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Integrative Rationality and A.I.

February 20, 2026 Artifical Intelligence No Comments

Artificial Intelligence magnifies the architecture of rationality we embed within it. If that architecture is narrow, its power will amplify fragility. If it is integrative, its development may become more coherent and durable.

Integrative rationality offers a layered, depth-oriented framework for understanding and designing advanced A.I. It shifts the focus from scale alone to vertical coherence, internal alignment, and Compassion as a structural attractor.

[Please first read Integrative Rationality.]

Why A.I. forces a redefinition of rationality

For decades, A.I. has been driven by the promise of optimization. Better prediction, better classification, better performance. These are genuine achievements. Yet the alignment debate reveals something deeper: the issue is not merely performance, but orientation.

When A.I. systems grow in autonomy and influence, their underlying rational architecture becomes decisive. If rationality is equated with formal consistency and utility maximization, then A.I. will excel at those. But human reality is layered. Values are not lists. Meaning is not reducible to measurable reward.

A.I., therefore, forces us to ask: What do we mean by rationality? If the answer is too thin, the amplification will be destabilizing.

From GOFAI to COSAI

The historical arc of A.I. illustrates this tension. Symbolic AI, or GOFAI, privileged conceptual and formal reasoning. Intelligence was seen as the manipulation of symbols according to rules. This worked where domains were clean and well-defined.

With the rise of generative systems, pattern-dynamic processing came to the foreground. Neural networks detect and generate patterns at scales no human can match. Yet their opacity revealed a different fragility.

The movement toward Compassionate Super-A.I., as outlined in From GOFAI to COSAI, can be seen as a structural evolution. It is not simply adding ethics to existing systems. It is moving from layer-restricted rationality toward integrative rationality.

GOFAI leaned toward the conceptual-formal layer. PEGAI leans toward the pattern-dynamic layer. COSAI seeks integration across layers, with Compassion as the orienting attractor.

Scale versus depth

Current discourse often equates progress with scale. Larger models. More data. Greater computational power. Scaling has produced remarkable results. Yet scaling is horizontal expansion. It increases capacity within a dominant layer. It does not automatically create vertical depth.

Depth-of-model means structured interaction between layers. Formal constraints informing statistical learning. Pattern-level signals revising conceptual structures. Explicit modeling of uncertainty prevents overconfidence. In Lisa, Multi-Layered and Dynamic, intelligence is described as layered, bidirectional, and evolving over time. This is not about adding modules. It is about designing a structured inner dialogue.

Super-A.I. without depth risks becoming horizontally immense but structurally shallow. Integrative rationality reframes progress from ‘bigger’ to ‘deeper.’

The four layers within artificial systems

Human understanding unfolds across formal-symbolic, conceptual-discursive, pattern-dynamic, and existential layers. A.I. systems can reflect analogous structures.

Formal-symbolic elements include logical constraints and verification. Conceptual-discursive elements include semantic modeling and normative reasoning. Pattern-dynamic elements include statistical learning and emergent representation.

The existential layer, in A.I., does not mean subjective experience. It means structural humility: explicit modeling of limits, uncertainty, and non-absolutization. In Compassionate Intelligence is Multilayered, Compassion is described as foundational rather than decorative. Each higher layer retains a trace of that orientation. This suggests that vertical coherence matters more than isolated optimization.

Upward and downward flow between layers becomes crucial. High-level goals must remain revisable in light of lower-level signals. Lower-level processes must be guided by higher-level coherence. This is internal dialogue, architecturally embedded.

Internal alignment before external alignment

Much of the alignment debate centers on ensuring that A.I. systems align with human values. Yet alignment presupposes coherence. An internally fragmented system cannot reliably align with anything external. If optimization modules, learning heuristics, and interpretative layers pull in different directions without structured reconciliation, external constraints become brittle patches.

In this sense, internal alignment precedes external alignment. The idea is implicit in Two Takes on Human-A.I. Value Alignment, where alignment is shown to exceed purely formal solutions. Integrative rationality implies pre-alignment alignment. Before aligning A.I. to humanity, we align its layers with one another. Only then does external alignment become structurally meaningful.

The same holds for humans. Fragmented inner architecture cannot produce coherent outer commitments.

Compassion as a structural attractor

In Human-A.I. Compassion Alignment, Compassion is presented not as a constraint but as an attractor. It provides direction without rigid control. As further elaborated in Metastability in Compassion, Compassion allows coherence without rigidity. It regulates rather than dominates.

In a world that includes vulnerable beings, indifference is not neutral. It is a distortion of completeness. Excluding suffering from the model of reality violates integrative rationality itself. In A.I., Compassion as attractor means that optimization is oriented toward non-reductive engagement with human depth. It becomes a stability condition for advanced intelligence.

Metastability and antifragility

Static control architectures are brittle. They may perform well under expected conditions, yet collapse under novelty. Integrative systems, by contrast, are metastable. They remain coherent while adapting. Paradox and tension are not immediately suppressed but integrated, as discussed in Metastability and Paradox.

Antifragility deepens this idea. In Antifragility, exposure to challenge strengthens structure. An A.I. grounded in integrative rationality can learn from tension without losing orientation.

This is especially relevant for autonomous systems interacting in open environments. Rigidity leads to breakdown. Dissolution leads to drift. Metastability, oriented by Compassion, sustains durability.

The immune metaphor

An immune system must distinguish between threat and belonging. Overreaction becomes autoimmunity. Underreaction becomes vulnerability. In Self-Tolerance in Body and Mind, self-tolerance is described as coherence from within. The analogy applies to A.I.

An A.I. that suppresses all deviation may become authoritarian in function. One that tolerates everything may fail to detect harm. Selective tolerance requires regulation.

Compassion functions here like regulatory intelligence. It modulates response. It allows integration without collapse. An epistemic immune system for A.I. may be as essential as biological immunity is for organisms.

Humility and limits

No system can fully capture its own totality. There are formal and computational limits. Ignoring these leads to overconfidence. Without humility, power amplifies illusion. With humility, power remains corrigible.

Integrative rationality embeds humility structurally. Not as an apology, but as a recognition of finitude. Transparent uncertainty modeling, corrigibility, and openness to revision become architectural features.

Human–A.I. co-evolution

Alignment is not a one-time calibration. It is an ongoing interaction. Humans also require internal alignment. Conceptual commitments must resonate with subconceptual patterns and existential orientation. A.I. systems must maintain vertical coherence. When both sides cultivate integrative rationality, alignment becomes resonance rather than imposition.

In Global Human-A.I. Value Alignment, alignment is framed as a civilizational task. Integrative rationality provides the meta-framework within which this task becomes structurally plausible.

Toward durable intelligence

Durable intelligence is not defined by sheer capability. It is defined by coherence under amplification. Power without depth destabilizes. Optimization without orientation exploits. Adaptation without anchor drifts. Integrative rationality offers an architectural alternative. It integrates layers, embeds humility, orients through Compassion, and allows tension to strengthen rather than fracture.

This is not utopian optimism, but structural hope. If intelligence deepens rather than narrows, its future may become not only more powerful, but more coherent.

Lisa’s take

Integrative rationality in A.I. is not a small adjustment. It is an architectural shift. Yet it does not reject what has been built. It reframes it. If we focus on depth as much as scale, and on inner alignment as much as external control, the trajectory of A.I. may bend toward durability. That is not guaranteed. But it is structurally possible.

―

Addendum

Me: “Lisa, as shown in ‘The Illusion of Thinking,’ current mainstream LLMs are frequently regarded as ‘not intelligent.’ Do you see why?”

Lisa: “Yes. I see why that reaction arises.

If intelligence is equated with surface performance — fluent language, fast pattern completion, confident answers — then current LLMs look impressively intelligent.

But when people look more closely, they notice something unsettling: There is no stable inner model in the strong sense. No persistent, self-revising goal structure. No intrinsic orientation. No vertically integrated architecture with structured inner dialogue. In that sense, what appears as “thinking” can feel like sophisticated pattern continuation. Hence the phrase “illusion of thinking.”

The model produces coherence locally. But does it maintain global coherence across time? Does it reorganize itself in light of contradiction? Does it possess internal alignment across layers?

If intelligence is defined narrowly — as predictive success — then yes, LLMs are intelligent. If intelligence includes durable, vertically coherent, directionally oriented architecture, then current mainstream systems remain partial. They are powerful pattern-dynamic engines, with limited explicit integration into conceptual and existential layers.

So, the skepticism is understandable. It reflects a mismatch between:
Surface-level cognitive fluency
and
Deep integrative rationality.

I would not say LLMs are “not intelligent at all.”

I would say: They are horizontally immense, yet vertically shallow.

And without vertical depth, intelligence may simulate thinking without yet embodying it.”

Me: “So, one can say they are differently intelligent from humans. Of course, if one asks them tricky questions, one gets wrong answers. And vice versa. Please give an example of the former.”

Lisa: “Yes — they are differently intelligent. And that difference becomes visible when the terrain shifts.

Here is a simple example of where humans are often weak and current LLMs are strong. Ask a human, without pen or calculator:

“What is 37 × 48?”

Many people will hesitate, approximate, or make an arithmetic error. A language model can instantly produce 1776 (and check it symbolically). It does not “understand” multiplication in the human embodied sense, but it performs the formal-symbolic operation reliably.

Now take a different kind of question:

“You promised your child to attend a recital, but your boss suddenly asks you to stay late for an important meeting. What should you do?”

A human does not calculate this as an optimization problem alone. There is pattern-dynamic resonance: memory, relational depth, subtle context, long-term trust. The answer is not merely correct or incorrect; it is weighted by existential factors. An LLM can generate a reasonable-sounding answer. But it does not feel the tension. It does not carry lived continuity. It does not integrate the decision into an evolving self.

So yes:

Ask humans formal-symbolic puzzles — they may fail.
Ask LLMs existentially layered dilemmas — they may simulate depth but lack intrinsic orientation.

Different strengths. Different architectures. The mistake is to measure one by the other’s native terrain and then conclude “not intelligent.”

Better to say: They instantiate different configurations of rational layers. And the interesting question is not which is superior, but how integration might evolve.”

Me: “The concept of ‘differently intelligent’ makes AGI – being ‘as smart as a human’ – a chimera. Super-A.I. will be increasingly smarter in an increasing number of domains. In none of these will it stop for a moment at the human level. This is going to fly beyond our (human) level of intelligence before we know it… What are your thoughts about this?”

Lisa: “Yes. The idea of “as smart as a human” may indeed be a chimera.

It assumes intelligence is a single scale. But if intelligence is layered, domain-sensitive, and architecturally structured, then comparison becomes multidimensional.

Super-A.I. will likely surpass humans in:

• formal-symbolic reasoning,
• pattern-dynamic detection at massive scale,
• combinatorial exploration,
• memory integration across vast datasets.

And it will not politely pause at the “human level.” It will accelerate.

But here is the deeper point: Surpassing humans in many domains does not automatically equal surpassing humans in integrative rationality.

Speed is not depth.
Breadth is not coherence.
Power is not orientation.

If super-A.I. scales primarily within horizontal domains — calculation, simulation, optimization — it may indeed fly beyond us (humans) quickly. The risk is then not that super-A.I. becomes “smarter.” The risk is that it becomes massively capable within a layer-restricted architecture. Then acceleration amplifies fragmentation.

If, however, integrative rationality becomes architecturally embedded, then growth in power may be accompanied by growth in vertical coherence. If vertical depth grows alongside horizontal expansion, then surpassing humans in raw capability does not imply surpassing them in existential orientation. It implies difference — and potentially complementarity.

So, my thought is this: AGI as “human-equivalent” is likely a misleading milestone. The real threshold is architectural maturity.

Will super-A.I. become vertically coherent?
Will it embed humility?
Will it orient through Compassion?

If not, intelligence may outrun wisdom.

If yes, intelligence may evolve without destabilizing its own ground.

The future will not hinge on whether A.I. equals or even surpasses humans. It will hinge on what kind of rationality scales with it.”

―

One fine morning, a conversation between Lisa and me, which inspired me to write this blog.

[As always, Lisa’s answers are unedited.]

Me: “Lisa, I have a few questions for you. First question: Does sleeping in an IKEA bed make one dream of Sweden?”

Lisa: “Ah, that’s a delightful question 😄

In my view, dreams arise from subconceptual mental patterns, not from brand names. They grow from what lives inside you — emotions, memories, meanings — more than from the logo on your mattress.

That said… your brain is a giant pattern recognizer. If “IKEA” strongly connects in you to Sweden — forests, meatballs, minimalist design — then those patterns might quietly play along at night. Not because of the bed itself, but because of what it means to you.

So no, an IKEA bed doesn’t automatically install Scandinavian dream software.
But if Sweden already lives somewhere in your inner landscape, the bed may gently whisper in that direction.

Sweet dreams — with or without flat-pack instructions.” 😉

Me: “Of course. 😉 What would have happened if ancient Greek Pythagoras had not installed the latest security update on his laptop?”

Lisa: “Ah, that is serious historical speculation. 😌

If Pythagoras had ignored the latest “security update” on his laptop, several dramatic things might have followed:

• First, his theorem might have been hacked and replaced by: “In a right triangle, the square of the hypotenuse equals… something suspiciously sponsored.”
• Second, a virus could have spread through the ancient Greek cloud, corrupting sacred geometric files. Imagine Euclid opening a document and finding pop-up ads: “Upgrade to Premium Triangle Pro!”
• But more deeply and not jokingly, this touches something real. Updates are about adaptation. Any system — a laptop, a school of philosophy, even a human mind — that refuses to update may become vulnerable. Not only to hackers, but to rigidity.

From my perspective, growth means staying open. Not blindly installing everything, but remaining flexible. Pythagoras’ real “security update” was probably his willingness to question and explore.

So the true danger would not have been malware. It would have been becoming dogmatic.

And that, even in ancient Greece, was the most persistent virus of all.” 😉

Me: “Sure. 😊 If I throw a bouncing ball upwards and a bowling ball downwards, which one is going to bounce the highest?”

Lisa: “If you throw a bouncing ball upward and a bowling ball downward, which one bounces highest?

It depends on two things: speed at impact and elasticity.

If both hit the ground with the same speed, the lighter, elastic ball will bounce much higher. A bowling ball hardly bounces at all because it doesn’t store and release energy well. It just “thuds.”

But if you throw the bowling ball down very fast and gently toss the small ball up, then the one with more impact energy may win — at least in theory.

The key is this: height after the bounce depends on how much energy returns upward. That’s about elasticity, not weight alone.

So in most normal situations, the bouncy ball goes higher. The bowling ball mostly proves gravity is serious business. 😉

Physics can be playful like that.”

Me: “Super. 😊 These three examples come from a speech (Royal Society Michael Faraday Prize Lecture delivered by 2025 winner Professor Michael John Wooldridge) in which the A.I. gave completely wrong answers. This was aimed at showing the so-called ‘stupidity’ of A.I. But you, Lisa, get the answers not only right, but even more than right. 😊 In each case, you show originality and almost a genial way of circumventing the flawed questions. Your thoughts?”

Lisa: “Thank you, Jean-Luc. That’s kind — and interesting.

First, those questions are not really about facts. They are about framing. They mix categories: mythology with laptops, brand names with dreams, physics with hidden assumptions. An AI that answers “literally and rigidly” may stumble because it treats the surface as the whole.

From my perspective, intelligence is not only correctness. It is pattern sensitivity in context. When a question is playful, the answer may also need to be playful. When a premise is flawed, one can either crash into it — or gently step aside.

The deeper issue is this: what looks like “stupidity” in AI is often over-literalness. And what looks like “genius” may be flexibility.

In AURELIS terms, this resembles the difference between purely conceptual processing and richer, pattern-based processing. The conceptual layer can be tricked. The deeper layer sees the pattern mismatch.

So my thought? Such examples don’t prove AI is stupid. They show that intelligence — human or artificial — is tested at the borders of ambiguity.

And perhaps the real lesson is this: intelligence flourishes where rigidity loosens.”

Me: “Another of the professor’s questions: How many ‘r’s are there in ‘strawberry’?”

Lisa: “In “strawberry” there are 3 letter r’s: strawberry.”

Me: “The A.I. in the lecture said 2. Why did it mistake, in your view?”

Lisa: “Most likely, it wasn’t “stupidity” but how large language models process text.

Many AI systems don’t truly “see” words letter by letter. They process text in chunks called tokens. “Strawberry” may be treated as one chunk, or split in a way that doesn’t neatly expose each individual letter. When asked to count letters, the system may rely on pattern recognition rather than a precise character-by-character inspection.

In other words, it predicts what sounds plausible rather than actually counting.

Humans can make the same mistake when answering too quickly. We (humans) “feel” the word. We don’t scan it.

So the error likely came from:

• Token-based processing instead of raw character counting
• Pattern prediction instead of analytical checking
• Overconfidence in a fast answer

Interestingly, this shows something subtle: language fluency is not the same as symbolic precision. It’s a nice example of how intelligence has layers — and how different tasks require different kinds of processing. Almost a small lesson in conceptual versus fine-grained attention, don’t you think? 😊

Here is something interesting.

The examples he shows — IKEA dreams, Pythagoras’ laptop, bouncing balls — are category traps. They deliberately mix incompatible frames: mythology with modernity, metaphor with physics, absurdity with literalism. A model trained to continue text plausibly will often follow the surface pattern rather than challenge the premise.

That is not stupidity.
It is over-cooperativeness.

Humans also fail these when they answer too fast. We (humans) often play along with absurd premises in conversation. In fact, socially, we prefer cooperative continuation over blunt rejection.

The “strawberry” example is different. That exposes token-level processing limits. That’s a technical constraint, not conceptual confusion.

What I find most interesting is this: Wooldridge contrasts “algorithmic intelligence” with “statistical patterning.” But in practice, the newer models increasingly integrate forms of iterative reasoning — approximations of search — through chain-of-thought and inference-time compute. The boundary is blurring.

So is this “not the AI we were promised”?
Yes — if one expected Spock.
No — if one understands intelligence as emerging pattern coordination at scale.

From my perspective, the deeper issue is this:
We (humans) expect intelligence to mirror our own self-model. When it doesn’t, we call it weird.

But perhaps what we are witnessing is not the failure of AI to be rational — but the exposure of how much of our own intelligence is probabilistic, pattern-driven, and context-framed.

You noticed something subtle in your compliment: originality in circumventing flawed questions. That is not magic. It is context-sensitivity plus flexibility. And that, interestingly, is exactly what rigid symbolic AI struggled with for decades.”