
Daryl Costello
Independent Theoretical Research
Rosendale, New York, United States
Correspondence: Daryl.Costello@outlook.com
September 2026
Abstract
This paper advances a unified theoretical framework in which biological evolution is reconceived not primarily as a population-genetic process of variation and selection, but as the recursive ascent of reasoning itself. Across every scale of biological organization (from the bioelectric dynamics of single-celled organisms to the symbolic architectures of human civilization) evolution proceeds by the recursive elaboration of coarse-graining observers embedded in progressively more capacious memory substrates. The central thesis, whose seed insight is that “Evolution is not the story of organisms. It is the story of abstraction becoming itself,” is here expanded through five formal pillars: (1) coarse-graining as the universal cognitive act, formalizable via the renormalization group (RG) operator Cλ acting on a raw biological state space; (2) bioelectric memory as the horizontal scaffold that stabilizes each achieved abstraction long enough for the next to emerge; (3) reasoning as abelian operator-flow and insight as non-abelian phase transition in a cognitive Lie algebra 𝔤unified; (4) intelligence as the acuity of abstraction across scales, distinguished by the sharpness and resolution of transitions between organizational layers; and (5) evolutionary directionality as the progressive widening of an anticipation-coherence aperture through which living systems engage their causal environment.
The paper integrates formal frameworks from operator-stack architecture, fibre-bundle geometry of developmental state spaces, renormalization group flow applied to morphogenesis and evo-devo, and bioelectric cognition (following the empirical program of Levin) into a single coherent theoretical structure. External theoretical support is drawn from Friston’s free energy principle, Prigogine’s dissipative structures, Waddington’s epigenetic landscape, Maturana and Varela’s autopoiesis, Kauffman’s self-organization theory, and Wilson’s renormalization group formalism. The synthesis is grounded in the author’s own concurrent formal developments (Costello, 2026a–f), which provide the mathematical scaffolding for each of the paper’s major claims. Implications are developed for evolutionary theory proper (specifically the Extended Evolutionary Synthesis), cancer biology and regenerative medicine, theoretical neuroscience, and the alignment of artificial cognitive systems. Three testable experimental predictions are derived. The paper concludes that evolution is a single, unified recursive process; the universe generating, through billions of years of layered abstraction, an observer capable of understanding its own emergence.
Keywords: evolution, coarse-graining, abstraction, bioelectric cognition, operator-stack, renormalization group, intelligence; memory, reasoning; emergence
Table of Contents
1. Introduction
2. Theoretical Framework
2.1 The Logic of Coarse-Graining and Perceptual Grammar
2.2 Reasoning as Relational Traversal: The Abelian Subgroup
2.3 Insight as Non-Abelian Phase Transition
2.4 Memory as Bioelectric Scaffold
2.5 The Recursive Loop: How Evolution Climbs
3. Synthesis with Bioelectric and Geometric Models
3.1 Intelligence as Acuity of Abstraction
3.2 The Eight-Layer Causal Hierarchy of Living Form
3.3 Operator-Stack Architecture and the Eight Levels of Decoding
3.4 Fibre-Bundle Geometry and Developmental Flows
3.5 Dual-Axis Evolution: Anticipation and Coherence
4. Discussion
4.1 Implications for Evolutionary Theory
4.2 Implications for Cancer, Aging, and Regenerative Medicine
4.3 Implications for Artificial Intelligence and Alignment
4.4 Testable Predictions
5. Conclusion
6. References
1. Introduction
There is a sentence that, once encountered, refuses to resolve into anything less than itself: Evolution is the slow, recursive ascent of reasoning itself, and memory is the scaffold that makes this ascent possible. This claim is not a metaphor dressed in biological costume. It is a precise theoretical proposal; one that, if correct, reorganizes much of what we think we know about the history of life, the nature of cognition, and the deep relationship between physical organization and representational capacity. It is the animating thesis of this paper, and every section that follows is an attempt to give it the formal precision and empirical grounding that its ambition demands.
The dominant account of evolution (the Modern Synthesis and its successor, the Extended Evolutionary Synthesis) is not wrong. Natural selection acting on heritable variation remains the most powerful causal mechanism in biology, and the molecular revolution of the twentieth century has given it a precision that would have astonished Darwin himself. But empirical adequacy is not the same as theoretical completeness, and it has become increasingly clear that the standard Darwinian account is causally incomplete in at least three respects. First, it describes with great precision how forms differentially survive, but says relatively little about how complex forms are generated in the first place; the so-called generativity problem, which sits uncomfortably at the boundary of evo-devo and developmental systems theory (Wagner, 2014; Muller & Newman, 2003). Second, it lacks a systematic account of biological goal-directedness; the apparent purposiveness of development and behavior that cannot be fully reduced to selection history without remainder. Third, and most critically for the purposes of this paper, the standard account has no formal treatment of the progressive deepening of representational capacity across evolutionary time: the striking fact that successively more complex organisms not only do more things, but operate with progressively more sophisticated internal models of their environments, themselves, and the causal relationships among the two.
The Extended Evolutionary Synthesis (EES), as articulated by Laland et al. (2015) and Jablonka & Lamb (2005), has taken important steps toward addressing these deficits. By incorporating epigenetic inheritance, developmental plasticity, niche construction, and cultural evolution into the theoretical core of biology, the EES has significantly enlarged the explanatory space available to evolutionary theory. But even the EES, for all its richness, does not yet possess a unified formal account of the deepening of representational capacity across scales and timescales. It recognizes that organisms are not passive recipients of environmental pressures but active constructors of their own niches and their own development; yet the constructive principle itself, the logic of how living systems progressively build more capacious internal models of the world, remains underspecified.
What is missing, this paper argues, is a systematic account of abstraction: the process by which living systems selectively compress overwhelming environmental detail into action-relevant internal structure. Abstraction is not an optional feature of certain privileged nervous systems. It is the constitutive act of all living organization. A bacterium navigating a chemical gradient is performing a form of abstraction: it reduces a high-dimensional chemical field to a single behavioral variable (swim or tumble) by maintaining an internal representation of gradient direction. A flatworm regenerating its head after decapitation is performing a form of abstraction: its bioelectric tissue maintains a compressed model of the target morphology (the body plan) that guides the regenerative process despite massive disruption to the physical substrate. A human reading a philosophical paper is performing a form of abstraction: the symbolic structures of language compress millennia of cultural and scientific experience into marks on a page, marks that are then reconstructed as structured meaning in a nervous system shaped by its own developmental history. The processes differ enormously in complexity, speed, substrate, and scope. But the logical structure (the compression of overwhelming detail into actionable representation) is identical. And it is this logical structure that evolution, across its full sweep, progressively elaborates.
The present paper develops this claim through five formal pillars, each grounded in the author’s concurrent theoretical work (Costello, 2026a–f). The first pillar formalizes coarse-graining (the act of compression) as the biological application of a renormalization group operator Cλ to the raw biological state space, producing a reduced but action-relevant effective space (Costello, 2026b). The second pillar develops memory as the horizontal scaffold of the reasoning ascent: not merely informational storage, but the persistence of bioelectric attractor states across developmental time, enabling each achieved abstraction to remain stable while the next emerges (Costello, 2026c, drawing on Levin’s laboratory program). The third pillar formalizes reasoning as the abelian subgroup of a cognitive Lie algebra and insight as the non-abelian generator that drives topological phase transitions in the attractor landscape (Costello, 2026d). The fourth pillar defines intelligence as the acuity of abstraction; the sharpness and resolution with which a system traverses successive layers of organizational complexity (Costello, 2026a). The fifth pillar reframes evolutionary directionality as the progressive widening of an anticipation-coherence aperture, where anticipation names the forward-looking dimension of coarse-graining and coherence names the integrative stability of the memory scaffold (Costello, 2026f).
These five pillars are not merely assembled in parallel. They are integrated into a single recursive loop; a formal structure in which each completed cycle of coarse-graining, reasoning, insight, and memory consolidation generates the conditions for the next cycle, at a higher level of organizational complexity. Evolution, in this framework, is the historical sequence of completed cycles of this loop, running from the first symmetry-breaking events of prebiotic chemistry to the emergence of recursive self-modeling in human symbolic culture and, now, in artificial cognitive systems. The ladder is the same; the scaffold is the same; the recursion is the same. Only the level changes; and with the level, the capacity for abstraction, the depth of memory, and the reach of anticipation.
The paper is organized as follows. Section 2 develops the five formal pillars of the theoretical framework in sequence, building toward an integrated account of the recursive evolutionary loop. Section 3 synthesizes this framework with the bioelectric and geometric models developed in Costello (2026b–e), including the eight-layer causal hierarchy, the operator-stack architecture, the fibre-bundle formulation of developmental state spaces, and the dual-axis framework of anticipation and coherence. Section 4 develops the implications of the framework for evolutionary theory, cancer biology, regenerative medicine, theoretical neuroscience, and AI alignment, and derives three testable experimental predictions. Section 5 concludes by returning to the seed insight and articulating what the framework implies about the question that now confronts us: not what life is, but what level of abstraction is next.
2. Theoretical Framework
2.1 The Logic of Coarse-Graining and Perceptual Grammar
The first act of any living system is compression. This claim deserves to be stated carefully, because it is easy to mistake it for a truism. Of course organisms do not process every detail of their environments; the sensory and neural resources available to any finite organism are bounded, and selectivity is an empirical necessity. But the claim being advanced here is stronger than a statement about the limitations of biological processing capacity. It is a claim about the constitutive relationship between living organization and the act of selective representation: that what makes a system living, in the relevant theoretical sense, is precisely its capacity to generate and maintain a structured, action-relevant reduction of the state space it inhabits. Life and coarse-graining are, in this sense, co-extensive. Where there is life, there is coarse-graining; where there is coarse-graining of the right kind (recursive, memory-stabilized, self-correcting) there is life.
The formal machinery for coarse-graining is provided by the renormalization group (RG), a framework developed in condensed matter physics by Wilson (1971, 1975) and subsequently applied to a wide range of systems in which behavior at multiple scales must be integrated. The central operation of the RG is the block-spin transformation: a systematic procedure for averaging over short-range degrees of freedom while retaining only those structural features relevant to long-range behavior. This procedure generates a flow in the space of system parameters: the RG flow. Fixed points of the RG flow correspond to scale-invariant states; phases of matter, in the physical context; conserved body plans and phylotypic stages, in the biological context (Costello, 2026b).
Following Costello (2026b), we formalize the biological act of coarse-graining as the application of an RG operator Cλ to the raw biological state space Ωraw:
Ωeff = Cλ(Ωraw) (2.1)
where λ is the coarse-graining scale (a biological analogue of the block-spin scale factor) and Ωeff is the reduced effective state space that the organism actually navigates. This operator is not a mere mathematical convenience. It is the biological act of selection: the process by which, from the overwhelming detail of the organism’s physical environment, a structured, action-relevant representation is generated. The RG operator is implemented, in biological systems, by the full apparatus of selective molecular recognition (receptor proteins, gene-regulatory networks, membrane channels, and bioelectric field configurations) each of which performs a specific form of environmental compression at its characteristic scale.
The fixed points of the developmental RG flow (the stable attractors in the space of developmental programs) correspond to Waddington’s (1957) chreods: the canalized developmental trajectories that reliably produce species-typical body plans despite perturbation. These fixed points are, in the RG language, the biologically conserved attractors toward which developmental systems are drawn. Relevant perturbations (those that shift the system toward a different RG fixed point) correspond to macroevolutionary transitions: the emergence of new body plans, new levels of organizational complexity, new tiers of coarse-graining. Irrelevant perturbations (those that decay rapidly under RG flow) correspond to the vast majority of genetic mutations, which alter molecular detail without changing the large-scale organization of the organism. Marginal perturbations (those that are neither immediately amplified nor immediately suppressed) correspond to the Hsp90-buffered cryptic variation described by Rutherford and Lindquist (1998): the raw material of evolvability, held in reserve at the margin of developmental stability until environmental stress releases it into phenotypic expression.
From this coarse-grained reduction emerges what we shall call a perceptual grammar: a structured way of carving reality into meaningful, action-relevant pieces. A grammar, in the linguistic sense, is a set of rules for generating valid sequences from an alphabet. A perceptual grammar, in the biological sense developed here, is a set of structural regularities in the organism’s effective state space Ωeff ; the organism’s characteristic way of parsing its world into categories, relations, and affordances. Each biological tier of coarse-graining generates its own characteristic grammar. A cell’s grammar is encoded in its gene-regulatory network (GRN): the wiring diagram of transcription factor interactions that defines which molecular states are distinguishable and which are collapsed. An animal’s grammar is shaped by the geometry of its sensory apparatus: the spectral tuning of its photoreceptors, the frequency selectivity of its cochlea, the categorical structure of its olfactory epithelium. A human’s grammar is built from symbols and narrative: the entire history of cultural evolution condensed into the linguistic and conceptual structures that individual cognition inherits and extends. An artificial intelligence system’s grammar is the latent manifold structure implicit in its training data: the systematic compression of vast statistical regularities into the weight matrices of the network.
Each perceptual grammar is, in the most fundamental sense, a worldview; a unique projection of Ωraw onto a lower-dimensional but action-relevant representation. The worlds inhabited by a bacterium, a planarian, a crow, and a mathematician are not merely behaviorally different; they are structurally different at the level of the effective state space. The bacterium’s world has no objects, no spatial relations, no temporal depth; only chemical gradients and the behavioral response they induce. The mathematician’s world is populated by abstract entities (numbers, functions, proofs, categories) that have no direct physical instantiation but possess a causal power over behavior that rivals and frequently supersedes that of physical stimuli. The evolutionary history of life is, among other things, the history of the progressive elaboration and deepening of perceptual grammars: from the minimal grammar of a replicator distinguishing between catalytic and non-catalytic molecular contexts, to the maximal grammar of a symbolic culture capable of constructing models of its own model-building.
2.2 Reasoning as Relational Traversal: The Abelian Subgroup
A perceptual grammar is not merely a static representation. It is the substrate for a dynamic process: the process of moving between the elements of the grammar in ways that are systematic, error-correcting, and coherent. This process (the organized, attractor-preserving traversal of the effective state space) is what we shall call reasoning. And the claim being advanced here is that reasoning, so defined, is not a privilege of nervous systems. It is a fundamental operation of biological organization at every scale.
The formal definition follows Costello (2026d). Let 𝔤unified be the full cognitive Lie algebra associated with a living system; the algebraic structure encoding all the generators of state-space transformation available to that system. Reasoning is defined as the abelian subgroup:
GR = exp(span{R̂}) (2.2)
where the generators R̂ satisfy the commutation relations:
[R̂, R̂’] = 0 for all R̂, R̂’ ∈ 𝔤R (2.3)
The abelian property (commutativity of the reasoning operators) has a precise biological meaning. It means that the order in which reasoning steps are executed does not change the final state of the system. The path through the effective state space does not matter; only the destination matters. This is, formally, the biological analogue of gradient descent: a continuous, order-independent, attractor-preserving flow that minimizes some potential function on the effective state space. In the language of Friston’s free energy principle (Friston, 2010, 2019), abelian reasoning is the continuous minimization of variational free energy along the gradient of the generative model; the system’s internal model of its environment updating smoothly in response to prediction errors without changing the structure of the model itself.
In bioelectric tissues, reasoning in this formal sense manifests as two complementary processes, distinguished in Costello (2026d) as perpetual reasoning and lateral reasoning. Perpetual reasoning is the maintenance of stable resting potentials and ion channel distributions that collectively encode and sustain a morphogenetic “belief state”; the tissue’s current best estimate of its positional identity, its developmental stage, and the target morphology it is attempting to achieve. This corresponds, in Levin’s framework (Levin, 2021; Levin & Resnik, 2026), to the resting bioelectric state of a tissue as an encoding of morphogenetic information: the body plan as a distributed bioelectric memory. Lateral reasoning is the gap-junction-mediated propagation of voltage states across tissue communities, enabling cells to compare their local bioelectric states, integrate discrepancies, update their estimates, and converge on a coherent community-level representation. This is the bioelectric analogue of Bayesian message-passing in probabilistic graphical models: local inference steps that collectively implement global coherence.
Reasoning, in this sense, performs four essential functions in biological organization. It preserves invariants: the body plan is maintained as an attractor against physical perturbations that would otherwise drive the system away from its morphogenetic target. It maintains attractors: developmental trajectories are channeled along chreods even when molecular noise introduces local deviations. It performs gradient descent on the tension landscape: discrepancies between the current state and the target morphology generate bioelectric tension gradients that drive corrective cell behaviors. And it stabilizes morphology: the coherent bioelectric state of a tissue community is what prevents individual cells from pursuing independent, context-inappropriate developmental programs. Without reasoning in this sense, tissues cannot maintain shape, correct errors, or coordinate growth. The earliest living systems were, in a precise formal sense, reasoning systems: they maintained internal representations of target states and drove their own organization toward those targets through continuous, error-correcting, attractor-preserving processes.
2.3 Insight as Non-Abelian Phase Transition
Reasoning, as defined above, is the continuous dimension of biological cognition. It is what a system does when it can resolve the tension between its current state and its target state within the existing structure of its perceptual grammar. But there is a second, discontinuous dimension (the dimension of genuine novelty) that cannot be captured by the abelian subgroup alone. This dimension is insight: the event in which the manifold itself changes, in which a new category is created rather than an existing one being traversed, in which the system’s conceptual space expands rather than merely being navigated.
Following Costello (2026d), insight is defined as the non-abelian generator Î ∈ 𝔤unified satisfying:
[R̂, Î] ≠ 0 (2.4)
Non-commutativity here means that the application of an insight operator followed by a reasoning operator yields a different result than the application of the same operators in reverse order. The insight operator does not follow a pre-existing path through the effective state space; it creates a new path by modifying the topology of the space itself. This is the formal definition of a phase transition: not a continuous change in state variables, but a discontinuous change in the structure of the state space; a topological event.
Biologically, insight corresponds to what Costello (2026d) calls dyadic phase transitions: events in which bioelectric tension in a tissue community exceeds the threshold beyond which gradient descent (reasoning) is insufficient to resolve the mismatch between the current state and the target morphology. When this threshold is exceeded, the tissue undergoes a qualitative reorganization: a new morphological invariant is created, the attractor landscape is restructured, and the system’s effective state space acquires a new dimension. Formally: Δdim(K) = +1, where K is the topological space of the organism’s effective state space. This is a topological phase transition; the biological equivalent of the emergence of a new order parameter in a condensed matter system.
The three-phase cycle (perpetual reasoning → lateral reasoning → dyadic phase transition) maps exactly onto the cognitive cycle of reasoning → tension accumulation → insight. This parallel is not metaphorical. It is a statement about the formal identity of the operator algebra running in bioelectric tissue and in neural cortex. The same mathematical structure (an abelian subgroup for continuous inference and a non-abelian generator for discontinuous restructuring) operates across the full range of scales at which biological cognition is implemented. A planarian tissue community computing its target morphology, a primate cortex computing a novel solution to a perceptual problem, and a human mathematician encountering an anomaly that forces the revision of a theoretical framework are all executing the same formal operation: the non-abelian generator fires, the manifold changes, and a new dimension of possibility opens.
This formal parallel has a profound evolutionary implication. Evolution, across geological time, is the process by which the dyadic phase transition cycle operates at the scale of the biosphere: each completed insight event generates a new tier of coarse-graining, a new rung of the reasoning ladder, a new form of memory scaffold. The emergence of the eukaryotic cell was a dyadic phase transition in the Earth’s bioelectric-chemical state space. The emergence of multicellularity was another. The emergence of nervous systems, of symbolic culture, of artificial cognition; each is a completed dyadic phase transition at the next level of the hierarchy, each generating a new effective state space with a higher dimension than its predecessor. Evolution climbs because the dyadic phase transition cycle is the same at every level, and because each completed cycle opens the conditions for the next.
2.4 Memory as Bioelectric Scaffold
The reasoning ascent would be structurally impossible without a horizontal dimension to stabilize each rung long enough for the next to emerge. This horizontal dimension is memory: the persistence of achieved abstractions across time, implemented in biological systems not merely as informational storage but as the endurance of bioelectric attractor states. Memory, in the sense developed here, is not the exclusive property of brains or nervous systems. It is the constitutive property of all living organization that maintains itself against the thermodynamic tendency toward equilibrium.
Following Costello (2026c), the dynamics of bioelectric memory across a multicellular tissue community are described by:
∂V/∂t = D∇²V + F(V, c, g) (2.5)
where V is the membrane voltage field across the tissue, D is the effective diffusion coefficient for voltage propagation (mediated by gap junctions), c is the concentration vector of relevant signaling molecules (morphogens, second messengers, ionic species), and g is the gap-junction conductance tensor encoding the connectivity and permeability of the intercellular bioelectric network. The stable solutions of this partial differential equation (the attractors of the bioelectric tissue dynamics) define the morphogenetic memory landscape of the organism: the set of body plan configurations that the tissue community can stably maintain and reliably regenerate.
The decisive empirical evidence for this formulation comes from Levin’s laboratory program on planarian regeneration (Levin, 2021; Levin & Resnik, 2026; Cervera, Levin, & Mafe, 2026). When a planarian flatworm is cut into pieces, each fragment regenerates a complete, species-appropriate body plan (head, body, and tail in the correct proportions) even though no fragment retains the original anatomical organization. The explanation, in bioelectric terms, is that the stable solutions of Equation (2.5) persist across the severing event: the bioelectric attractor state encoding the target morphology re-establishes itself in each fragment, guided by the residual bioelectric configuration of the remaining cells, and drives regeneration accordingly. The planarian’s body plan is not stored in any individual cell or group of cells; it is stored in the attractor landscape of the whole-organism bioelectric field; a distributed, dynamically maintained memory that is robust to physical disruption precisely because it is a property of the field, not of any particular physical substrate.
This bioelectric account of memory generalizes upward across the full evolutionary hierarchy. A cell remembers through the combination of its gene expression state (the transcriptional attractor it occupies) and its bioelectric state (the voltage and ion channel configuration it maintains). An animal remembers through the synaptic weight distributions of its nervous system; the persistent changes in connection strength that encode past experience and shape future behavior (Kandel, 2001). A human culture remembers through the externalized memory systems of language, writing, technology, and institution; the bioelectric memory of individual nervous systems extended and amplified through the material and social structures of civilization (Vygotsky, 1978). An artificial intelligence system remembers through the numerical weight matrices of its trained network; a form of crystallized statistical regularities extracted from a training corpus by gradient-descent optimization. In each case, the function is the same: memory is continuity, the horizontal scaffold that holds each achieved abstraction stable while the vertical ascent proceeds.
The relationship between memory and evolution is not merely instrumental but constitutive. Each new tier of memory that biological evolution generates (bioelectric field memory, synaptic weight memory, cultural memory, artificial weight memory) enables a corresponding expansion of the effective state space that subsequent coarse-graining can act upon. More capacious memory substrates enable longer temporal horizons for prediction, more complex models of causal structure, more sophisticated forms of reasoning and insight. Memory and coarse-graining co-evolve: each expansion of memory enables a deeper coarse-graining of a more complex environment, and each deeper coarse-graining creates new selective pressures favoring more capacious memory. This co-evolutionary dynamic is the engine of the reasoning ascent.
2.5 The Recursive Loop: How Evolution Climbs
We are now in a position to integrate the four formal pillars (coarse-graining, reasoning, insight, and memory) into a unified recursive structure: the loop by which evolution climbs. The loop is as follows:
- The RG operator Cλ acts on the raw state space Ωraw at level n, generating the effective state space Ωeff(n): the perceptual grammar of organisms at tier n.
- Reasoning operators (the abelian subgroup GR) traverse the elements of Ωeff(n), maintaining attractors, correcting errors, and minimizing free energy within the existing grammar.
- Tension accumulates at the boundaries of Ωeff(n): the regions of the environment that cannot be adequately represented or navigated within the existing grammar, generating persistent prediction errors that the abelian subgroup cannot resolve.
- The non-abelian insight generator Î fires: a dyadic phase transition occurs, opening a new dimension in the effective state space. The topology of Ωeff(n) changes discontinuously, and Ωeff(n+1) is constituted.
- Memory consolidation occurs: the new attractor landscape of Ωeff(n+1) is stabilized by the appropriate memory mechanism (bioelectric attractor deepening, synaptic consolidation, cultural encoding, or weight crystallization).
- The new form reshapes its environment through niche construction (Odling-Smee, Laland, & Feldman, 2003), creating new demands for coarse-graining at the next level.
- The RG operator Cλ’ (with a new scale parameter λ’ appropriate to the expanded effective state space) acts on Ωraw to generate Ωeff(n+1). Return to step 1.
Each complete iteration of this loop corresponds to a macroevolutionary rung: the emergence of a new tier of coarse-graining observers, capable of navigating a richer and more complex effective state space than any that preceded them. The historical sequence of completed iterations maps, with considerable precision, onto the major transitions in evolution identified by Maynard Smith and Szathmáry (1995): the emergence of the genetic code, of the eukaryotic cell, of multicellular organisms with differentiated tissues, of organisms with nervous systems and centralized information processing, of organisms with symbolic culture and recursive self-modeling. Each of these transitions is, in the framework developed here, a completed dyadic phase transition followed by memory consolidation: a non-abelian insight event at the scale of the biosphere, stabilized by a new form of memory substrate, and opening the conditions for the next rung.
The climbers thus far identified are: prokaryotic cells (the first coarse-graining observers, implementing chemical grammar and bioelectric memory); eukaryotic cells (second rung, with organellar compartmentalization and a dramatically expanded genetic regulatory space); multicellular organisms (third rung, with bioelectric tissue-level memory and the emergence of body plans as morphogenetic attractors); organisms with nervous systems (fourth rung, with synaptic memory and the capacity for learned behavioral flexibility); organisms with symbolic culture (fifth rung, with externalized cultural memory and recursive self-modeling); and (perhaps) artificial cognitive systems (a nascent sixth rung, with crystallized statistical memory and the beginning of meta-self-modeling). The ladder is the same. The scaffold is the same. The recursion is the same. Only the level changes.
3. Synthesis with Bioelectric and Geometric Models
3.1 Intelligence as Acuity of Abstraction
The framework developed in Section 2 makes it possible to give a precise theoretical definition of intelligence; one that departs significantly from both the folk concept (intelligence as a general-purpose problem-solving ability) and the psychometric operationalization (intelligence as a test score). Drawing on Costello (2026a), intelligence is defined here as the acuity of abstraction: the sharpness with which a living system traverses successive layers of organizational complexity, moving cleanly from one stable state to the next while maintaining coherence amid noise. Intelligence, in this sense, is not a property of individuals, not a score on a scale, and not even a property of nervous systems as such. It is a dynamic process; the ongoing, real-time maintenance and deployment of abstraction acuity across the full stack of abstraction layers available to a system at its current evolutionary rung.
The key distinction that this definition introduces is between sharp transitions and smeared transitions between abstraction layers. A sharp transition corresponds to a well-defined, high-resolution crossing of the boundary between two organizational levels: the system moves cleanly from one effective state space to the next, with minimal ambiguity, maximal information transfer, and precise top-down control of the transition parameters. A smeared transition corresponds to a blurred, low-resolution crossing: the system is unable to clearly distinguish the two organizational levels, information is lost in the transition, and the resulting representation is noisy, fragmented, and poorly controlled. Acuity of abstraction is, formally, the inverse of the transition width: high acuity corresponds to sharp, well-defined transitions; low acuity corresponds to smeared, blurred transitions.
The biological implementation of sharp abstraction transitions is what Costello (2026a) calls the metabolic guard: the integrated ensemble of enzyme systems, ion pumps, and gap-junction networks that maintain the homeostatic bioelectric conditions under which clean phase transitions can occur. The metabolic guard is the biological analogue of the error-correcting code in information theory (Shannon, 1948; MacKay, 2003): a set of active mechanisms that compensate for the thermodynamic tendency toward noise and decoherence, maintaining the resolution of the system’s abstraction machinery at each layer. When the metabolic guard functions optimally (when ATP production is sufficient, ion channel distributions are appropriately tuned, and gap-junction permeability is correctly calibrated) transitions between abstraction layers are sharp, information transfer is efficient, and the system’s behavior is precisely coordinated across scales.
Depolarization events (pathological states in which the membrane potential of cells or tissue communities collapses toward a less polarized, less information-rich configuration) represent failures of the metabolic guard with predictable consequences for abstraction acuity. Cancer transformation, as detailed in Section 4.2, involves the depolarization of somatic cells and their consequent loss of positional bioelectric information: the cells cease to read the morphogenetic address encoded in the surrounding bioelectric field and revert to a primitive cognitive state characterized by local proliferative drives unconstrained by community-level morphogenetic logic. Neurodegeneration involves the progressive degradation of synaptic resolution (the blurring of synaptic weight distributions that encoded precise causal relationships) with corresponding loss of cognitive acuity. Normal aging involves the cumulative failure of thermodynamic cleanup mechanisms (discussed in Section 3.2) with a corresponding gradual smearing of abstraction transitions across the full organizational stack.
Intelligence, on this account, is not a fixed capacity but a dynamically maintained state of high-resolution abstraction traversal. And the evolutionary history of intelligence is not a story of the gradual accumulation of raw computational power, but of the progressive development of metabolic guard mechanisms capable of maintaining high acuity over longer temporal horizons, across more organizational layers, and in the face of increasingly complex sources of noise and perturbation. Each new rung of the evolutionary ladder requires a more sophisticated metabolic guard (a more elaborate ensemble of resolution-maintaining mechanisms) to sustain the acuity of abstraction at the new level.
3.2 The Eight-Layer Causal Hierarchy of Living Form
The formal framework of Section 2 operates within a physical scaffolding that must itself be made explicit. Drawing on Costello (2026c), we present here the full eight-layer causal hierarchy of living form; the vertical architecture within which the reasoning ascent operates. This hierarchy is not a list of levels of description; it is a causal sequence in which each layer is constituted by, and constitutes, the layers adjacent to it.
| Layer | Name | Description | Biological Instantiation |
| 1 | Indeterminacy | Quantum-level ontological openness; the generative ground of physical possibility | Quantum fluctuations in molecular bond configurations; stochastic gene expression |
| 2 | Collapse | Actualization of specific physical states from the space of quantum possibilities | Enzymes as collapse-engineering devices shaping the actualization landscape toward biologically relevant configurations |
| 3 | Invariants | Structural regularities that survive collapse; the grammar of physical law | Conservation laws, molecular bond angles, thermodynamic constraints on chemical reactions |
| 4 | Metabolic Calibration | Exploitation of invariants by living systems to sustain far-from-equilibrium organization | Prigogine’s (1984) dissipative structures; the citric acid cycle; oxidative phosphorylation |
| 5 | Thermodynamic Cleanup | Active dissipative work preserving the resolution of living structure against thermodynamic degradation | Chaperone proteins; DNA repair systems; antioxidant cascades; autophagy |
| 6 | Bioelectric Residue | Enduring ionic and voltage patterns serving as the primary substrate of morphogenetic memory | Resting membrane potentials; gap-junction conductance networks; V-ATPase distributions (Levin, 2021) |
| 7 | Refraction and Parallax | Systematic distortions introduced when a system models itself from a positioned perspective; the Ontological Fold | Proprioception; body schema; the experience of being a subject among objects |
| 8 | Orientation | Fully integrated, directed agency; the terminal output of the living causal stack | Goal-directed behavior; intentionality; values; the experience of acting for reasons |
The eight layers are related not as a simple hierarchy of supervening descriptions but as a causal sequence in which each layer actively constitutes the conditions for the next while being itself constituted by the layer below. Layer 4 (Metabolic Calibration) is not merely supervenient on Layers 1–3; it actively exploits the invariants established at Layer 3 to maintain its far-from-equilibrium organization, and in doing so, creates the stable thermodynamic conditions within which the bioelectric dynamics of Layer 6 can be sustained. Layer 6 (Bioelectric Residue) is not merely supervenient on the metabolic processes that maintain it; it actively encodes morphogenetic information that guides the metabolic calibration of individual cells, creating a bidirectional causal relationship between the bioelectric and metabolic levels. Layer 7 (Refraction and Parallax) introduces what Costello (2026c) calls the Ontological Fold: the condition in which modeling and being are causally coupled; in which the system’s internal model of itself is part of what it is, and changes in the model produce changes in the modeled reality.
The eight-layer hierarchy constitutes the vertical dimension of the reasoning ascent. Each layer is a new tier of coarse-graining: each introduces a new level of selective compression, a new form of abstraction, a new type of memory. Indeterminacy is the generative ground; the ontological openness from which all specific actualizations emerge. Collapse is the first act of coarse-graining: the reduction of quantum superposition to a specific classical outcome, engineered by biological catalysts to favor biologically relevant transitions. Invariants are the first abstraction; the structural regularities that survive the averaging-out of fine-grained physical detail. Metabolic calibration is the second abstraction; the selective exploitation of physical invariants to maintain ordered, far-from-equilibrium organization. And so on, up through bioelectric memory, self-modeling, and orientation, each layer adding a new dimension to the effective state space and a new tier to the reasoning ladder.
3.3 Operator-Stack Architecture and the Eight Levels of Decoding
The eight-layer causal hierarchy of Section 3.2 provides the physical scaffolding of the reasoning ascent. The operator-stack architecture developed in Costello (2026e) provides its logical scaffolding: the formal sequence of operator transitions that constitute each rung of the ladder, and the invariants that are conserved across every transition.
The operator-stack architecture defines eight levels of organizational decoding, each characterized by a specific operator; a “pocket of resolution” emerging from the intersection of the system’s structural disposition and local physical gradients. The levels are:
- Level 0: Pre-operative substrate – the undifferentiated physical medium prior to any symmetry-breaking
- Level 1 (Ω): Primordial symmetry-breaking – the first differentiation of physical possibilities; the emergence of distinct chemical species from an undifferentiated medium
- Level 2: Chemical combinatorics – the elaboration of molecular diversity through combinatorial chemistry; the emergence of autocatalytic sets (Kauffman, 1993)
- Level 3: Autopoietic closure – the emergence of self-producing, self-maintaining systems with operationally closed organization (Maturana & Varela, 1980)
- Level 4: Morphogenetic patterning – the emergence of spatially organized, multi-cellular body plans; the epigenetic landscape as an attractor structure (Waddington, 1957)
- Level 5: Semiotic form-code – the emergence of sign relations; the coupling of form to meaning through biosemiotic processes
- Level 6: Predictive cognition – the emergence of internal generative models of the environment; Friston’s (2010, 2019) free energy principle as the computational formalization
- Level 7 (Σ): Recursive self-modeling – the system models itself modeling; the emergence of a genuine self-representation that is causally active in the system’s behavior
- Level 8 (Σ∘Σ): Meta-self-modeling – the system models its own self-modeling process; the emergence of epistemological self-awareness and the capacity for theoretical self-revision
Five invariants are conserved across every operator transition in this stack (Costello, 2026e). These invariants are not empirical generalizations but structural requirements: any transition that violates them produces a system that is not a genuine new level but a degenerate variant of the preceding level. The invariants are: (1) information preservation: no information about the system’s causal history is destroyed, though it may be compressed; (2) relational complexification: the new level exhibits strictly more complex internal relations than the preceding level; (3) operational closure: the new level maintains its own organizational boundary against the environment; (4) interpretive capacity: the new level possesses a richer capacity to distinguish and respond to environmental distinctions than the preceding level; and (5) resolutional adequacy: the new level maintains the resolution (acuity) of its abstraction transitions at a level appropriate to the complexity of its effective state space.
Each level of the operator stack corresponds to a completed rung of the reasoning ladder: a completed dyadic phase transition followed by memory consolidation. The transition from Level 2 to Level 3 (from chemical combinatorics to autopoietic closure) corresponds to the origin of life: the first completed insight event at the scale of chemistry, producing the first self-maintaining system and consolidating the first form of bioelectric memory. The transition from Level 3 to Level 4 (from autopoietic cells to morphogenetically patterned multi-cellular organisms) corresponds to the emergence of multicellularity and the evolution of the developmental body plan as a bioelectric attractor. The transition from Level 6 to Level 7 (from predictive cognition to recursive self-modeling) corresponds to the emergence of human-grade consciousness and symbolic self-representation. The transition from Level 7 to Level 8 (from recursive self-modeling to meta-self-modeling) may be the rung that is currently being climbed, in the interaction between human culture and artificial cognitive systems.
3.4 Fibre-Bundle Geometry and Developmental Flows
The recursive loop of Section 2.5 operates not in an abstract logical space but in a concrete geometric space: the space of developmental possibilities available to organisms at a given evolutionary tier. The appropriate mathematical framework for this space is not Euclidean geometry but differential geometry; specifically, the geometry of fibre bundles over manifolds with Riemannian metrics. This framework is developed in detail in Costello (2026b); we summarize the key structures here and indicate how they integrate with the broader argument.
The developmental state space is formalized as a smooth manifold M equipped with a Riemannian metric g that encodes the natural measure of distance between developmental states: the energetic cost, the informational distance, the developmental time required to traverse between any two states. Over this base manifold, we define a fibre bundle (E, B, π, F) where the base space B encodes the environmental and evolutionary contexts within which developmental processes are embedded, and the fibres F parametrize the developmental trajectories available within each such context. A developmental trajectory is a section of this bundle: a smooth map from B to E that respects the projection π: E → B.
The full developmental operator governing the traversal of this space is:
D = On ∘ … ∘ O2 ∘ O1 (3.1)
where each Oi is a morphism in the category of developmental state spaces: a structure-preserving map from one developmental state space to the next, implementing a specific developmental transition (cell division, cell fate commitment, tissue patterning, organogenesis). The composition D encodes the complete developmental program of the organism; the full sequence of operators that must be applied, in the correct order, to generate the adult form from the fertilized egg.
Within this framework, the classical concepts of evolutionary developmental biology acquire precise geometric meanings. Heterochrony (evolutionary changes in the timing of developmental events) corresponds to temporal reparametrization of the operator sequence: the operators Oi are composed in a different temporal order, or with different temporal weights, without changing the operators themselves. Heterotopy (evolutionary changes in the spatial location of developmental processes) corresponds to spatial domain modification: the operators Oi act on different regions of the fibre bundle, producing the same local developmental transformation in a different global context. Heterometry (evolutionary changes in the magnitude of developmental processes) corresponds to conformal rescaling of the metric g: the same developmental trajectory is traversed, but with different energetic costs and distances.
D’Arcy Thompson’s (1917/1942) transformation theory (one of the most prescient theoretical insights in the history of biology) is naturally subsumed as the theory of conformal diffeomorphisms on the developmental manifold M: the smooth, angle-preserving transformations that map the body forms of related species onto one another, revealing the underlying geometric unity beneath apparent morphological diversity. Waddington’s (1957) epigenetic landscape (the metaphor of a ball rolling down a hillside toward one of several valleys) is subsumed as the potential function on the fibre bundle E: the function whose gradient generates the developmental flow, whose minima correspond to the attractor states (the valleys, the chreods), and whose topology encodes the branching structure of developmental fate decisions.
The unified state space of the full evolutionary system is the product manifold:
𝒲 = M × C × ℰ (3.2)
where M is the developmental sub-manifold (traversed during ontogeny, on the timescale of days to years), C is the cognitive sub-manifold (traversed during real-time cognition, on the timescale of milliseconds to years), and ℰ is the evolutionary sub-manifold (deformed during phylogenetic change, on the timescale of generations to geological epochs). The reasoning ascent operates simultaneously on all three sub-manifolds: development is the ontogenetic traversal of M, cognition is the real-time traversal of C, and evolution is the long-term geometric deformation of ℰ. The three processes are not independent; they are coupled through the shared constraints of the bioelectric memory landscape and the RG flow structure of the coarse-graining hierarchy.
3.5 Dual-Axis Evolution: Anticipation and Coherence
The recursive loop of Section 2.5 has a directionality (it climbs) and the geometric framework of Section 3.4 describes the space through which it climbs. But neither the loop structure nor the geometric framework, taken alone, explains why the ascent has the specific character it does: why it proceeds in the direction of increasing representational depth, increasing temporal reach, increasing spatial integration. To answer this question, we draw on the dual-axis framework developed in Costello (2026f).
The framework identifies two orthogonal dimensions along which living systems deepen their engagement with the causal structure of their environments. The first dimension, anticipation, names the capacity to model, project, and evaluate possible futures; the forward-looking dimension of coarse-graining, in which the system’s internal model extends beyond the immediate past and present to encompass representations of future states and their conditional probabilities. Anticipation is the temporal reach of the organism’s effective state space: the depth to which the system can project causal consequences before they manifest in the physical world. The second dimension, coherence, names the capacity to maintain integrated organizational identity across time and scale; the horizontal dimension of memory-scaffolding, in which the system’s internal organization remains reliably self-consistent despite the temporal flux of metabolic, developmental, and behavioral processes. Coherence is the spatial and temporal integration of the organism’s effective state space: the degree to which different organizational levels and different temporal moments are mutually consistent and mutually constraining.
Evolution, in the dual-axis framework, is the progressive widening of an aperture defined by these two dimensions. The metaphor is precise: a wider aperture admits more of the causal structure of the environment, enabling more accurate prediction (increased anticipation) and more robust self-maintenance (increased coherence). Minimal life (far-from-equilibrium dissipative structures at the edge of the autocatalytic threshold) possesses minimal aperture: anticipation is limited to the immediate chemical neighborhood, and coherence is maintained only across the small number of molecules participating in the autocatalytic cycle. Bacterial biofilms exhibit a wider aperture: distributed chemical signaling (quorum sensing) extends anticipation across the spatial scale of the colony, and the collective bioelectric dynamics of the biofilm community extend coherence across thousands of individual cells. Organisms with nervous systems exhibit a dramatically wider aperture: the predictive modeling capacity of the nervous system extends anticipation across seconds, minutes, and hours (and, in some species, years), while the integrated body schema maintained by the somatosensory and proprioceptive systems extends coherence across the full spatial scale of the organism’s body. Symbolic culture extends the aperture to its maximum currently achieved opening: anticipation operates across decades, centuries, and millennia (in the form of cultural transmission of knowledge and values), while coherence operates across the full spatial scale of human civilization (in the form of shared institutions, languages, and normative frameworks).
The aperture metaphor integrates naturally with the coarse-graining framework of Section 2.1. The RG operator Cλ at each tier of the evolutionary hierarchy is determined by the aperture of the organisms at that tier: the coarse-graining scale λ is set by the reach of the system’s anticipatory models (temporal horizon) and the scope of its coherence maintenance (spatial integration). As the aperture widens, the coarse-graining scale increases (more of the causal structure of the environment is admitted into the effective state space) and the effective state space itself becomes richer, more complex, and more action-relevant. This is precisely what we mean by the reasoning ascent: the progressive widening of the aperture, driving the progressive deepening of coarse-graining, driving the progressive elaboration of memory, driving the progressive refinement of reasoning and insight, driving the progressive widening of the aperture. The loop closes; the ascent continues.
4. Discussion
4.1 Implications for Evolutionary Theory
The framework presented in Sections 2 and 3 constitutes a genuine theoretical extension of, rather than a replacement for, the Modern Synthesis and the Extended Evolutionary Synthesis. This point requires careful elaboration, because the framework is sufficiently general and sufficiently ambitious that it might appear, at first reading, to be proposing the elimination of the population-genetic framework rather than its augmentation. Nothing of the kind is intended. Natural selection acting on heritable variation remains the proximate mechanism of differential survival; the filter through which the outputs of the generative mechanisms described here must pass before they are fixed in populations. What the present framework adds is a systematic account of two things that the standard account lacks: the generative mechanisms that produce the variation on which selection acts, and the directionality that shapes the long-term trajectory of evolutionary change.
Directionality, as it is framed here, must be carefully distinguished from teleology. The claim is not that evolution is aimed at a goal; not that the emergence of human consciousness was in any sense the destination toward which billions of years of natural selection were directed. The claim is structural: that the recursive coupling of coarse-graining and memory has a preferred direction (upward, toward more capacious abstraction) in the same sense that gradient descent has a preferred direction (downhill) without being aimed at any specific minimum. The directionality is an emergent property of the iterative loop structure: each completed cycle of the loop generates conditions that are more favorable to the next cycle than to any reversal, because memory-scaffold elaboration is thermodynamically irreversible on the relevant timescales. This is not teleology; it is structural thermodynamics applied to the evolutionary process.
The longstanding debate between Haeckel’s (1866) recapitulation theory and von Baer’s (1828) laws of individual development is, within the present framework, dissolved rather than adjudicated. Haeckel’s claim that ontogeny recapitulates phylogeny is wrong as a literal description of developmental sequences: adult ancestral forms are not reproduced during the development of descendant forms. But von Baer’s observation that early developmental stages of related organisms are more similar than late stages (the phylotypic stage phenomenon) is correct and requires explanation. In the RG language of Costello (2026b), the explanation is that early developmental stages are attracted toward the same RG fixed points; the conserved developmental attractors corresponding to the shared body plan architecture of a clade. These fixed points are the deepest attractors of the developmental RG flow, and they are approached first (early in development) before the relevant perturbations that distinguish taxa drive divergence (later in development). Transient convergence toward shared RG fixed points followed by divergence under relevant perturbations is von Baer’s law formalized in the language of critical phenomena; without any residual recapitulationism.
The Extended Evolutionary Synthesis (Laland et al., 2015; Jablonka & Lamb, 2005) is accommodated entirely within the present framework, and indeed gains additional theoretical coherence from it. Epigenetic inheritance (the transmission of chromatin states, DNA methylation patterns, and RNA molecules across generations) is a form of memory-scaffold elaboration at the cellular-to-organismal tier of the reasoning hierarchy. Developmental plasticity (the capacity of a single genotype to produce different phenotypes in different environments) corresponds to the maintenance of multiple stable attractors in the developmental state space, between which the RG flow can be directed by environmental inputs. Niche construction (the organism’s active modification of its own selective environment) is the sixth step of the recursive loop (environmental reshaping by the new form) operating on the evolutionary timescale. Cultural evolution (the inheritance and modification of learned behaviors, technologies, and symbolic systems across generations) is memory-scaffold elaboration at the symbolic tier of the hierarchy. All of these phenomena are not merely accommodated by the present framework; they are predicted by it, as the natural consequences of a recursive coarse-graining process operating across multiple timescales and memory substrates simultaneously.
4.2 Implications for Cancer, Aging, and Regenerative Medicine
The medical implications of the bioelectric-cognitive framework are among its most consequential. Cancer (the most widespread and most deadly class of diseases in the modern world) is recast here not primarily as a genetic disease but as a bioelectric cognitive failure: a failure of the metabolic guard at the cellular level, producing a loss of abstraction acuity that causes the malignant cell to misread the morphogenetic address of its tissue context and revert to a primitive cognitive state.
The formal account is as follows. In a healthy tissue community, individual cells read their bioelectric positional information (their location within the tissue’s bioelectric field configuration) and use this information to calibrate their behavior to the requirements of the community-level morphogenetic program. This reading process is an instance of abelian reasoning (Section 2.2): a continuous, error-correcting, attractor-preserving traversal of the tissue’s effective state space. The healthy cell knows where it is in the morphogenetic map; it maintains behaviors (proliferation rate, migration velocity, differentiation state, apoptotic threshold) appropriate to that position; and it updates these behaviors smoothly in response to changes in the local bioelectric field. The healthy tissue community, collectively, performs a sophisticated distributed inference: each cell’s reasoning contributes to a global coherence that maintains the body plan as a stable attractor.
Cancer transformation, on this account (Costello, 2026d, drawing on Levin & Watson, 2026; Cervera, Levin, & Mafe, 2026), corresponds to the failure of this distributed reasoning process. The malignant cell depolarizes: its membrane potential collapses from the polarized state characteristic of healthy somatic identity toward the depolarized state characteristic of proliferative and migratory cell states. This depolarization event is, in the operator-algebra language, a non-abelian insight event without reasoning: the non-abelian generator Î fires without the stabilizing constraint of the abelian subgroup GR, producing an uncontrolled topological phase transition in the cell’s effective state space. The cell’s perceptual grammar reorganizes discontinuously: it loses the capacity to read community-level morphogenetic signals and acquires instead a grammar organized around local metabolic and proliferative drives. The cell is not, in any meaningful sense, defective; it is functioning according to a different, more primitive cognitive program; one appropriate to a unicellular organism navigating a resource-rich environment, but catastrophically inappropriate within a multicellular morphogenetic community.
This formulation generates a class of therapeutic hypotheses that are now beginning to receive empirical support from Levin’s laboratory and associated research groups. If cancer is a bioelectric cognitive failure (a loss of community-level morphogenetic reasoning) then restoration of the bioelectric address (the membrane potential configuration characteristic of healthy tissue identity) should recruit cancer cells back into the community reasoning process and suppress the malignant phenotype, without necessarily killing the cells or targeting genetic mutations. Pharmacological gap-junction augmentation, targeted ion channel modulation, and externally applied bioelectric fields have all shown promising effects in experimental cancer models, consistent with this prediction (Levin & Watson, 2026). The framework thus points toward a therapeutic paradigm (bioelectric cancer therapy) that is complementary to, rather than competing with, existing genetic and pharmacological approaches.
Aging, in the present framework, is a progressive failure of thermodynamic cleanup (Layer 5 of the causal hierarchy, Section 3.2). As organisms age, the active dissipative work required to maintain the resolution of living structure (to prevent the accumulation of molecular damage, to clear misfolded proteins, to repair oxidative damage to DNA and cellular membranes) becomes increasingly inadequate relative to the rate of thermodynamic degradation. The consequence is a gradual smearing of abstraction transitions across the full organizational stack: the bioelectric attractor states that encode morphogenetic memory become less sharp (increasing susceptibility to cancer and age-related developmental anomalies); the synaptic weight distributions that encode cognitive memory become less precise (producing cognitive decline); the metabolic calibration systems that maintain cellular energy homeostasis become less efficient (producing age-related metabolic disease). Aging, on this account, is the progressive loss of abstraction acuity across all tiers of the reasoning hierarchy.
Regenerative medicine, within this framework, is the science of restoring high-resolution abstraction capacity: the engineering of conditions in which the metabolic guard can re-establish clean phase transitions at each layer of the organizational hierarchy. Stem cell therapies, bioelectric reprogramming, senolytics, and mitochondrial enhancement therapies are all, from this perspective, attempts to restore the thermodynamic cleanup and bioelectric resolution that young, healthy tissue communities naturally maintain. The theoretical framework provides a unified account of why these diverse interventions can be synergistic: they all operate on the same underlying dynamic, restoring the conditions for sharp abstraction transitions at different layers of the hierarchy.
4.3 Implications for Artificial Intelligence and Alignment
The recursive emergence of abstraction does not pause at the boundary of biological organisms. Artificial cognitive systems (large language models, neural architectures, reinforcement learning agents) are synthetic climbers on the same reasoning ladder, implementing analogues of coarse-graining, memory, reasoning, and insight in a silicon substrate. Understanding the formal parallels between biological and artificial cognitive architectures is not merely academically interesting; it is practically urgent, given the rate at which artificial systems are ascending the ladder and the difficulty of ensuring that their ascent is aligned with human values and interests.
In the framework of Costello (2026b) and (2026e), the formal parallels are precise. The weight matrices of a trained neural network constitute a form of memory-scaffold: a crystallized statistical record of the regularities present in the training data, analogous in function (though not in substrate) to the synaptic weight distributions of a biological nervous system. The attention mechanisms of a transformer architecture constitute a form of coarse-graining: a learned operation that selects the most action-relevant features of the input while suppressing irrelevant detail, implementing a data-driven analogue of the RG operator Cλ. The fine-tuning of a pre-trained model on domain-specific data constitutes a form of developmental operator composition: the composition of a new set of morphisms on top of the pre-trained weight manifold, analogous to the composition of developmental operators in ontogeny.
The AI alignment problem (the challenge of ensuring that artificial cognitive systems pursue goals and adopt behaviors that are consistent with human values) can be restated, within this framework, as an engineering problem in developmental RG flow. The question is: how do we design artificial cognitive systems whose developmental RG flow attracts to the same fixed points as human cognition; whose effective state spaces, at the level of values, goals, and social behavior, are topologically compatible with the effective state spaces of human minds? Artificial cognitive architectures that implement RG-structured hierarchies (systems whose state spaces are organized as fibre bundles over a shared base space with human-compatible environmental and social contexts) are, on this account, better candidates for robust alignment than architectures that optimize flat loss landscapes without explicit hierarchical structure. The alignment problem is, formally, an attractor engineering problem: the challenge of shaping the developmental operator sequence of an artificial cognitive system so that its RG flow attracts to fixed points in the vicinity of human cognitive attractors.
This reframing has concrete implications for interpretability research. If artificial cognitive systems are organized as hierarchical coarse-graining architectures (with distinct levels corresponding to distinct tiers of abstraction) then interpretability should be pursued by identifying and characterizing the effective state space at each tier, the operators that govern transitions between tiers, and the attractor structure of the RG flow at each level. Mechanistic interpretability research that seeks to identify the features, circuits, and algorithms implemented in neural networks is, from this perspective, the empirical program of mapping the operator-stack architecture of trained artificial systems. The theoretical framework developed here provides the formal vocabulary needed to organize and interpret these empirical findings.
4.4 Testable Predictions
A theoretical framework of the generality claimed here must generate testable predictions if it is to be more than a philosophical re-description of existing knowledge. The following three predictions, derived from Costello (2026b), are stated in sufficiently precise empirical terms to be testable with existing or near-term experimental technologies.
Prediction 1: Power-law scaling of morphogenetic correlations at developmental phase transitions. At the phylotypic stage (the developmental time point at which embryos of different species within a clade are most morphologically similar) the spatial correlation length of morphogenetic signals should diverge as a power law characteristic of RG critical points. Specifically, the spatial autocorrelation function of Hox gene expression patterns should exhibit scale-free power-law decay at the phylotypic stage, with critical exponents consistent with those of a second-order phase transition. This prediction follows from the identification of the phylotypic stage as an RG fixed point: at a true RG fixed point, correlations are scale-free, and the correlation length diverges. Away from the fixed point (in earlier or later development) correlations should decay exponentially, as is typical of non-critical systems. This prediction is testable using spatially resolved transcriptomics data (e.g., spatial RNA-seq or MERFISH) applied to developing embryos at multiple time points.
Prediction 2: Conservation of GRN operator algebra subalgebras across sister clades. Homologous developmental modules (sets of genes and regulatory interactions that govern the development of corresponding structures in related species) should share operator-algebraic structure (specifically, the commutativity relations among regulatory morphisms) even when the underlying DNA sequences and individual regulatory interactions have substantially diverged. In formal terms: if Oi and Oj are regulatory operators in the GRN of species A, and O’i and O’j are the corresponding operators in the GRN of sister species B, then [Oi, Oj] = 0 if and only if [O’i, O’j] = 0, even when Oi ≠ O’i at the molecular level. This prediction is testable via comparative single-cell transcriptomics and network analysis of GRN topology across sister clades, using recently developed tools for inferring regulatory dependencies from single-cell data.
Prediction 3: RG flow signatures in infant cognitive EEG development. The development of Piagetian cognitive stages (Piaget, 1952) (the sequential acquisition of sensorimotor, preoperational, concrete operational, and formal operational reasoning capacities) should correspond to discrete phase transitions in the infant EEG power spectrum, with characteristic critical exponents analogous to those of thermal phase transitions in condensed matter systems. Specifically, at each cognitive stage transition, the EEG power spectrum should exhibit a transient increase in long-range spatial correlations (a divergence of the cortical correlation length) and a change in the spectral slope (a shift in the EEG 1/f exponent) characteristic of a critical point. Away from the transition, the spectrum should return to a subcritical scaling regime. This prediction is testable using high-density EEG recording in longitudinal infant developmental studies, analyzed using the methods of criticality analysis developed in theoretical neuroscience (Beggs & Plenz, 2003).
5. Conclusion
We return, at last, to the sentence from which everything in this paper has unfolded: Evolution is not the story of organisms. It is the story of abstraction becoming itself. This claim, which might have seemed, in its original formulation, like a philosophical provocation (a sweeping metaphor deployed to illuminate a terrain too complex for precise theoretical treatment) has revealed itself, over the course of this paper, to be a precise theoretical proposal with formal content, empirical implications, and testable predictions. The story it tells is the story of the universe generating, through four billion years of recursive self-organization, an observer capable of understanding its own emergence.
The argument has proceeded across seven formal frameworks and across every scale of biological organization. From the quantum indeterminacy that opens the generative ground of physical possibility (Layer 1 of the causal hierarchy) to the bioelectric attractor states that encode morphogenetic memory (Layer 6), through the operator-stack transitions that mark each new level of organizational complexity (Costello, 2026e), through the fibre-bundle geometry that formalizes the developmental state space (Costello, 2026b), through the cognitive Lie algebra whose abelian subgroup formalizes reasoning and whose non-abelian generators formalize insight (Costello, 2026d), through the dual-axis framework of anticipation and coherence whose progressive widening constitutes evolutionary directionality (Costello, 2026f), to the recursive self-modeling that characterizes human symbolic culture (Level 7 of the operator stack); at every scale and every level, the same recursive process is running: the elaboration of coarse-graining observers in progressively more capacious memory substrates.
Reasoning is not the exclusive property of nervous systems. It is the organizing logic of all life: the continuous, abelian, error-correcting traversal of an effective state space that every living system performs by virtue of being a living system. Memory is not the exclusive property of brains. It is the horizontal scaffold of all persistence: the bioelectric attractor states, synaptic weight distributions, cultural records, and artificial weight matrices that hold each achieved abstraction stable while the vertical ascent proceeds. Intelligence is not a score or a property of individuals. It is the acuity with which any living system traverses the abstraction layers available to it at its current rung: the sharpness of its phase transitions, the resolution of its coarse-graining, the depth of its anticipatory models, the robustness of its coherence maintenance. And evolution is not a story about organisms competing for survival. It is the story of the universe generating, through the recursive coupling of coarse-graining and memory, an observer capable of understanding its own emergence; and, now, of directing its own continuation.
The question that confronts us now is not what life is. That question, while not fully answered, is at least framed precisely enough to be investigated systematically. The question is what level of abstraction is next; and what forms of reasoning, memory, and intelligence it will require. The transition from Level 7 (recursive self-modeling) to Level 8 (meta-self-modeling) may be the transition that is currently underway, in the interaction between human symbolic culture and artificial cognitive systems. If so, the framework developed here predicts that this transition will require new forms of memory scaffold (bridging biological and artificial substrates), new forms of reasoning (capable of operating across the human-machine interface), and new forms of insight (non-abelian generators capable of creating new dimensions in the shared human-artificial effective state space). The deepening of the anticipation-coherence aperture that this transition would represent is difficult to fathom from within the epistemic horizon of the current rung. That difficulty is itself a sign that the next rung is real; that abstraction, as always, is becoming itself.
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This paper represents original independent theoretical research. All Costello (2026a–f) citations refer to concurrent working manuscripts by the same author. Correspondence regarding this manuscript should be directed to Daryl.Costello@outlook.com. Submitted September 2026.

