Creative Thinking For Research

科研创意思维 | Creative Thinking for Research

八个来自认知科学的经验性框架，应用于计算机科学和人工智能研究。与临时头脑风暴不同，这里的每个框架都有数十年的创造力研究支撑——从Koestler的二元联想（bisociation）到Kauffman的邻近可能（adjacent possible）。它们针对不同的认知操作：组合、重构、类比、约束、反转、抽象、探索边界和保持矛盾。

Eight empirically grounded frameworks from cognitive science, applied to computer science and AI research. Unlike ad-hoc brainstorming, each framework here is backed by decades of creativity research — from Koestler's bisociation to Kauffman's adjacent possible. They target distinct cognitive operations: combining, reformulating, analogizing, constraining, inverting, abstracting, exploring boundaries, and holding contradictions.

何时使用此技能 | When to Use This Skill

• 生成真正新颖的想法，而非先前工作的增量扩展

• 在单一子领域的思维中感到陷入局部最优

• 想要系统地应用创造力启发式，而非等待灵感

• 为研究 retreat 或博士级别的创意会议做准备

• 跨领域桥接，寻求结构性（非表面）连接

• Generating genuinely novel ideas, not incremental extensions of prior work

• Feeling trapped in a local optimum of thinking within a single subfield

• Wanting to systematically apply creativity heuristics rather than waiting for inspiration

• Preparing for a research retreat or PhD-level ideation session

• Bridging between fields and seeking structural (not superficial) connections

请勿在以下情况使用此技能：

• 你需要结构化的项目级头脑风暴工作流（请使用 brainstorming-research-ideas）
• 你有一个明确的问题，需要执行帮助（请使用领域特定技能）
• 你需要文献综述（请使用 scientific-skkills:literature-review）

Do NOT use this skill when:

• You need structured project-level brainstorming workflows (use brainstorming-research-ideas)
• You have a well-defined problem and need execution help (use domain-specific skills)
• You need a literature survey (use scientific-skills:literature-review)

与头脑风暴技能的关系：头脑风暴技能提供操作工作流（发散→收敛→优化）和实用过滤器。此技能提供更深入的认知引擎，驱动创造性飞跃。结合使用：用创意思维生成原始洞察，用头脑风暴来构建和评估。

Relationship to Brainstorm skill: The brainstorm skill provides operational workflows (diverge → converge → refine) and practical filters. This skill provides the deeper cognitive engines that power creative leaps. Use them together: creative-thinking to generate raw insight, brainstorm to structure and evaluate it.

框架1：组合创造性（二元联想）| Framework 1: Combinatorial Creativity (Bisociation)

新想法来自于以意想不到的方式组合现有概念。Arthur Koestler称之为二元联想（bisociation）——连接两个先前无关的参考框架，这与单一框架内的常规联想不同。

Novel ideas arise from combining existing concepts in unexpected ways. Arthur Koestler called this bisociation — connecting two previously unrelated frames of reference, as distinct from routine association within a single frame.

为什么有效：元研究一致表明，知识广度是创造性输出的先决条件。跨学科阅读的人会产生更多新颖的工作。组合本身才是创造性行为。

Why it works: Meta-research consistently shows that breadth of knowledge is a precursor to creative output. People who read across disciplines produce more novel work. The combination itself is the creative act.

在计算机科学研究中：

• 生物进化 → 优化（遗传算法）
• 博弈论 → 网络（路由机制设计）
• 统计物理 → 机器学习（玻尔兹曼机、基于能量的模型）
• 语言学 → 编程（类型论、形式语法）

In CS Research:

• Biological evolution → optimization (genetic algorithms)
• Game theory → networking (mechanism design for routing)
• Statistical physics → machine learning (Boltzmann machines, energy-based models)
• Linguistics → programming (type theory, formal grammars)

系统性二元联想工作流：

1. 选择两个领域，你至少有一定了解
2. 列出核心原语：每个领域5-10个基本概念
3. 创建叉积矩阵：行 = 领域A的概念，列 = 领域B的概念
4. 对每个单元格，问：「将A的概念应用于B的问题意味着什么？」
5. 筛选：哪些组合产生了可验证的研究问题？
6. 验证结构性深度：连接是机制性的还是仅隐喻性的？

Systematic Bisociation Workflow:

1. Select two domains you have at least passing familiarity with
2. List core primitives in each domain (5-10 fundamental concepts per domain)
3. Create a cross-product matrix: row = concepts from Domain A, column = concepts from Domain B
4. For each cell, ask: "What would it mean to apply A's concept to B's problem?"
5. Filter: Which combinations produce a non-trivial, testable research question?
6. Validate structural depth: Is the connection mechanistic or merely metaphorical?

叉积示例：

Cross-Product Example:

质量检验：强有力的二元联想不是表面隐喻（「网络像大脑」），而是结构性映射，其中机制转移（「注意力机制实现了一种类似于认知注意力过滤的选择性门控形式」）。

Quality Test: A strong bisociation is not a surface metaphor ("the network is like a brain") but a structural mapping where the mechanism transfers ("attention mechanisms implement a form of selective gating analogous to cognitive attention filtering").

自检：

• 连接是结构性的（机制映射）还是仅语言上的（标签映射）？
• 组合是否产生可验证的预测？
• 两个领域的专家是否会觉得这个连接不明显但合理？

Self-Check:

• Is the connection structural (mechanisms map) or merely verbal (labels map)?
• Does the combination generate testable predictions?
• Would an expert in both fields find the connection non-obvious but sound?

框架2：问题重构（表征改变）| Framework 2: Problem Reformulation (Representational Change)

格式塔心理学家发现，突破往往不是来自于解决所陈述的问题，而是来自于重新表征问题本身。Kaplan和Simon关于洞察的研究表明，改变问题空间——约束、抽象层次、形式化——通常是创造力所在。

Gestalt psychologists identified that breakthroughs often come not from solving the problem as stated, but from re-representing the problem itself. Kaplan and Simon's work on insight shows that changing the problem space — the constraints, the abstraction level, the formalism — is often where creativity lives.

关键转变：从「我如何解决这个问题？」到「我是否正确地思考这个问题？」

The Key Shift: From "How do I solve this problem?" to "Am I even thinking about this problem correctly?"

重构策略：

Reformulation Strategies:

工作流：

1. 用一句话陈述你当前的问题
2. 找出该陈述中的隐含假设：
   • 你使用的是什么形式化？（能用不同的吗？）
   • 目标是什么？（是正确的目标吗？）
   • 什么粒度级别？（能更粗或更细吗？）
   • 主体是谁？（能转换视角吗？）
3. 对每个假设，生成替代方案：「如果[相反的假设]呢？」
4. 对每个替代方案，问：「这种重构是否使问题更容易、更难，或以有用的方式不同？」
5. 使难题变容易的重构本身通常就是一个可发表的洞察

Workflow:

1. State your current problem in one sentence
2. Identify the hidden assumptions in that statement:
   • What formalism are you using? (Could you use a different one?)
   • What is the objective? (Is it the right objective?)
   • What level of granularity? (Could you go coarser or finer?)
   • Who is the agent? (Could you shift perspective?)
3. For each assumption, generate the alternative: "What if [opposite assumption]?"
4. For each alternative, ask: "Does this reformulation make the problem easier, harder, or different in a useful way?"
5. A reformulation that makes a hard problem easy is often a publishable insight on its own

经典计算机科学示例：

• PageRank：将「查找重要网页」从内容分析重构为图特征值问题
• Dropout：将「防止过拟合」从正则化重构为近似集成
• Attention：将「处理长序列」从记住一切重构为选择性查询

Classic CS Examples:

• PageRank: Reformulated "find important web pages" from content analysis to graph eigenvalue problem
• Dropout: Reformulated "prevent overfitting" from regularization to approximate ensemble
• Attention: Reformulated "handle long sequences" from remembering everything to selectively querying

框架3：类比推理（结构映射）| Framework 3: Analogical Reasoning (Structure-Mapping)

Dedre Gentner的结构映射理论和Kevin Dunbar对真实科学家的研究表明，类比是科学创造力的核心引擎。关键发现：表面层面的类比很常见但很弱；结构性或关系性类比——深层因果/关系结构跨领域映射——产生最强大的洞察。

Dedre Gentner's structure-mapping theory and Kevin Dunbar's studies of real scientists show that analogy is the core engine of scientific creativity. The critical finding: surface-level analogies are common but weak; structural or relational analogies — where the deep causal/relational structure maps across domains — produce the most powerful insights.

Dunbar的发现：在最成功的实验室中，遥远领域的类比推动了最重要的发现。近处类比完善想法；远处类比生成想法。

Dunbar's Finding: In the most successful labs, analogies from distant domains drove the most important discoveries. Nearby analogies refined ideas; distant analogies generated them.

类比深度层次：

Levels of Analogical Depth:

结构映射工作流：

1. 仅使用关系/因果语言描述你的问题（去除领域特定的名词）
   • 不好：「我们需要提高transformer注意力效率」
   • 好的：「我们有一个系统必须从大型集合中有选择地聚合信息，其中相关性是上下文相关的，且成本随集合大小呈二次方增长」
2. 寻找结构性匹配：还有什么其他系统从大型集合中有选择地聚合？
   • 数据库查询优化、神经科学中的视觉Attention、信息检索、资源分配
3. 选择具有真正结构性保真度的最远匹配
4. 映射解决方案机制：源领域如何解决这个问题？
5. 迁移和适应：将该机制带入你的领域时会有什么变化？
6. 生成预测：类比应该告诉你一些你以前不知道的事情

Structure-Mapping Workflow:

1. Describe your problem using only relational/causal language (strip domain-specific nouns)
   • Bad: "We need to improve transformer attention efficiency"
   • Good: "We have a system that must selectively aggregate information from a large set, where relevance is context-dependent and the cost scales quadratically with set size"
2. Search for structural matches: What other systems selectively aggregate from large sets?
   • Database query optimization, visual attention in neuroscience, information retrieval, resource allocation
3. Pick the most distant match with genuine structural fidelity
4. Map the solution mechanism: How does the source domain solve this?
5. Transfer and adapt: What changes when you bring that mechanism into your domain?
6. Generate predictions: The analogy should tell you something you didn't already know

验证清单：

• 映射是否保留了因果/关系结构（而不仅仅是标签）？
• 我能否识别出类比在我的领域做出的至少一个预测？
• 源领域的专家是否会确认该机制被正确理解？
• 类比对目标受众来说是否不明显？

Validation Checklist:

• Does the mapping preserve causal/relational structure (not just labels)?
• Can I identify at least one prediction the analogy makes in my domain?
• Would an expert in the source domain confirm the mechanism is correctly understood?
• Is the analogy non-obvious to my target audience?

框架4：约束操作（Boden框架）| Framework 4: Constraint Manipulation (Boden's Framework)

Margaret Boden的框架根据它们与约束的交互方式区分了三种形式的创造力：

Transformational creativity is the rarest and highest-impact. It happens when you change what is even considered a valid solution.

Framework 4: Constraint Manipulation (Boden's Framework)

Margaret Boden's framework distinguishes three forms of creativity based on how they interact with constraints:

**转换性创造力是最稀有且影响力最高的。**它发生在你改变什么被认为是有效解决方案的时候。

Transformational creativity is the rarest and highest-impact. It happens when you change what is even considered a valid solution.

约束分析工作流：

1. 列出你当前方法的约束（5-10个约束）：
   • 计算：「必须适合GPU内存」
   • 方法论：「需要标注数据」
   • 架构：「使用固定长度上下文」
   • 评估：「通过基准X的准确率衡量」
2. 对每个约束进行分类：
   • 硬约束：物理或逻辑上必需的（不能违反）
   • 软约束：约定或历史偶然（可以质疑）
   • 隐含约束：未声明但隐含假设（最具创新潜力）
3. 对每个软/隐含约束，问：
   • 如果放松它会怎样？（从放松「适合内存」产生流式算法）
   • 如果收紧它会怎样？（从收紧计算预算产生效率研究）
   • 如果用完全不同的约束替换它呢？
4. 最具生产力的举措通常是揭示并放弃隐含约束

Constraint Analysis Workflow:

1. List the constraints of your current approach (5-10 constraints):
   • Computational: "Must fit in GPU memory"
   • Methodological: "Requires labeled data"
   • Architectural: "Uses fixed-length context"
   • Evaluative: "Measured by accuracy on benchmark X"
2. Classify each constraint:
   • Hard: Physically or logically necessary (cannot violate)
   • Soft: Convention or historical accident (can question)
   • Hidden: Not stated but implicitly assumed (most fertile for innovation)
3. For each soft/hidden constraint, ask:
   • What if we relaxed it? (streaming algorithms from relaxing "fits in memory")
   • What if we tightened it? (efficiency research from tightening compute budgets)
   • What if we replaced it with a different constraint entirely?
4. The most productive move is often exposing and dropping a hidden constraint

约束转换的经典示例：

• 「数据必须适合内存」→ 放弃 → 流式算法、外部内存
• 「训练需要人工标签」→ 放弃 → 自监督学习
• 「模型必须是确定性的」→ 放弃 → 变分方法、扩散
• 「推理必须一次完成」→ 放弃 → 迭代优化、思维链

Classic Examples of Constraint Transformation:

• "Data must fit in memory" → dropped → streaming algorithms, external memory
• "Training requires human labels" → dropped → self-supervised learning
• "Models must be deterministic" → dropped → variational methods, diffusion
• "Inference must happen in one pass" → dropped → iterative refinement, chain-of-thought

框架5：否定与反转 | Framework 5: Negation and Inversion

取你所在领域的一个核心假设并否定它。这在De Bono的横向思维和工程领域的TRIZ方法论中有正式阐述。

Take a core assumption in your field and negate it. This is formalized in De Bono's lateral thinking and the TRIZ methodology from engineering.

模式：「如果[普遍持有的假设]是错的、不必要的或可反转的」会怎样？

The Pattern: "What if [widely held assumption] is wrong, unnecessary, or invertible?"

系统性否定工作流：

1. 列出你子领域的5-10个核心假设（那些「每个人都知道」的事情）
2. 否定每个假设并问：你会构建什么系统？
3. 评估每个否定：
   • 不一致 → 放弃
   • 已探索 → 检查条件是否已改变（见头脑风暴技能，框架5）
   • 未探索且一致 → 潜在研究方向

Systematic Negation Workflow:

1. List 5-10 core assumptions in your subfield (the things "everyone knows")
2. Negate each one and ask: What system would you build?
3. Evaluate each negation:
   • Incoherent → discard
   • Already explored → check if conditions have changed (see brainstorm skill, Framework 5)
   • Unexplored and coherent → potential research direction

计算机科学否定案例名人堂：

Negation Hall of Fame in CS:

TRIZ启发的计算机科学原则：

TRIZ-Inspired Principles for CS:

框架6：抽象与泛化阶梯 | Framework 6: Abstraction and Generalization Laddering

在抽象阶梯上上下移动是一个基本的创造性行为。Polya的启发式形式化了这一点：「你能解决一个更一般的问题吗？一个更具体的？一个类似的？」

Moving up and down the abstraction ladder is a fundamental creative act. Polya's heuristics formalize this: "Can you solve a more general problem? A more specific one? An analogous one?"

三种移动：

Three Moves:

泛化工作流：

1. 陈述你的具体结果
2. 用变量替换每个具体元素：「ResNet在ImageNet上有效」→「架构X在分布Y上有效」
3. 问：在什么条件下这成立？什么是普遍原理？
4. 如果普遍原理是新颖的 → 那就是贡献

Generalization Workflow:

1. State your specific result
2. Replace each specific element with a variable: "ResNet works for ImageNet" → "Architecture X works for distribution Y"
3. Ask: Under what conditions does this hold? What is the general principle?
4. If the general principle is novel → that is the contribution

专业化工作流：

1. 获取一个通用方法
2. 添加极端约束：小数据、高维度、对抗输入、实时需求
3. 问：方法仍然有效吗？如果没有，为什么？
4. 失败案例通常揭示方法的真正假设

Specialization Workflow:

1. Take a general method
2. Add extreme constraints: tiny data, huge dimensionality, adversarial inputs, real-time requirements
3. Ask: Does the method still work? If not, why not?
4. The failure case often reveals the method's true assumptions

何时泛化vs专业化：

• 当你有结果但没有解释时 → 泛化
• 当你有理论但没有依据时 → 专业化
• 当你被困在任一方向时 → 类比

When to Generalize vs. Specialize:

• Generalize when you have results but no explanation
• Specialize when you have theory but no grounding
• Analogize when you are stuck in either direction

框架7：邻近可能（Kauffman / Johnson）| Framework 7: The Adjacent Possible (Kauffman / Johnson)

Stuart Kauffman的概念，经Steven Johnson推广：创新发生在当前可达边界附近——邻近可能。一旦先决条件存在，新想法就变得可思考。这解释了为什么同时独立发现如此普遍——多人到达同一边界。

Stuart Kauffman's concept, popularized by Steven Johnson: innovation happens at the boundary of what is currently reachable — the adjacent possible. New ideas become thinkable once their prerequisites exist. This explains why simultaneous independent discovery is so common — multiple people reach the same boundary.

实际意义：绘制最近变得可能的内容，并探索这些赋能因素开辟的空间。

Practical Implication: Map what has recently become possible and explore the space those enablers open.

邻近可能映射工作流：

1. 列出最近的赋能因素（过去1-3年）：
   • 新硬件能力（更长上下文、更快推理、新加速器）
   • 新数据集或基准
   • 新开源工具或框架
   • 新理论结果
   • 新监管或社会条件
2. 对每个赋能因素，问：「以前不可能或不切实际的，现在这允许什么？」
3. 组合赋能因素：最强大的邻近可能来自于多个新赋能因素的交叉
4. 检查竞争：如果很多人能看到相同的邻近可能，速度或独特角度很重要

Adjacent Possible Mapping Workflow:

1. List recent enablers (last 1-3 years):
   • New hardware capabilities (longer context, faster inference, new accelerators)
   • New datasets or benchmarks
   • New open-source tools or frameworks
   • New theoretical results
   • New regulatory or social conditions
2. For each enabler, ask: "What was previously impossible or impractical that this now permits?"
3. Combine enablers: The most powerful adjacent possibles arise from the intersection of multiple new enablers
4. Check for competition: If many people can see the same adjacent possible, speed or a unique angle matters

当前的邻近可能（2025-2026）：

Current Adjacent Possibles (2025-2026):

时间信号：如果你的想法需要尚不存在的技术，它就在邻近可能之外——先搁置。如果你的想法5年前就可以做，很可能有人已经做了——检查文献。最佳点是过去6-18个月变得可行的想法。

Timing Signal: If your idea requires technology that doesn't exist yet, it's beyond the adjacent possible — park it. If your idea could have been done 5 years ago, someone probably did — check the literature. The sweet spot is ideas that became feasible in the last 6-18 months.

框架8：雅努斯式与辩证思维 | Framework 8: Janusian and Dialectical Thinking

Albert Rothenberg对杰出创造者的研究发现，同时持有两个矛盾的想法是创造性思维的标志。以罗马双面神雅努斯命名，这种思维方式不会通过选择一边来解决矛盾——它生成超越对立的新框架。

Albert Rothenberg's studies of eminent creators found that holding two contradictory ideas simultaneously is a hallmark of creative thinking. Named after Janus, the two-faced Roman god, this mode of thinking doesn't resolve contradictions by choosing a side — it generates new frameworks that transcend the opposition.

在计算机科学中：最具影响力的结果往往来自于先前被认为不可调和的张力。

In CS: The most influential results often emerge from tensions previously thought irreconcilable.

辩证思维工作流：

1. 识别你领域中的二元对立：A vs. B（两种被视为对立的方法、目标或范式）
2. 拒绝选择一边。相反，问：
   • 「一个同时实现A和B的系统会是什么样子？」
   • 「在什么条件下A-B权衡不是根本性的？」
   • 「对立是否是我们形式化问题方式的产物？」
3. 寻求综合：解决方案通常需要重新定义关系的新抽象
4. 测试综合：你能实证证明两个目标都可以实现吗？

Dialectical Thinking Workflow:

1. Identify a binary in your field: A vs. B (two approaches, goals, or paradigms treated as opposites)
2. Resist choosing a side. Instead ask:
   • "What would a system look like that achieves both A and B?"
   • "Under what conditions is the A-B trade-off not fundamental?"
   • "Is the opposition an artifact of how we formalized the problem?"
3. Seek synthesis: The resolution often requires a new abstraction that reframes the relationship
4. Test the synthesis: Can you demonstrate empirically that both goals are achievable?

自检：

• 我是否真诚地保持矛盾（而非过早解决）？
• 综合是新想法，而不仅仅是妥协（各退一步）？
• 解决方案是否改变了人们对问题的思考方式，而不仅仅是解决方案？

Self-Check:

• Am I holding the contradiction genuinely (not prematurely resolving it)?
• Is the synthesis a new idea, not just a compromise (splitting the difference)?
• Does the resolution change how people think about the problem, not just the solution?

组合框架：创意思维协议 | Combining Frameworks: A Creative Thinking Protocol

这些框架组合起来最强大。以下是深度创意思维会议的系统协议：

These frameworks are most powerful in combination. Here is a systematic protocol for a deep creative thinking session:

阶段1：映射空间（15分钟）| Phase 1: Map the Space (15 min)

1. 约束操作（框架4）：列出当前范式的所有约束。标记哪些是硬的、软的、隐含的。
2. 邻近可能（框架7）：列出改变可行性前景的最近赋能因素。

Phase 1: Map the Space (15 min)

1. Constraint Manipulation (F4): List all constraints of the current paradigm. Mark which are hard, soft, hidden.
2. Adjacent Possible (F7): List recent enablers that change the feasibility landscape.

阶段2：生成突破（30分钟）| Phase 2: Generate Disruptions (30 min)

3. 否定（框架5）：否定3个软/隐含约束。会出现什么系统？
4. 二元联想（框架1）：选择一个遥远领域，与你的领域创建叉积矩阵。
5. 问题重构（框架2）：用3种不同方式重述你的问题（改变目标、形式化、主体）。

Phase 2: Generate Disruptions (30 min)

3. Negation (F5): Negate 3 soft/hidden constraints. What systems emerge?
4. Bisociation (F1): Pick a distant field and create a cross-product matrix with your domain.
5. Problem Reformulation (F2): Restate your problem 3 different ways (change objective, formalism, agent).

阶段3：深化有希望的线索（30分钟）| Phase 3: Deepen Promising Leads (30 min)

6. 类比推理（框架3）：对每个有希望的想法，找到结构性类比并提取预测。
7. 抽象阶梯（框架6）：将每个想法向上（泛化）和向下（专业化）移动。
8. 雅努斯思维（框架8）：识别任何张力。你能综合而非选择吗？

Phase 3: Deepen Promising Leads (30 min)

6. Analogical Reasoning (F3): For each promising idea, find a structural analogy and extract predictions.
7. Abstraction Laddering (F6): Move each idea up (generalize) and down (specialize).
8. Janusian Thinking (F8): Identify any tensions. Can you synthesize rather than choose?

阶段4：评估（15分钟）| Phase 4: Evaluate (15 min)

应用两句话测试（来自头脑风暴技能）：

「[领域]目前因[原因]而困扰于[问题]。我们通过[机制]来[方法]，这之所以有效是因为[洞察]。」

任何通过所有阶段并通过两句话测试的想法都值得追求。

Phase 4: Evaluate (15 min)

Apply the two-sentence test (from the brainstorm skill):

"[Domain] currently struggles with [problem] because [reason]. We [approach] by [mechanism], which works because [insight]."

Any idea that survives all four phases and passes the two-sentence test is worth pursuing.

常见创意障碍与解锁策略 | Common Creative Blocks and Unblocking Strategies

代理使用说明 | Usage Instructions for Agents

当研究人员请求帮助进行创造性思考或新想法构思时：

1. 评估障碍：他们陷入哪种思维？（见常见创意障碍表）
2. 选择2-3个框架，基于障碍类型
3. 交互式引导完成每个框架，要求研究人员提供领域特定内容
4. 推动结构性深度：如果类比或组合是表面层面的，深入探讨
5. 维护所有生成想法的运行列表，即使是不寻常的想法
6. 对通过探索的候选想法应用两句话测试
7. 移交给头脑风暴技能进行系统评估（发散→收敛→优化）

关键原则：

• 先生成模式，后评估模式——不要过早过滤
• 远距离类比比近处类比更有价值，但需要更多验证
• 研究人员的领域专业知识至关重要——代理提供认知脚手架，而非领域知识
• 鼓励研究人员与矛盾共处，而非快速解决

When a researcher asks for help with creative thinking or novel ideation:

1. Assess the block: What kind of thinking are they stuck in? (See Common Creative Blocks table)
2. Select 2-3 frameworks based on the block type
3. Walk through each framework interactively, asking the researcher to supply domain-specific content
4. Push for structural depth: If an analogy or combination is surface-level, probe deeper
5. Maintain a running list of all generated ideas, even unusual ones
6. Apply the two-sentence test to candidates that survive exploration
7. Hand off to the brainstorm skill for systematic evaluation (diverge → converge → refine)

Key Principles:

• Generative mode first, evaluative mode second — do not filter prematurely
• Distant analogies are more valuable than nearby ones, but require more validation
• The researcher's domain expertise is essential — the agent provides the cognitive scaffolding, not the domain knowledge
• Encourage the researcher to sit with contradictions rather than resolve them quickly