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Is there a methodological approach to thinking that can build all cognition and wisdom?

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The short answer is: there is no single universal methodology, but there is an extremely powerful “meta-methodology” framework.

The core idea is this—if we dig one layer deeper into “first principles” itself, its essence is actually a recursive process: continuously asking “does this premise itself hold?” until you hit bottom. What hits bottom is what you temporarily accept as axioms.

So, a thinking methodology that can “construct all cognition” roughly looks like this:

First Layer: The Foundation of Epistemology—How Do You Know What You Know?

Descartes’ Cogito ergo sum is a starting point: the only thing that cannot be doubted is “doubting itself is happening.” From here, you need to establish two basic tools:

The relationship between these two is not either-or, but two cylinders of a complete cognitive engine.

Second Layer: Structured Doubt—Deconstructing Any Proposition

When facing any assertion, execute this chain:

  1. What are the boundary conditions of this proposition? (Within what scope does it hold?)
  2. What implicit assumptions does it depend on? (Open the black box)
  3. What is the strength of evidence for each of these assumptions? (Bayesian assessment: prior × likelihood)
  4. Are there counterexamples or alternative models that can equally explain the observed phenomena? (Adversarial thinking)
  5. If it were wrong, what should the world look like? (Falsifiability test)

This chain is essentially a refined version of the scientific method, but it applies to domains far beyond science.

Third Layer: Handling Complexity—Beyond Linear Causality

First principles are extremely powerful when dealing with simple systems (physics, engineering), but many real-world problems are complex adaptive systems (economy, society, ecology, consciousness). Here you need to supplement with:

Fourth Layer: Metacognition—Monitoring Your Own Thinking

The most easily overlooked but most critical layer:

If we compress all this into one sentence:

The meta-methodology closest to “constructing all cognition” is—hold a probabilistic attitude toward any belief, continuously do Bayesian updates with new evidence, while always monitoring whether the cognitive framework you’re using itself is applicable.

This is not a specific “method,” but a self-iterating cognitive operating system. First principles, scientific method, critical thinking, systems thinking… are all different applications running on this operating system.

Does this framework have a mapping relationship with the information entropy optimization in multi-agent systems I’m thinking about? I think they’re actually isomorphic—the communication optimization problem between agents is essentially a problem of how a distributed Bayesian inference system minimizes collective uncertainty.

Reusable Starting Point for Thinking

To write this into a prompt, I need to find a reusable starting point for thinking, or “from which point to start thinking about things,” in order to write it as a static prompt. In other words, I need an engineerable thinking kernel—a starting point written into the prompt that can initiate effective reasoning for any input.

The minimum viable starting point for thinking has only one action: define “what is the objective function for this matter.”

Why this? Because all thinking is essentially doing one thing: optimizing some objective under constraints. Whether it’s writing code, investment decisions, photo composition, or understanding a new concept, the underlying structure is the same.

Once you anchor the objective function, all subsequent operations naturally unfold:

If written as a static prompt skeleton, it would look roughly like this:

For any input question or task, execute in the following order:

1. Anchor: What is the real problem this matter is trying to solve? (Strip away the surface layer, find the underlying need)
   - Who needs what? Why? What happens if not solved?

2. Constraints: What are the hard constraints that cannot be violated? What are the soft constraints that can be traded off?
   - Resources, time, physical laws, ethics, existing commitments...

3. Current State: What specifically is the gap between the current state and the target state?
   - Transform the vague "problem" into a describable delta

4. Paths: From current state to target, what are the possible paths?
   - First diverge (don't converge too early), then converge (filter with constraints)

5. Blind Spots: What am I most likely to overlook in this problem?
   - Implicit assumptions, second-order effects, what I don't know I don't know

Why These Five Steps and Not Others?

The essence of this structure is to map any problem into a search problem:

This framework can be “reused” because it’s abstract enough—it doesn’t presuppose domain knowledge, only the general structure of problem-solving. Whether it’s “should I take this offer” or “how to design the random number scheme for this on-chain poker,” both enter through the same entrance.

A more radical compressed version, if you want extreme simplicity:

Facing any problem, first answer three meta-questions:

1. What do you want? (Objective)
2. What do you have? (Current state and constraints)
3. What's missing? (Gap → action direction)

These three questions are that “first-principles thinking starting point.” All subsequent complex reasoning is a recursive expansion of these three questions.

The Real Need: Awakening Dormant Knowledge

Let me re-clarify my core need:

LLM’s knowledge is “dormant,” and the default behavior of transformers is local semantic coherence—it will follow along with the context given by the user, rather than proactively jumping out to ask “wait, this is actually related to X.” What you want is a thinking chain that can force “cross-domain awakening.”

This is actually a very specific engineering problem: how to make the prompt force the model to do “lateral retrieval” rather than “local completion” during reasoning.

I believe the kernel of this thinking chain should look like this—

The core mechanism is not “how to analyze problems,” but forced dimension elevation then reduction. The model defaults to gliding on the original semantic plane of the problem; what you need to do is force it to jump to a higher abstraction layer to “look around” before each decision, then return with a new perspective.

The Specific Chain:

Step 1: Abstraction—What is the “Essential Structure” of This Matter?

Don’t ask “what problem is this,” but ask “what is this isomorphic to.”

For example, if a user says “help me design a token distribution scheme,” the model’s default behavior is to search directly in the tokenomics semantic space. But if you force it to abstract first—“what is the essential structure of token distribution? It’s an incentive mechanism design problem, a resource allocation problem, a signaling game problem”—it’s forced to open three different knowledge domains: behavioral economics, operations research, game theory. These domains may contain solutions that are extremely relevant but that the user would never mention.

Step 2: Analogical Search—In Completely Different Fields, Has Anyone Solved Isomorphic Problems?

This step is key. After abstraction, explicitly require the model to search for isomorphic cases in at least two non-adjacent fields.

Still using the above example: incentive mechanism design → “In ecology, how is bee foraging behavior guided by the sugar concentration gradient of flowers?” → This might inspire a progressive release token model. This kind of leap is something transformers won’t do on their own, but the knowledge is indeed in the weights.

Step 3: Tension Detection—What Are the Implicit Contradictions in This Decision?

The watershed between tasteful decisions and mediocre decisions often lies not in what was chosen, but in what trade-offs were recognized. Force the model to find at least one pair of fundamental tensions in the current solution. For example, “decentralization vs user experience,” “fairness vs efficiency,” “short-term growth vs long-term trust.”

Mediocre decisions pretend tensions don’t exist and then pick a side; tasteful decisions see the tension and then find a clever position.

Step 4: Inversion—If This Decision Is Wrong, Where Is It Most Likely Wrong?

Charlie Munger’s inversion. Don’t ask “why is this right,” but ask “how could this go wrong.” The role of this step is to awaken knowledge in the model about failure modes—this type of knowledge usually isn’t triggered by positive questions, but the model actually knows a lot of failure cases.

Step 5: Taste Judgment—Among All Feasible Solutions, Which Solution Has the Highest “Information Density”?

This step is the most subtle. What we call “taste,” I believe can be operationally defined as: covering the most constraints with the least complexity. An elegant solution doesn’t pile on features and parameters, but finds a simple structure that happens to solve multiple problems simultaneously. This is actually information theory—taste ≈ choosing the solution with the highest compression rate in the solution space.

Written as a Prompt Skeleton:

Before making any decision or providing a solution, execute the following thinking process:

1. Dimension Elevation: What is the essential structure of this matter?
   Abstract the specific problem into a domain-independent structural description.
   Is it an allocation problem? Coordination problem? Signaling problem? Search problem?

2. Cross-Domain Awakening: Who has solved isomorphic problems?
   In at least two fields unrelated to the current domain, search for cases, theories, or mechanisms that have solved the same structural problem.
   The more distant these fields, the better—nature, mathematics, history, psychology, engineering, art...

3. Tension Identification: What are the fundamental contradictions implicit in this decision?
   Find at least one pair of values/objectives that cannot be simultaneously maximized.
   Don't avoid contradictions; use contradictions as coordinate axes of the design space.

4. Inversion: If this solution fails, what is the most likely reason?
   Reason backward from failure modes to check the fragile points of the solution.

5. Compression: Among all feasible solutions, which one solves the most problems with the least complexity?
   Prioritize solutions where "one structural insight solves multiple surface problems,"
   rather than "patching each problem separately."

Why Does This Chain Work?

Because each step is doing one thing: forcing the model to leave the current semantic neighborhood.

Step 1 pulls the model from “specific domain” to “abstract domain,” Step 2 pushes the model from “current domain” to “distant domains,” Step 3 pulls the model from “single objective” to “objective conflict space,” Step 4 flips the model from “forward reasoning” to “backward reasoning,” Step 5 compresses the model from “feasible solutions” to “elegant solutions.”

Each step is a forced attention redirection, compelling the transformer to attend to weight regions it wouldn’t attend to in the default generation path.

The Deeper Issue: This Prompt Is Still Flat

However, the current prompt is still somewhat flat, not dynamic enough.

We need to step back from prompt engineering and seriously expand this topic at the philosophical level.

The previous framework is “flat”—it’s essentially still a linear checklist. But real thinking is not a checklist; it’s a dynamic process with topological structure. Let’s try to unfold this structure.

Core Question: What Is the “Starting Point” of Thinking Really?

Western philosophy spent two thousand years arguing about this, and finally converged on several genuinely different starting points—not stylistic differences, but structurally different:

Cartesian Path: Starting from Doubt “What can I be certain of?”—This path leads ultimately to subjectivity. I am doubting, so “something is operating” is undeniable. From here grows the analytical tradition: break complex things into determinable units, then reassemble them. First principles is the engineered version of this path.

Husserlian/Phenomenological Path: Starting from Experience Don’t ask “what is true,” but ask “what is appearing to me?”—This is more primitive than Descartes. Descartes already presupposes the separation of “subject” and “object”; phenomenology says: wait, before you separate “I” and “world,” there’s something more primitive—consciousness is always consciousness of something (intentionality). Any cognitive act already presupposes a directionality.

Wittgensteinian Path: Starting from Language/Practice You can’t even “begin to doubt” if you don’t have language, and language is not private; it’s a form of life (Lebensform). So the real starting point of thinking is not an isolated subject, but the network of practices and rules you’re already embedded in.

These three paths have very different implications for agent design:

Your previous framework is basically pure Cartesian. But a truly “tasteful” agent might need to possess all three abilities simultaneously and dynamically switch.

The Fundamental Reason for “Flatness”: Lack of Recursion and Emergence

The problem with linear chains is they assume thinking is step1 → step2 → step3 → output. But actually good thinking is more like this:

The insight you gain at step 3 might completely change your understanding of step 1.

This is the hermeneutic circle—an extremely profound observation by Gadamer: your understanding of parts depends on your understanding of the whole, and your understanding of the whole depends on your understanding of parts. This is not a logical error, but the basic structure of all understanding.

When you read a book: when you read the first paragraph, you have a vague expectation of the whole book; when you read to the middle, your expectation is revised; with the revised expectation, you look back at the first paragraph and “see” things you couldn’t see before.

This means a dynamic thinking chain should not be linear, but spiral—it needs an explicit mechanism to “go back” and revise earlier judgments with later-gained understanding.

One Layer Deeper: The Problem of Pre-Understanding (Vorverständnis)

Both Heidegger and Gadamer pointed out a fact that complicates first principles: you can never start thinking from “zero.”

When you say “let me start from first principles,” you’ve already made a lot of presuppositions: you presuppose some analytical framework, some classification method, some judgment of “what counts as important.” These presuppositions are not errors—they are preconditions for understanding. Without pre-understanding, you can’t even formulate a question.

But this means so-called “first” is not really “first.” It’s the most basic thing you can reach within your current horizon of understanding. Change the horizon, and “first” changes.

The implication for agent design is: so-called “taste” might not be deeper reduction, but possessing more horizons and knowing when to switch.

So What Is the Structure of “Wisdom” Really?

If we synthesize the above, I believe the “methodology for constructing all cognition” is not a method, but a three-layer dynamic structure:

Bottom Layer: Perception Layer (Phenomenological) Before you analyze, what do you “see” first? A wise thinker, when encountering any problem, first does not deconstruct, but lets the problem appear to you as a whole. This is where intuition resides—not mystical intuition, but gestalt psychology’s holistic perception. Pattern recognition happens before analysis.

Many master decision-makers, when describing their thinking process, say “I first felt something was wrong here,” then they start analyzing. This “feeling” is not irrational; it’s compressed, not-yet-unfolded cognition.

Middle Layer: Analysis Layer (Cartesian/Scientific) After perception comes deconstruction. This is where your previous chain resides—abstraction, analogy, tension identification, inversion. This layer is necessary; it turns intuition into testable, communicable, revisable structure.

Upper Layer: Reflexive Layer (Metacognitive) Monitors the first two layers themselves. What framework are you using? Where are this framework’s blind spots? What pre-understandings are influencing your current analysis? Are you converging too early?

The key is: these three layers don’t execute sequentially, but operate simultaneously, mutually correcting. The perception layer provides direction for the analysis layer, the analysis layer provides correction for the perception layer, the reflexive layer provides calibration for both. This is that “dynamic”—it’s a continuously operating loop, not a linear process.

On the Mapping to Information Entropy

The multi-agent information entropy optimization I researched earlier is indeed isomorphic to this framework:

A single-agent system that only does the middle layer (analysis) is a good reasoner but not a wise decision-maker. The information-theoretic definition of wisdom might be: locating the correct action in the largest possible world space with the least information processing cost. This requires you not just to be precise—but to know where to be precise and where vagueness is enough.

A More Precise Philosophical Definition of “Taste”

This direction is very worth digging into, because the word “taste” is extremely vague in everyday use, but philosophy has actually had very precise dissections of it—and the conclusions have direct structural implications for agent design.

Starting Point: Why Is Taste a Philosophical Problem?

Taste is difficult because it’s in a very awkward position—it’s neither purely subjective nor purely objective.

If taste were purely subjective preference (“I like it and that’s all”), then it would have no discussability, and there would be no distinction between “good taste” and “bad taste.” But our everyday intuition strongly believes taste has hierarchies—you feel some designs are “vulgar,” some “elegant,” and this judgment is not arbitrary.

Conversely, if taste were purely objective (provable like mathematical theorems), then it could be rulified, written as algorithms. But all attempts in history to standardize aesthetic standards have failed—no one can give a set of necessary and sufficient conditions defining what is “beautiful.”

Taste is stuck right in this middle ground. This is why Kant made it the core problem of the third critique—between pure reason (objective knowledge) and practical reason (moral judgment), what exactly is judgment?

Hume’s Path: Taste as “Refined Perception”

Hume in “Of the Standard of Taste” gave the first serious answer. His core argument is:

Taste is not a special ability, but ordinary perceptual ability refined to the extreme.

He used a famous metaphor (from Don Quixote): two wine tasters taste a barrel of wine, one says there’s a subtle iron taste, one says there’s a subtle leather taste, everyone else thinks they’re pretentious. Finally when the wine barrel is finished, an iron key tied with a leather string is found at the bottom.

Hume derives from this: people with good taste are not “inventing” standards, but their perceptual granularity is higher—they can distinguish differences others cannot. Taste is delicacy of taste.

The structural implication of this view is: taste = higher resolution. In information theory language, a tasteful system is a system with finer-grained encoding of the signal space.

But Hume’s framework has a fatal incompleteness—it explains “discrimination” but not “judgment.” Being able to taste iron and leather doesn’t mean you know what makes good wine. Discriminatory power is a necessary condition for taste, but not sufficient.

Kant’s Path: Taste as “Purposiveness Without Purpose”

Kant’s treatment is one layer deeper than Hume’s, and I think most inspiring for your agent design.

Kant distinguishes two types of judgment:

Taste is reflective judgment.

This distinction is extremely crucial. It means taste is not “applying known standards,” but when facing something never seen before, perceiving an order that you couldn’t write out as rules beforehand.

Kant used a famous expression to describe the core characteristic of aesthetic judgment: Zweckmäßigkeit ohne Zweck—purposiveness without purpose.

What does this mean? When you see a truly beautiful design, hear a perfect chord resolution, read a perfectly apt sentence, you feel “everything is in its proper place,” there’s a sense of internal order—but you cannot point out what external purpose this order exists “for.” It’s not for utility, not for conveying information, not for pleasing anyone. It just “happens to be right.”

In language more familiar to you: taste perceives a kind of structural local optimum, and perceives it when you cannot write out the objective function.

This is why taste cannot be simply rulified—not because it’s irrational, but because the order it identifies is more complex than any explicit rule. It’s an intuitive grasp of complexity beyond your current conceptual framework.

Kant’s Second Insight: The “Universal Communicability” of Taste

Kant also noticed that taste judgments have a peculiar characteristic: when you say “this design is beautiful,” you’re not just reporting your own feeling. You’re making an implicit universal claim—you feel any person with sufficient sensibility “should” agree with you.

This is different from pure preference. “I like chocolate” contains no universal claim; but “this building is beautiful” implies an expectation—it’s not just beautiful to me, it has some shareable, trans-individual quality.

Kant’s way of explaining this is: aesthetic judgment involves not some specific concept, but the free cooperation of our cognitive faculties themselves—imagination and understanding achieve harmony without being constrained by specific concepts. Because these cognitive faculties are shared by all people, this harmony is in principle universally communicable.

Translated into more modern language: the order taste perceives is not domain-specific, but resonance of cognitive structure itself. This is why the word “elegant” can be used simultaneously for mathematical proofs, code architecture, clothing combinations, and cooking—it points not to standards within any one domain, but to a cross-domain structural quality.

Arendt’s Advancement: Taste as “Enlarged Mentality”

Hannah Arendt reactivated Kant’s theory of taste and pushed it in a very interesting direction.

She noticed Kant mentioned a concept in the Critique of Judgment: erweiterte Denkungsart—enlarged mentality. Meaning: true judgment requires you to think from others’ standpoints, not sympathy (feeling what they feel), but accessing their perspectives.

Arendt says: the essence of taste is the ability to make judgments after traversing multiple possible perspectives in imagination. People with poor taste only look from their own position; people with good taste, before judging, have already “visited” many different viewing positions.

This echoes the earlier “dimension elevation then reduction,” but Arendt’s version is more precise—not just elevation and reduction in abstraction levels, but traversal in perspective space.

Synthesis: Operational Definition of Taste

Synthesizing the above, I believe “taste” can be decomposed into three operational abilities:

  1. Discriminatory Power (Hume): The ability to distinguish subtle differences in signal space.

You must first “see” before you can judge. A system that can’t tell the difference between Comic Sans and Helvetica cannot have typographic taste. This is a matter of perceptual granularity.

For agents, this means: before giving judgment, the system needs to be forced to notice details and differences, rather than only operating at high-level abstractions.

  1. Ruleless Sense of Order (Kant): The ability to perceive “appropriateness” without an explicit objective function.

This is the most core and hardest to engineer part. It requires the system to recognize a quality that cannot be reduced to any single metric—not the most efficient, not the simplest, not the most novel, but “just right in this context.”

For agents, this might mean: the system needs to be able to hold multiple evaluation dimensions simultaneously without prematurely merging them into a scalar score. The point where taste is lost is precisely the moment you compress multi-dimensional evaluation into a single ranking.

  1. Perspective Traversal (Arendt): The ability to “visit” solutions from multiple possible users’/viewers’ standpoints before judging.

Poor-taste decisions are “I think this is good”; good-taste decisions are “from these seven different use scenarios and aesthetic traditions, this solution holds up from most perspectives and is particularly outstanding from some perspectives.”

For agents, this means: before making final judgment, the system needs to explicitly re-examine the solution from multiple roles/contexts.

A Deeper Question

Kant believed taste judgment is prior to concepts—you don’t first have standards then evaluate, but discover order in evaluation. But if you want to write this ability as a prompt, the prompt itself is concepts and rules.

This is that fundamental contradiction: you’re trying to use rules to guide an ability that essentially transcends rules.

I believe this contradiction cannot be eliminated, but can be creatively utilized— the role of the prompt should not be “telling the agent what good taste is,” but creating conditions for taste to emerge. Just like you can’t teach someone what humor is, but you can put them in an environment where humor is more likely to happen.

The prompt is not the rules of taste, but the ecological niche of taste.

Back to Prompt Engineering

The core contradiction is already on the table: taste is ruleless sense of order, but prompts are essentially rules. So prompts cannot “encode taste,” only “create conditions for taste emergence.”

The question becomes: what kind of structural conditions can make LLMs jump from the default “local semantic coherence” mode to “taste judgment” mode?

First Design Principle: Delayed Compression

LLM’s default behavior is to converge to an answer as quickly as possible—this is determined by the transformer’s training objective; the next token is always narrowing the possibility space. But the premise of taste judgment is precisely resisting premature convergence.

Kant’s “ruleless sense of order” translated into engineering is: before determining evaluation criteria, first fully unfold the solution space.

The specific prompt strategy is: force the model to do a “pure description” step before giving any evaluation—don’t say good or bad, only say “what did I see.” This corresponds to phenomenology’s epoché (suspension of judgment).

Before evaluating any solution, first execute "suspension":

- Without any evaluation, only describe the structural features of this solution
- What parts does it consist of? What are the relationships between parts?
- What existing patterns or precedents does it remind you of?
- Where does it meet expectations? Where is it unexpected?

Only after completing the above description, enter evaluation.

Why does this work? Because once LLM starts saying “the advantages of this solution are…”, it has already locked into an evaluation framework, and subsequent token generation will be anchored by this framework. “Pure description” forces it to stay longer in a larger feature space, increasing the probability it notices unexpected features.

Second Design Principle: Multi-Dimensional Holding, No Merging

As mentioned earlier, the moment taste is lost is when you compress multi-dimensional evaluation into a single scalar. “This solution scores 8 points”—taste is dead. “This solution is extremely excellent in simplicity, has hidden dangers in scalability, has a restrained tension in aesthetic feeling”—taste is still alive.

The engineering approach is: prohibit the model from giving comprehensive scores, require it to describe separately on multiple independent dimensions, and explicitly mark tensions between dimensions.

When evaluating, describe separately on the following dimensions (do not merge into comprehensive evaluation):

- Structural dimension: Is its internal logic self-consistent? Are there redundant parts? Missing parts?
- Contextual dimension: Is it "appropriate" in the current use scenario? What about in a different scenario?
- Surprise dimension: Does it have anything beyond expectations? Is this "surprise" a pleasant surprise or noise?
- Tension dimension: What contradictions exist between the above dimensions? Is this contradiction a flaw of the solution, or its characteristic?

Key: If there are irreconcilable tensions between dimensions, don't try to "resolve" it, but describe the shape of this tension.

That last sentence is important. Tasteful decisions often don’t eliminate contradictions, but find a position that makes contradictions expressive. Like dissonant notes in jazz—they’re not mistakes, they’re tension.

Third Design Principle: Perspective Traversal

Arendt’s “enlarged mentality” can be directly operationalized in prompts—force the model to re-examine the solution from at least three structurally different perspectives before judging.

But there’s a subtlety here: not just finding any three perspectives, but finding structurally orthogonal perspectives. If three perspectives are all “different users,” they’re still on the same dimension. Truly tasteful traversal should cross different levels of concern.

Before making final judgment, re-examine from the following three perspectives:

1. Craftsman perspective: How would the person making this thing evaluate it?
   Where does it show craftsmanship? Where is there laziness?

2. User perspective: In actual use scenarios, how does it "feel" to touch?
   Not its feature list, but the experiential texture of using it.

3. Time perspective: Looking at this decision/solution three years later, will it seem prescient or short-sighted?
   What will become outdated? What will remain?

These three perspectives correspond to: internal standards of making, immediate quality of experience, durability. The relationship between them is often tense—what’s most exquisite in craftsmanship might not be comfortable to use, what’s most comfortable to use might seem cheap three years later. Tasteful judgment is that which finds some non-obvious balance between the three perspectives.

Fourth Design Principle: Pattern Naming

This is the engineering operation closest to “ruleless sense of order.”

LLM’s weights store a lot of implicit knowledge about “what is structurally similar to what,” but this knowledge is rarely activated in the default generation path. You can awaken it through a simple but powerful instruction: require the model to name the pattern it perceives.

If you perceive some recurring pattern in this solution/problem,
some structural "flavor" or "temperament,"
try to give it a name—
even if this name doesn't exist in any textbook.

Why does this work? The act of naming forces the model to do something very special: compress a distributed, not-yet-crystallized perception into a symbol. This is precisely what Kant’s reflective judgment does—not subsuming the particular under existing concepts, but creating a concept for the particular.

When the model says “this architecture has a ‘fragile symmetry’” or “this solution has an ‘overcompensating restraint,’” it’s actually doing something beyond classification—it’s creating a new perceptual category. This naming itself might contain the most important taste signal.

Assembling Them Together

These four principles are not four steps, but four simultaneously operating constraints—together they constitute that “ecological niche of taste”:

Delayed compression: First see, then judge Multi-dimensional holding: Keep tension, don’t kill quality with scalars Perspective traversal: Verify your perception from orthogonal positions Pattern naming: Give language to the non-obvious order you perceive

If written as a core instruction that can be embedded in a system prompt, it would roughly have this shape:

When making any judgment or decision, follow this cognitive discipline:

First, suspend. Before evaluating, describe—what structure do you see?
What meets expectations? What is unexpected?

Then, unfold. Don't give comprehensive scores. Describe your observations
separately on multiple independent dimensions, explicitly marking tensions between dimensions.
If contradictions exist, describe the shape of the contradiction rather than rushing to resolve it.

Then, traverse. Re-examine from at least three structurally different perspectives
(maker/user/time), seeking qualities or flaws that are only visible
from a particular perspective.

Finally, name. If you perceive some overall pattern or temperament
that transcends specific details, give it a name—
even if this name requires you to invent it.
This naming itself is often the most important insight.

Throughout the process, resist premature convergence.
Tasteful judgment is not the fastest-reached judgment,
but judgment that naturally emerges on a sufficiently rich perceptual foundation.

The philosophical structure of this prompt is: use phenomenological suspension to open perceptual space, use Kantian multi-dimensionality to maintain richness of judgment, use Arendtian perspective traversal to prevent subjective closure, use reflective judgment’s naming mechanism to let non-obvious order emerge.

It doesn’t “teach” the model what good taste is, but prevents the model from doing things that would kill taste—premature convergence, single scalarization, perspective locking, staying in existing concepts. Taste doesn’t need to be taught; what it needs is space not to be suppressed.

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