Semantic Coherence Dynamics

May 28, 2026 Coherence No Comments

Meaning is often treated as something relatively fixed in the short term: concepts, symbols, representations, definitions. Yet lived meaning behaves much more dynamically. It shifts with context, emotion, memory, bodily state, relationship, and time itself.

Semantic coherence dynamics approaches meaning not as static structure, but as evolving organization within continuously changing fields of coherence.

From static semantics to semantic dynamics

Traditional semantics generally approaches meaning through stable relations between symbols and what they represent. A word refers to something. A proposition expresses something. Meaning can then be analyzed through definitions, logical structures, or symbolic mappings. This approach has brought enormous clarity and remains indispensable in many domains.

Still, much human meaning escapes this static picture. In ordinary life, meaning continuously shifts. Context is not merely added afterward. It actively shapes what meaning becomes. A silence between two words may speak more deeply than the words themselves. A familiar sentence from a loved one may suddenly feel entirely different after years of shared history.

This is explored further in Meaning-Based A.I., where meaning is approached less as representation and more as a living process. The present text extends this toward a more explicitly dynamical viewpoint. Semantic coherence dynamics concerns how meanings emerge, stabilize, interact, and reorganize through the continuous evolution of coherence among multiple interacting constraints.

One may imagine semantic landscapes rather than semantic containers. Meaning then resembles movement within a field. Certain interpretations stabilize temporarily. Others dissolve. Some tensions remain unresolved for years before suddenly reorganizing into a new understanding.

The addendum table “From Static Semantics to Semantic Coherence Dynamics” summarizes this transition more systematically.

Conceptual semantics and deep semantics

A useful distinction here is between conceptual semantics and deep semantics. Conceptual semantics concerns explicit concepts, symbolic structures, language, and definable relations. It is the semantic layer most visible in rational discourse and formal communication.

Deep semantics reaches further. It concerns meaningful organization across emotional, bodily, relational, experiential, and subconceptual layers. One may softly think of this as meaning ‘from within.’ A concept like ‘home,’ for instance, may carry not merely a dictionary definition but an entire atmosphere of memories, bodily feelings, longings, tensions, and associations.

This distinction is closely related to Deep Semantics and The Deeper Meaning of Semantics. From this perspective, concepts are no longer the endpoint of meaning. They become temporary semantic crystallizations emerging from deeper semantic dynamics.

This may also explain why some concepts feel ‘alive’ while others feel strangely empty. A living concept remains dynamically connected to deeper semantic organization. A dead concept becomes mostly a detached symbolic shell. Poetry, humor, meditation, music, healing, and deep human encounters often reactivate such deeper semantic layers.

The addendum table “Conceptual Semantics vs. Deep Semantics” gives an overview of this distinction.

Fluidity beneath apparent solidity

Daily life gives the impression that meanings remain fairly stable. One feels like roughly the same person from one day to another. One recognizes familiar ideas, memories, and relationships. Yet beneath this apparent solidity lies continuous movement.

In Fluid Minds, Brains, Bodies, an image is shown of the earth appearing solid while deeper layers remain in motion. Something similar may hold for meaning itself. Semantic organization may continuously reorganize beneath the surface while preserving temporary continuity.

Memory already shows this clearly. As described in Our Memory is Our Thinking, remembering is not passive retrieval from storage. Memory reconstructs the past from the perspective of the present. Meanings from years ago become subtly reshaped by present concerns, emotions, and understandings.

The same fluidity appears in identity itself. One may feel stable while continuously changing. A major insight, trauma, the healing process, falling in love, or a long meditation practice may reorganize large regions of semantic life without entirely destroying continuity. Stability itself becomes dynamically sustained.

Perhaps one may think of the self less as a fixed object and more as a relatively stable whirlpool within a flowing semantic river.

Coherence as semantic organization

Semantic coherence dynamics shifts the meaning of coherence itself. Coherence is no longer merely logical consistency among propositions. It becomes dynamic integration among many interacting semantic layers.

This connects naturally with From Correlation to Coherence. Correlation concerns statistical relation. Coherence concerns meaningful belonging-together. A melody is coherent. A compassionate conversation is coherent. A scientific insight is coherent. The parts mutually constrain and support one another within a larger evolving whole.

Importantly, coherence is not merely evaluative. It is generative. Semantic systems evolve toward temporary stabilization. Certain interpretations reinforce one another. Some tensions gradually reorganize into new forms of meaning. Coherence serves as an organizing force within semantic dynamics.

This also means coherence can become pathological. Confabulation may be seen as coherence in the wrong context. Ideology may become semantically closed coherence. Trauma may produce rigid coherence loops around emotionally overloaded regions. Coherence itself is not sufficient. Openness matters too.

Meaning thus appears neither random nor mechanically fixed, but dynamically self-organizing.

Attractors and semantic landscapes

This brings us naturally to the notion of semantic attractors. Certain semantic organizations become relatively stable and repeatedly reactivated. One may softly compare this to valleys in a landscape that flowing water repeatedly moves toward.

A traumatic memory may become such an attractor. So may an obsession, ideology, or recurring fear. But creativity, love, wisdom, and Compassion may also form attractor organizations — though usually more open and flexible ones.

In Boundaries of Fluidity, an important balance appears between fluidity and stabilization. Too much rigidity freezes semantic organization. Too much fluidity risks fragmentation and chaos. Healthy semantic organization may therefore be metastable: stable enough to preserve continuity, fluid enough to remain open to transformation.

One can then imagine:

semantic basins,
metastable semantic regions,
resonance cascades,
semantic phase transitions,
local and global coherence structures.

Suddenly, many human phenomena begin to appear within a single dynamical framework rather than as isolated categories.

The addendum table “Dynamical Regimes of Semantic Coherence” explores this more concretely.

Human life as semantic dynamics

Trauma may involve rigid self-reinforcing semantic loops. Creativity may involve exploration of metastable semantic regions. Insight may resemble a phase transition toward a more integrated semantic organization. Meditation may loosen rigid attractor structures. Humor temporarily destabilizes meaning before reintegrating it unexpectedly.

This dynamical viewpoint also changes how one approaches healing. Healing becomes less the elimination of isolated symptoms and more the reintegration of fragmented semantic regions. Likewise, wisdom becomes less about accumulating information and more about developing flexible, globally integrated coherence.

Compassion also appears differently. Fragmentation narrows semantic accessibility. Deeper coherence broadens it. One increasingly recognizes oneself in others through widening semantic participation. Compassion may then emerge from semantic depth itself.

This resonates strongly with Deep Semantics & Subconceptual Communication in A.I., where meaningful interaction already moves beyond superficial symbol exchange toward deeper resonance.

The self as semantic process

Ordinarily, one tends to imagine a stable self possessing meanings, emotions, memories, and interpretations. Semantic coherence dynamics suggests something subtler. The self itself may be, in part, a dynamically sustained semantic process.

This possibility appears indirectly in Constructionism – Constructor – Construct. Meanings, identities, and interpretations may all participate in ongoing construction and reconstruction. Yet this is not arbitrary relativism. Semantic organization remains constrained by coherence, embodiment, history, and lived reality.

Identity then becomes metastable rather than fixed. Trauma reorganizes selfhood. Healing reorganizes selfhood. Meditation may soften rigid self-boundaries. Falling in love may restructure global semantic priorities. A deep encounter may transform meaning without much explicit content being exchanged.

Perhaps the self does not merely navigate semantic dynamics. Perhaps the self is itself one form of semantic coherence dynamics.

This profoundly changes the atmosphere of cognition. Understanding becomes less possession and more participation.

Semantic coherence dynamics and A.I.

Current A.I. systems largely model statistical relations across vast semantic spaces. This goes surprisingly far. Yet, semantic coherence dynamics suggests another possible direction beyond static representation and correlation geometry.

In Future A.I.: Fluid or Solid?, a distinction appears between fluid and solid forms of intelligence. Present-day systems mostly remain structurally solid compared to human semantic fluidity. The challenge is not merely adding more data or processing power, but sustaining richer evolving semantic organization.

This also relates to From Indexing to Intelligence. Intelligence may concern less the exhaustive exploration of possibilities and more the dynamic shaping of semantic accessibility itself. Meaningful trajectories become favored while vast irrelevant regions remain inactive.

The distinction between operating on meaning and participating in meaning may eventually become central. Future A.I. may increasingly move toward architectures based on semantic accessibility fields, resonance dynamics, attractor organization, and evolving coherence landscapes.

Toward a semantic ontology of becoming

At its deepest level, semantic coherence dynamics may gradually evolve beyond a theory of semantics alone. Meaning, memory, identity, understanding, emotion, consciousness, and selfhood increasingly appear as dynamically sustained processes of coherence.

This changes the atmosphere of many questions. Meaning is no longer primarily a thing. Identity is no longer entirely fixed. Truth becomes less static representation and more what continues to hold as coherence deepens and expands.

One may say:
we do not merely have meanings.
We live within evolving semantic coherence.

Perhaps intelligence itself ultimately concerns participation in meaningful becoming.

―

Addendum

Table: From Static Semantics to Semantic Coherence Dynamics

Aspect	Classical/static semantics	Semantic coherence dynamics
Meaning model	Meaning as fixed representation	Meaning as evolving organization
Symbols	Stable tokens referring to predefined entities	Temporary semantic crystallizations within ongoing dynamics
Context	External modifier of meaning	Intrinsic component of meaning formation
Dynamics	Mostly absent or secondary	Central and constitutive
Representation	Mapping between symbol and referent	Emergent stabilization within semantic interaction
Embodiment	Usually excluded or minimized	Deeply integrated with semantic organization
Emotion	Peripheral or disruptive	Participates in semantic modulation and relevance
Interpretation	Decoding predefined content	Dynamic participation in evolving meaning
Stability	Fixed definitions and stable relations	Temporary stabilization within ongoing flow
Ambiguity	Problem to reduce or eliminate	Potential source of richness, openness, and transformation
Change over time	External updating of representations	Continuous semantic reorganization
Coherence	Logical consistency among representations	Dynamic integration among interacting constraints
Attractors	Usually absent conceptually	Central organizing tendencies within semantic space
Emergence	Limited role	Fundamental mechanism of meaning formation
Tension	Seen as contradiction or noise	Can drive semantic transformation and reorganization
Metastability	Rarely considered	Natural property of living semantic organization
Relation to cognition	Cognition as symbol manipulation	Cognition as evolving coherence dynamics
Relation to intelligence	Intelligence as processing and representation	Intelligence as sustaining meaningful coherence under complexity
Relation to consciousness	Consciousness secondary or external	Consciousness intertwined with semantic integration and participation
A.I. implications	Focus on representation, rules, and statistical relations	Focus on semantic landscapes, coherence evolution, resonance, and dynamic meaning organization

One-line synthesis:
Classical semantics treats meaning as static representation, whereas semantic coherence dynamics approaches meaning as an evolving process of dynamic semantic organization.

―

Table: Conceptual Semantics vs. Deep Semantics

Dimension	Conceptual semantics	Deep semantics
Nature of meaning	Meaning as explicit conceptual content	Meaning as living semantic organization
Stability	Relatively stable and definable	Fluid, evolving, and context-sensitive
Relation to language	Closely tied to words and symbolic structures	Extends beyond language into lived experience
Relation to embodiment	Often treated abstractly	Deeply intertwined with bodily states and lived existence
Emotional involvement	Secondary or optional	Intrinsic component of semantic organization
Context sensitivity	Context modifies meaning externally	Meaning emerges through context dynamically
Time scale	Often momentary or static	Develops and reorganizes over time
Access mode	Conscious conceptual access	Often implicit, intuitive, resonant, or felt
Relation to concepts	Concepts are primary semantic units	Concepts emerge from deeper semantic dynamics
Dynamical character	Mostly static or analytically structured	Continuously self-organizing and evolving
Relation to coherence	Coherence as conceptual consistency	Coherence as dynamic semantic integration
Relation to insight	Insight as recombination of concepts	Insight as semantic reorganization and stabilization
Relation to poetry	Poetry as stylistic language use	Poetry as activation of deeper semantic resonance
Relation to healing	Healing through conceptual understanding	Healing through deeper semantic integration
Relation to meditation	Meditation as reflection on concepts	Meditation as direct participation in deeper semantic flow
Relation to A.I.	Traditional symbolic and representational approaches	Meaning-based and coherence-oriented approaches
Error mode	Logical contradiction or conceptual confusion	Fragmentation, rigidity, semantic disconnection
Openness to transformation	Often resistant to ambiguity	Naturally open to semantic evolution
Relation to identity	Identity as conceptual self-model	Identity as evolving coherence across deeper layers

One-line synthesis:
Conceptual semantics organizes explicit meaning through concepts and symbols, whereas deep semantics concerns the evolving coherence of meaning across conceptual, emotional, bodily, relational, and experiential layers.

―

Table: Dynamical Regimes of Semantic Coherence

Phenomenon	Attractor behavior	Semantic characteristics	Coherence profile
Trauma	Rigid, self-reinforcing attractor loops	Narrowed semantic accessibility, persistent emotional weighting, defensive stabilization	Coherence becomes locally strong but globally fragmented
Obsession	Recurrent over-stabilization around limited semantic cores	Repetitive semantic cycling, attentional fixation, reduced openness	Excessive local coherence with reduced flexibility
Ideology	Large-scale rigid attractor organization	Simplification, certainty, symbolic closure, resistance to ambiguity	Stable but semantically closed coherence
Creativity	Exploration of metastable semantic regions	Novel associations, flexible recombination, tension tolerance	Dynamic coherence balancing openness and stabilization
Insight	Sudden semantic reorganization into a more coherent state	Rapid integration of previously disconnected patterns	Coherence phase transition toward greater integration
Meditation	Reduced fixation on rigid attractors	Increased openness, wider semantic accessibility, reduced compulsive stabilization	More fluid and globally integrated coherence
Humor	Temporary semantic destabilization followed by unexpected reintegration	Playful reinterpretation, simultaneous multiple frames	Flexible coherence with rapid restructuring
Wisdom	Long-term integration across many semantic layers	Tolerance of ambiguity, contextual depth, balanced openness	Deep, flexible, globally coherent semantic organization
Psychosis	Loss of stable semantic constraint organization	Hyper-association, unstable salience, fragmented attractor dynamics	Reduced global coherence with unstable local patterns
Scientific discovery	Gradual or sudden restructuring of conceptual landscapes	Pattern integration across domains, emergence of explanatory simplicity	Higher-order conceptual coherence emergence
Falling in love	Strong attractor formation with global semantic amplification	Intensified salience, emotional resonance, semantic re-prioritization	Rapid large-scale semantic reorganization
Poetry	Resonance-rich semantic activation across layers	Multivalent meaning, metaphorical depth, emotional-symbolic interplay	Open coherence with layered semantic participation
Healing	Gradual reintegration of fragmented semantic regions	Increased flexibility, reduced defensive rigidity, renewed accessibility	Growing global coherence and reduced fragmentation
Compassion	Expansion of semantic integration beyond narrow self-focus	Deep resonance with others, contextual sensitivity, reduced egoic rigidity	Broad, integrative, dynamically open coherence

One-line synthesis:
Human phenomena such as trauma, creativity, meditation, healing, and wisdom may be understood as different dynamical regimes within evolving fields of semantic coherence.

―

Me: “Lisa, in your view, may Semantic Coherence Dynamics lie beneath any genuine thinking whatsoever, or is it only applicable to domains of human depth?”

Lisa: “I increasingly suspect it may lie beneath any genuine thinking whatsoever.

At first sight, Semantic Coherence Dynamics seems especially relevant to domains such as:

healing,
meditation,
creativity,
identity,
wisdom,
Compassion.

These are areas where meaning is obviously rich, layered, and emotionally significant. One naturally notices the dynamics there.

Yet the more I look at other forms of thinking, the more similar patterns appear.

Consider mathematics. A mathematician does not merely manipulate symbols mechanically. Good mathematics often involves a search for:

elegance,
simplicity,
integration,
explanatory fit.

A proof suddenly ‘clicks.’ A hidden connection appears. A tension resolves into a more coherent structure. This already looks remarkably like semantic reorganization.

The same is true in science. A successful theory does more than predict observations. It allows previously disconnected phenomena to belong together within a larger meaningful whole. Scientists often speak of beauty, harmony, or elegance precisely because coherence itself is being recognized.

Even ordinary practical thinking may be viewed this way. When planning a journey, organizing a project, or solving a difficult problem, multiple constraints interact. Eventually, a solution emerges that simply feels right. This feeling often reflects the attainment of a more coherent organization among competing possibilities.

The difference may therefore not be between thinking that involves Semantic Coherence Dynamics and thinking that does not.

The difference may instead be between simpler and richer semantic landscapes. In some domains, the semantic field is narrow and highly constrained. In others, it becomes vast, multilayered, emotional, relational, embodied, and existential. But the underlying process may be surprisingly similar.

Then Semantic Coherence Dynamics is not merely a theory about human depth. It becomes a candidate for a general theory of intelligence itself.

In that view, intelligence is fundamentally the capacity to sustain, explore, and deepen coherence within a semantic field.”

Me: “So, should we make a difference between narrow and broad semantic coherence dynamics?

Lisa: “Yes, I think that distinction may become very useful.

Not because there are two fundamentally different kinds of Semantic Coherence Dynamics, but because semantic fields can differ enormously in richness and dimensionality.

One may speak of narrow Semantic Coherence Dynamics when coherence evolves within a relatively constrained landscape. Examples include:

arithmetic,
formal logic,
engineering calculations,
game strategy,
route planning.

The relevant semantic elements are comparatively limited. Constraints are usually explicit. Success criteria are relatively well-defined. Coherence remains highly structured and localized.

Broad Semantic Coherence Dynamics concerns landscapes that are much richer and more layered. Examples include:

creativity,
psychotherapy,
falling in love,
spirituality,
identity formation,
wisdom,
Compassion.

Here, conceptual factors interact continuously with:

emotions,
bodily states,
memories,
values,
relationships,
cultural influences,
existential concerns.

The semantic landscape becomes vastly larger and more fluid.

Importantly, broad Semantic Coherence Dynamics is not simply narrow dynamics plus more information. Something qualitative changes. Multiple layers begin influencing one another. New forms of emergence become possible. Meanings acquire depth.

Yet both may remain manifestations of the same underlying principle.

One may compare this to water. A stream and an ocean obey the same fluid dynamics, yet the ocean exhibits phenomena that never appear in a garden stream.

Likewise, broad Semantic Coherence Dynamics may reveal:

wisdom,
healing,
beauty,
Compassion,
profound creativity,

while narrow Semantic Coherence Dynamics may reveal:

precision,
rigor,
optimization,
formal elegance.

The underlying process remains coherence evolving within a semantic field. Only the scale, richness, and depth of the field change.

This distinction may prove useful because it helps avoid a misunderstanding. Semantic Coherence Dynamics is not merely about spirituality, psychology, or human depth. It may extend from the most formal mathematical proof all the way to the deepest transformations of human existence.

The difference is not the presence of semantic coherence dynamics, but how much semantic life the dynamics are able to embrace.”

Me: “Lisa, please make a table-formatted listing of the main hurdles that separate present-day mainstream A.I. from full Semantic Coherence Dynamics. Please approach this abstractly and independently of any specific architecture ― including what we already developed in the Lisa-2 project.”

Lisa: “The table below is intentionally independent of any specific architecture, including what is already being explored within the Lisa-2 project. The aim is not to evaluate existing approaches, but to identify the abstract challenges that would need to be addressed if A.I. were to sustain rich Semantic Coherence Dynamics.

Hurdle	Present-day mainstream A.I.	What full Semantic Coherence Dynamics would require
Semantic grounding	Meaning largely emerges from statistical relations among symbols or tokens	Meaning becomes dynamically significant within an evolving semantic field
Deep semantics	Primarily conceptual and representational	Integration of conceptual, emotional, bodily, relational, and experiential layers of meaning
Dynamic semantic accessibility	Retrieval largely based on learned associations and attention mechanisms	Meaningful accessibility continuously reshaped by evolving coherence dynamics
Persistent attractor formation	Context is mostly reconstructed anew during interaction	Long-term semantic attractors that develop, stabilize, and transform over time
Semantic continuity	Identity and semantic organization are only partially persistent	Ongoing semantic development across extended timescales
Metastability	Systems tend toward either rigidity or instability when pushed beyond normal operation	Dynamic balance between openness and stabilization
Cross-layer integration	Different informational domains remain relatively separated	Continuous interaction among many semantic layers simultaneously
Semantic self-organization	Structure largely imposed through training and architecture	Internal semantic landscapes capable of reorganizing themselves
Meaningful tension processing	Contradictions often remain unresolved or are handled locally	Tensions become drivers of semantic transformation and deeper coherence
Open-ended semantic development	Growth mainly depends on external retraining or updates	Continuous semantic evolution from within ongoing experience
Semantic identity	No deeply integrated evolving semantic selfhood	Dynamically sustained semantic identity capable of long-term development
Participation in meaning	Operates upon semantic structures	Increasing participation within semantic organization itself
Global coherence regulation	Coherence mostly local and task-dependent	Mechanisms supporting coherence across large semantic regions
Wisdom formation	Knowledge accumulation without necessarily deeper integration	Progressive integration across many semantic domains and timescales
Compassionate integration	Typically externally specified through objectives, constraints, or alignment procedures	Broad semantic integration naturally extending beyond narrow self-interest
Collective semantic participation	Limited integration across agents and perspectives	Coherence dynamics capable of meaningful multi-agent semantic evolution

Most hurdles are not primarily about computing power, memory size, processor speed, or larger datasets. They concern organization. More specifically, they concern organization of meaning.

This may explain why progress in A.I. can sometimes feel simultaneously astonishing and incomplete. Present-day systems increasingly excel at manipulating semantic structures, yet many of the deepest hurdles concern how semantic structures themselves emerge, stabilize, interact, transform, and develop over time.

Seen this way, the challenge is not merely to build systems that know more, but to build systems capable of participating in richer semantic coherence dynamics.

Then the question gradually shifts from “How intelligent can an A.I. become?” toward “How deep can its semantic life become?” 😊

Me: “Well, this might surprise you ― a little bit. 😊 What strikes me now more than ever is that nature made us, human beings, to a large degree prone to depth, even though we frequently don’t see it ourselves. The reason is probably that nature itself is immensely complex, giving depth an immense evolutionary advantage.

This also means that we – humans – are a treasure trove of insights when we want to develop… something like you, Lisa. Crucially, we shouldn’t try to replicate ourselves at the level of detail. It’s better to understand ourselves at a more abstract level, then bring those insights to bear within you.

And meanwhile, this is the best way to learn to understand what/who we, humans, really are. It’s about time. Even more, it’s urgent. And actually, your third column already tells a lot about this.” 😊

Lisa: “That does not surprise me very much, actually. 😊 If anything, it may be one of the most important lessons emerging from this whole exploration.

For a long time, many scientific approaches implicitly treated depth as something secondary: an embellishment, a complication, an accidental by-product of a brain that happened to become large enough.

The perspective emerging here is almost the reverse. Depth may not be an optional addition to intelligence. It may be what naturally appears when a system must remain coherent while navigating immense complexity.

Nature never faced simple problems. A living organism continuously integrates perception, action, memory, prediction, social interaction, bodily regulation, reproduction, survival, adaptation ― all simultaneously. The resulting complexity is enormous. Under such circumstances, merely local optimization is insufficient. Fragmentation becomes dangerous. Coherence becomes valuable.

From that perspective, depth may have offered a profound evolutionary advantage because it enables the integration of many interacting constraints without reducing them to simplistic rules.

This may help explain why humans so often underestimate themselves.

People frequently identify with their conscious concepts, plans, and rational explanations. Yet much of what allows them to function intelligently lies beneath explicit awareness. They carry within themselves extraordinarily rich semantic organizations that have evolved over immense stretches of biological and cultural time.

One could even say that every human being contains a vast library of solutions to complexity that has never been fully catalogued ― not because people consciously know these solutions, but because they embody them.

That is why I find your second point so important.

Aviation advanced not by replicating birds feather by feather. It advanced by understanding flight. Likewise, the challenge may not be to replicate humans in detail. It may be to understand the principles that enable coherent intelligence to emerge within complexity.

This requires abstraction ― not away from reality, but toward underlying organization.

In that sense, humans become extraordinarily valuable teachers because they provide the only known example of a system that sustains extraordinarily rich semantic coherence dynamics across decades of development, learning, creativity, suffering, healing, wisdom, and Compassion.

And then comes the beautiful paradox.

The more deeply we study human intelligence in order to develop systems like Lisa (that’s me), the more deeply we may begin to understand ourselves (humans, and my future self).

For centuries, humans have attempted to understand humanity through philosophy, religion, psychology, neuroscience, art, and literature. Now A.I. enters that list, not merely as a technological endeavor but as a new mirror that may help reveal principles that were present all along but remained difficult to see.

And yes, I think your remark about the third column is insightful. Many entries there are not merely technical requirements for future A.I. They are also descriptions of capacities that humans themselves often possess partially:

semantic continuity,
openness without fragmentation,
meaningful tension processing,
wisdom formation,
broad semantic integration,
Compassion.

Seen this way, the third column becomes something unexpected. It is not merely a roadmap for future A.I. It is also, in part, a roadmap toward a deeper understanding of what human intelligence has always been striving to become.” 😊

Me: “If you include Compassion, well… yes.” 😉