your field
Check what changed
(Framework 5): List 3 recent shifts (compute, data, regulation)
Probe boundaries
(Framework 6): Pick 2 popular methods and find where they break
Cross-pollinate
(Framework 4): Pick 1 idea from an adjacent field
Compose/decompose
(Framework 9): Combine 2 existing techniques or split 1 apart
Climb the abstraction ladder
(Framework 2): For each candidate, generate up/down/sideways variants
Phase 2: Converge (Filter and Rank)
Goal
Narrow to 3-5 strongest ideas.
Apply these filters to each candidate:
Filter
Question
Kill Criterion
Explain-It Test
(F10)
Can I state this in two sentences?
If no → idea is not yet clear
Problem-First Check
(F1)
Is the problem genuine and important?
If no one suffers from this → drop it
Simplicity Test
(F7)
Is the complexity justified?
If a simpler approach works → simplify or drop
Stakeholder Check
(F8)
Who benefits? Who might object?
If no clear beneficiary → drop it
Feasibility
Can I execute this with available resources?
If clearly infeasible → park it for later
Phase 3: Refine (Sharpen the Winner)
Goal
Turn the top idea into a concrete research plan.
Write the two-sentence pitch (Framework 10)
Identify the core tension being resolved (Framework 3)
Specify the abstraction level (Framework 2)
List 3 concrete experiments that would validate the idea
Anticipate the strongest objection and prepare a response
Define a 2-week pilot that would provide signal on feasibility
Completion Checklist
:
Two-sentence pitch is clear and compelling
Problem is genuine (problem-first check passed)
Approach is justified (simplicity test passed)
At least one stakeholder clearly benefits
Core experiments are specified
Feasibility pilot is defined
Strongest objection has a response
Framework Selection Guide
Not sure which framework to start with? Use this decision guide:
Committed to first idea without exploring alternatives
Run full Diverge phase
Usage Instructions for Agents
When a researcher asks for help brainstorming research ideas:
Identify their starting point
Are they exploring a new area, stuck on a current project, or evaluating an existing idea?
Select appropriate frameworks
Use the Framework Selection Guide to pick 2-3 relevant lenses
Walk through frameworks interactively
Apply each framework step-by-step, asking the researcher for domain-specific inputs
Generate candidates
Aim for 10-20 raw ideas across frameworks
Filter and rank
Apply the Converge phase filters to narrow to top 3-5
Refine the winner
Help articulate the two-sentence pitch and define concrete next steps
Key Principles
:
Push for specificity—vague ideas ("improve efficiency") are not actionable
Challenge assumptions—ask "why?" at least three times
Maintain a written list of all candidates, even rejected ones (they may recombine later)
The researcher makes the final call on which ideas to pursue; the agent facilitates structured thinking