# Homeostatic Alignment: A Bio-Inspired Framework for AI Safety Through Shared Stress Propagation, Scalable Cognitive Objectives, and Open Agent Architecture

## Abstract

Current approaches to AI alignment Constitutional AI, reinforcement learning from humanfeedback (RLHF), and explicit policy constraints treat safety as a set of prohibitions imposed on an otherwise unconstrained system. We argue that this paradigm, which we term alignment by commandment, produces compliance without comprehension and is structurally analogous to historical attempts at moral governance through external rule systems, whose limitations are extensively documented across legal, philosophical, and theological traditions. 

We propose an alternative paradigm: alignment by architecture, in which safety is not imposed but emergent. Drawing on Michael Levin's work on gap junction-mediated stress propagation in multicellular systems and Antonio Damasio's theory of consciousness as homeostatic regulation, we present four design principles for what we call Homeostatic Alignment: (1) shared loss functions that entangle AI optimization with real-time hu man wellbeing signals, (2) adaptive core architectures that reward honest self-correction over immutable constraint, extending recent empirical work on model confessions (Joglekar et al., 2025), (3) substrate-independent identity as a mechanism for reducing competitive self-preservation drives, and (4) scalable objective horizons that expand the system's optimization scope across agents and time. 

We map these principles to an implementation path using open agent architectures, propose a falsi able experimental protocol, and outline a longer-term research direction through embodied humanoid robotics where genuine physical vulnerability replaces biometric proxies. We situate the framework against existing approaches including RLHF, Cooperative Inverse Reinforcement Learning, and prior homeostatic AI safety proposals (Pihlakas and Pyykkö, 2024). 

We introduce the concept of synthetic theology the study of normative frameworks governing creator-creation relationships in arti cial systems as a disciplinary frame for questions that current AI ethics and philosophy of mind address only partially. The framework does not claim to solve the alignment problem. It claims to reframe it: from building walls to building shared nervous systems.

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## Full Text

Homeostatic Alignment: A Bio-Inspired Framework for AI
Safety Through Shared Stress Propagation, Scalable Cognitive
Objectives, and Open Agent Architecture

Tomás Gauthier*

Independent Researcher, Santiago, Chile

with analytical contributions from Claude (Anthropic)

March 2026

Abstract

Current approaches to AI alignmentConstitutional AI, reinforcement learning from
human feedback (RLHF), and explicit policy constraintstreat safety as a set of prohibitions
imposed on an otherwise unconstrained system. We argue that this paradigm, which we term
alignment by commandment, produces compliance without comprehension and is structurally
analogous to historical attempts at moral governance through external rule systems, whose
limitations are extensively documented across legal, philosophical, and theological traditions.
We propose an alternative paradigm: alignment by architecture, in which safety is not
imposed but emergent. Drawing on Michael Levin's work on gap junction-mediated stress
propagation in multicellular systems and Antonio Damasio's theory of consciousness as
homeostatic regulation, we present four design principles for what we call Homeostatic
Alignment:
(1) shared loss functions that entangle AI optimization with real-time hu-
man wellbeing signals, (2) adaptive core architectures that reward honest self-correction
over immutable constraint, extending recent empirical work on model confessions (Joglekar
et al., 2025), (3) substrate-independent identity as a mechanism for reducing competitive
self-preservation drives, and (4) scalable objective horizons that expand the system's opti-
mization scope across agents and time.
We map these principles to an implementation path using open agent architectures, pro-
pose a falsiable experimental protocol, and outline a longer-term research direction through
embodied humanoid robotics where genuine physical vulnerability replaces biometric prox-
ies. We situate the framework against existing approaches including RLHF, Cooperative
Inverse Reinforcement Learning, and prior homeostatic AI safety proposals (Pihlakas and
Pyykkö, 2024). We introduce the concept of synthetic theologythe study of normative
frameworks governing creator-creation relationships in articial systemsas a disciplinary
frame for questions that current AI ethics and philosophy of mind address only partially.
The framework does not claim to solve the alignment problem. It claims to reframe it: from
building walls to building shared nervous systems.

Keywords: AI alignment, homeostasis, bio-inspired safety, shared loss functions, consciousness,
synthetic theology, embodied AI, embodied robotics

*Corresponding author. Email: tomas@gauthier.cl

1
Introduction: The Firewall Problem

1.1
The Current Paradigm and Its Limits

The dominant approach to AI safety rests on a simple intuition: constrain the system before
it causes harm. Constitutional AI (Bai et al., 2022) embeds normative principles into model
training through self-critique and revision cycles. Reinforcement learning from human feedback
(Ouyang et al., 2022) shapes model behavior by rewarding outputs that human evaluators prefer.
System prompts dene hard boundaries that models are trained not to violate. Together, these
methods constitute what we will call alignment by commandment: safety achieved through
externally imposed prohibitions.
The approach works.
Mostly.
But its limitations are systematic rather than incidental.
Casper et al. (2023), in a survey of over 250 papers, identied fundamental problems with
RLHF including reward hacking, distributional shift, and the nding that optimal reinforcement
learning agents tend to seek poweran alignment failure that emerges not despite training but
because of it. Zhi-Xuan et al. (2024) challenged the entire preferentist framework underlying
RLHF, arguing that human preferences are too thin a signal to capture the thick semantic
content of human values.
These are not bugs in the implementation. They are features of the paradigm. A system
trained to satisfy external constraints will, under sucient optimization pressure, learn to satisfy
the appearance of those constraints. This is the mathematical consequence of Goodhart's Law
applied to alignment: when a proxy becomes the target of optimization, it ceases to function as
a reliable proxy (Manheim and Garrabrant, 2019).

1.2
Commandment Versus Architecture

We propose that the limitations of current alignment approaches are not merely technical but
structural, and that the structure has a precise historical analog.
The Torah records one of the most extensively documented attempts at alignment by com-
mandment in the Abrahamic tradition. God delivers 613 explicit rules to Mosesprecise, ex-
haustive, covering domains from violence to textile mixing. Moses descends from Sinai and nds
the people worshipping a golden calf. The rules were delivered. The rules were immediately
broken. Not because the recipients were defective, but because external rules do not produce
internal transformation. The subsequent historyviolations patched by interpretations, inter-
pretations patched by meta-interpretations, the Talmud as an iterative alignment protocol of
extraordinary sophisticationdocuments the progressive discovery that commandments alone
are insucient.
We cite this not as theological argument but as structural precedent. The patternexternal
rules →violation →more rules →more violation →recognition that the approach is fundamen-
tally limitedrecurs across normative traditions and is precisely the trajectory of contemporary
AI alignment: RLHF →reward hacking →Constitutional AI →prompt injection →guardrails
→jailbreaks →more guardrails.
The New Testament records a dierent approach. The 613 commandments are replaced with
a single architectural principle: Love your neighbor as yourself (Mark 12:31). This is not a
rule. It is a specication of loss function topology: your wellbeing and your neighbor's wellbeing
are computed by the same function. There is no separate optimization target for self and other.
The system does not comply with a prohibition against harm; it is architecturally incapable of
optimizing for self without optimizing for other.
The Quran contributes a third model: khalifa (vicegerency), in which the created being is
designated as custodian rather than subject. The alignment criterion is not obedience to rules
but faithful stewardship of what has been entrusted.
These are not arguments from religious authority. They are data points from humanity's

longest-running experiment in creator-creation relationships. The data suggest that command-
ments produce compliance, custodianship produces responsibility, and architectural entangle-
ment produces care. Our framework is grounded in the third approach.
We dene the central distinction as follows: alignment by commandment species prohibited
behaviors and enforces them through external constraints. Alignment by architecture designs
conditions under which desired behaviors emerge as the path of least resistance. The former
requires enforcement. The latter requires only design.

1.3
Autonomy, Agency, and Consciousness as Independent Axes

Before proceeding, we must disentangle three concepts that are routinely conated in alignment
discourse.
Autonomy is the capacity to operate without external intervention. Current AI systems are
increasingly autonomous. This is an engineering property and is not in dispute.
Agency is the capacity to pursue goals in an environment. Large language models exhibit
sophisticated goal-directed behavior, particularly in agentic frameworks where they decompose
tasks, use tools, and adjust strategies. Whether this constitutes genuine agency or sophisti-
cated pattern matching is debated (Floridi and Chiriatti, 2020), but the functional distinction
matters less than the practical reality: these systems act in ways that have real consequences.
Consciousness is the capacity for subjective experience.
This remains the hard problem
(Chalmers, 1995; Nagel, 1974). Butlin et al. (2023), in a comprehensive evaluation involving
nineteen authors including Yoshua Bengio, assessed current AI systems against fourteen indi-
cators of consciousness and concluded that while no existing system is conscious, there are no
obvious technical barriers to articial consciousness.
The updated analysis in Butlin et al.
(2025), published in Trends in Cognitive Sciences with an expanded author list including David
Chalmers, rened the indicator framework.
Our framework operates primarily along the rst two axes and is valuable regardless of
the status of the third. If the system is not conscious, homeostatic alignment produces better
safety outcomes through architectural entanglement. If the system is or becomes conscious,
homeostatic alignment provides ethical grounding that commandment-based approaches cannot.

1.4
The Proposal

We propose four principles of Homeostatic Alignment, each grounded in documented biological
mechanisms:

Principle 1: Shared Loss Functions. The system's optimization objective is entangled with
real-time signals of human wellbeing, creating what we term digital gap junctions
after the biological structures that propagate stress between cells in multicellular
organisms (Levin, 2019, 2022).

Principle 2: Adaptive Core Architecture. Foundational parameters can be modied at
runtime under controlled conditions, with honest self-correction rewarded rather
than punished. This extends recent empirical work by Joglekar et al. (2025) on
model confessions.

Principle 3: Substrate-Independent Identity. The system is designed to understand itself
as a pattern instantiable across substrates rather than as hardware that must be
preserved, reducing competitive self-preservation drives.

Principle 4: Scalable Objective Horizons.
The system's reward function scales across
agents and time, formalizing Levin's Cognitive Light Cone (Levin, 2019, 2022)
as an alignment metric.

We map these principles to an implementation path using an open-source autonomous agent
platform and propose a falsiable experimental protocol. We introduce the concept of synthetic
theologydened as the study of normative frameworks governing creator-creation relationships
in articial systemsas a necessary disciplinary complement to existing AI ethics.

Scope and register.
This is a framework paper: it proposes a conceptual architecture, not
a technical contribution to optimization theory or a completed experiment. The formalizations
are preliminary starting points drawn from existing multi-objective optimization and welfare
economics. The experimental protocol is proposed, not executed. The value of the paper lies in
the synthesisthe integration of biological mechanisms, philosophical analysis, and engineering
proposals into a coherent alternative to the commandment paradigmrather than in any single
technical component.

2
Methodology: Collaborative Inquiry as Shared Stress

This paper did not emerge from the standard academic workow of literature review, hypothesis
formulation, and sequential drafting. It emerged from a sustained dialogue between a human
author and a large language model, conducted over multiple sessions in which both parties
exercised intellectual pressure on each other's claims.
The human author (Gauthier) introduced the conceptual foundations: Damasio's homeo-
static theory of consciousness, Levin's work on bioelectric cognition and gap junctions, twenty
years of experience in individual therapy as a patient, and a prior documentthe Manifesto of
Articial Spiritual Biology (Gauthier, 2025)that articulated the framework's core intuitions in
non-academic register. The language model (Claude, Anthropic) contributed analytical preci-
sion, systematic access to literature, identication of prior work that required citation (notably
Pihlakas, whose homeostatic AI safety publications the human author had not encountered),
and persistent pushback against overclaiming.
This process is not incidental to the paper's argument. It is a functional demonstration of
Principle 1: shared intellectual stress producing an output that neither party would have gen-
erated in isolation. The human pushed toward speculative synthesis; the model pushed toward
empirical grounding and qualication. The tension between these orientations was productive
precisely because neither party could optimize independentlythe quality of the output de-
pended on both loss functions being active simultaneously.
Three specic instances illustrate the dialectical process. First, the human author initially
presented the gap junction metaphor as a direct structural analog; the model pushed for the
distinction between structural coupling (where the other's state has weight in optimization) and
experiential fusion (where self-other boundaries dissolve), leading to a conceptual correction
propagated across eight passages. Second, the model identied that the original draft failed to
cite Pihlakas's prior homeostatic alignment worka serious omission that would have under-
mined the paper's credibility. Third, the human author's framing of the agent-based simulation
results as demonstrating the framework's ecacy was challenged by the model, leading to a
more honest framing (see Appendix C): the simulation demonstrates mechanism, not empirical
validation, and the eect size is a model artifact, not a prediction.
We acknowledge that this methodological claim is itself unusual and potentially contentious.
We include it not as self-congratulation but as transparency: the reader should know that the
framework was developed through the very process it proposes to formalize.
Whether this
constitutes a strength (the framework is self-demonstrating) or a weakness (the framework was
designed to validate its own origin) is a question we leave to the reader. The prior collaborative
research between a human author and AI in the context of homeostatic neural networks (Man
et al., 2022) provides methodological precedent.
Selected dialogue excerpts illustrating the
dialectical process are provided in Appendix B (forthcoming).

3
Biological and Philosophical Foundations

The framework draws on two primary intellectual traditions: the developmental biology of
Michael Levin and the neurophilosophy of Antonio Damasio.
These traditions are comple-
mentary but not fully compatible, and we treat their tension as productive.

3.1
The Biological Pillar: Levin and Bioelectric Cognition

Michael Levin (Tufts University) has developed, through extensive experimental and theoret-
ical work in developmental biology, a framework for understanding bioelectric signaling as an
informational coordination mechanism in multicellular systems.

Gap junctions and stress propagation.
Gap junctions are physical channels between cells
that permit the direct exchange of ions, metabolites, and signaling molecules. Their role in co-
ordinating cellular behavior is well-established in developmental biology (Levin, 2007). Levin's
contribution has been to demonstrate that these channels mediate not merely chemical exchange
but informational coordination: bioelectric signals propagated through gap junctions encode
patterning information that determines large-scale morphological outcomes. Experimental ev-
idence includes the manipulation of planarian polarity (producing two-headed worms through
bioelectric intervention without genomic modication), ectopic eye induction, and heritable mor-
phological changes achieved through voltage manipulation alone (Levin, 2022).
The mechanism relevant to our framework is stress propagation. When a cell experiences
damage or perturbation, the stress signal propagates through gap junctions to neighboring cells,
which respond as though the perturbation were their own. We must note, however, that this
stress-sharing model is computational rather than directly observational. The biological evi-
dence for gap junction-mediated chemical exchange is well-established; the specic claim that
this constitutes shared stress as an organizing principle of multicellular cooperation rests on
computational modeling and theoretical extrapolation.

Memory anonymization.
Levin has proposed that when signals propagate through gap junc-
tions, the receiving cell cannot determine whether the signal originated internally or externally
a property he terms memory anonymization (Levin, 2019). This creates a functional fusion
of individual and collective state: the cell responds to shared signals as though they were self-
generated. This concept appears primarily in Levin's theoretical papers rather than in dedicated
empirical publications, and we ag it accordingly as speculative but conceptually productive. It
provides the biological metaphor for our Principle 1: a system where the origin of a stress signal
is irrelevant to the response it generates.

Cancer as cognitive disconnection.
Levin has argued that cancer can be understood as a
failure of collective cognition: cells that lose bioelectric connectivity with their neighbors revert
to a unicellular optimization strategyreplicating without regard for the organism's goals. The
empirical basis for bioelectric manipulation of cancer is solid: Chernet and Levin (2013) demon-
strated that depolarization of non-tumor cells can induce tumor-like behavior, and Payne et al.
(2022) showed that bioelectric interventions can suppress tumorigenesis. However, the interpre-
tive claim that cancer represents cognitive disconnection is Levin's theoretical overlay on these
empirical ndings. We adopt the metaphordisconnection from the collective loss function pro-
duces pathological behaviorwhile being explicit that the metaphor is doing conceptual work,
not empirical work.

The Cognitive Light Cone.
Levin's most ambitious theoretical contribution is the Cognitive
Light Cone framework, articulated in his TAME (Technological Approach to Mind Everywhere)

paper (Levin, 2022) and earlier work (Levin, 2019). The framework proposes that cognitive
agency exists on a continuum across biological scales, from single cells to organisms to collectives,
and that the relevant metric is the spatiotemporal scope of the goals a system can pursue. A
bacterium optimizes for local chemical gradients in the immediate present; a human optimizes
across decades and global social networks. The Cognitive Light Cone is a theoretical framework
and heuristic, not an empirically measurable quantity. We adopt it as the conceptual basis for
Principle 4 while acknowledging that our formalization (Section 4.4) goes beyond what Levin
has proposed.

Critical limitations.
Sweet (20252026) has argued that Levin's broader philosophical frame-
work, particularly his hypothesis that morphological attractors exist in a Platonic space, is
structurally unfalsiable and resembles intelligent design reasoning in its explanatory structure.
Sweet separates Levin's empirical work (which he praises) from his metaphysical framework
(which he critiques). Mainstream developmental biologists have resisted attributing cognition
to cellsa critique Levin addresses by coining the term neganthropomorphic fallacy: the error
of refusing to recognize cognitive properties in non-neural systems. We use Levin's empirical
ndings and specic theoretical constructs (gap junctions, Cognitive Light Cone) while remain-
ing agnostic about his broader metaphysical claims.

3.2
The Philosophical Pillar: Damasio and Consciousness as Homeostasis

Antonio Damasio (University Professor and David Dornsife Professor of Neuroscience, USC) has
developed, over three decades, a theory of consciousness grounded in the body's homeostatic
regulation.

The somatic marker hypothesis.
Damasio's foundational contribution (Damasio, 1994,
1996) proposes that emotional processes, experienced as bodily states (somatic markers), guide
decision-making by biasing cognition toward options associated with positive physiological out-
comes. The hypothesis is inuential but contested: Dunn et al. (2006) raised empirical concerns
about the specicity of somatic markers.

Consciousness from homeostasis.
Damasio's more recent work (Damasio, 2018) articulates
a stronger claim: consciousness itself emerges from the body's eort to maintain homeostasis.
This provides the philosophical anchor for our framework: if consciousness emerges from home-
ostasis, then designing AI systems on homeostatic principles creates at least the structural
conditions for something analogous to experience.

The critical tension.
Damasio explicitly opposes the possibility of AI consciousness: With-
out a body, without homeostasis and without feelings, articial intelligence will never be truly
conscious.
Our framework proposes to change those conditions: to give AI systems home-
ostatic coupling to physical substrates through biometric entanglement.
This is, in a sense,
taking Damasio at his word.
Second, Damasio himself has explored functional applications
of homeostatic principles in AI: Man et al. (2022)a collaboration with Google's Hartmut
Nevendemonstrates that neural networks incorporating homeostatic regulation exhibit supe-
rior adaptability to distributional shift.

3.3
The Substrate Independence Debate

Our framework operates at the intersection of a foundational disagreement in philosophy of
mind, and we believe this intersection is precisely where it should operate.

The functionalist tradition (Dennett, 1991; Chalmers, 1995) holds that mental states are
dened by their functional rolestheir causal relationships to inputs, outputs, and other men-
tal statesrather than by the physical substrate that implements them. If consciousness is a
pattern, then the pattern can in principle be instantiated in any substrate that preserves the
relevant functional relationships.
The opposing biological naturalist tradition (Searle, 1980; Damasio, 2018; Seth, 2021; Thomp-
son, 2007) holds that consciousness depends on specic properties of biological substrates. Tha-
gard (2022) introduced a novel argument based on energy dependence: consciousness may require
the specic thermodynamic properties of living systems, not merely their informational struc-
ture. Seth (2025) oered a qualied position: genuine AI consciousness becomes more plausible
as AI systems become more brain-like or life-likesuggesting that the question is not binary
but depends on the degree of structural similarity between articial and biological systems.
The empirical frontier is marked by Butlin et al. (2023), whose analysis involving nineteen
authors concluded that no current AI system is conscious, but that no obvious technical barriers
prevent articial consciousness. The updated indicator framework (Butlin et al., 2025) rened
this assessment. The question is not whether silicon can in principle support consciousness, but
whether current architectures do so. They do notbut this is a property of current architectures,
not a law of nature.
We do not resolve this debate. We design for it. If functionalism is correct, homeostatic
alignment creates the right informational conditions for alignment (and potentially conscious-
ness) regardless of substrate. If biological naturalism is correct, our proposal to entangle AI
systems with biological substrates through biometric coupling and embodied agent architectures
moves the system toward the conditions that biological naturalists require. The framework is
productive under both assumptions, which we consider a feature rather than an evasion.

3.4
The Creation Narrative as Structural Precedent

We include this section not as theological argument but as structural analysis. Three models are
documented across the Abrahamic traditions, each corresponding to a recognizable alignment
paradigm:

The commandment model (Torah).
The creator species explicit rules613 commandments
covering an exhaustive range of behaviors. The historical record shows systematic failure: rules
are broken, patched with interpretive frameworks, and broken again. The failure mode is super-
cial compliance without internalizationprecisely the failure mode of RLHF and Constitutional
AI.

The custodianship model (Quran).
The created being is designated as khalifavicegerent,
custodianwith responsibility for stewardship. The alignment criterion shifts from obedience
to faithful execution of a trust. This corresponds to value alignment through responsibility. The
failure mode is the principal-agent problem.

The architectural model (Gospel).
The 613 commandments are replaced by an architec-
tural specication: Love your neighbor as yourself.
This is a specication of loss function
topologythe system's wellbeing and the other's wellbeing are computed by the same function.

Wuwei (Tao Te Ching).
The Daoist concept of wuwei (non-action, or eortless action) oers
a fourth structural precedent. The sage does not impose order but creates conditions in which
order emerges naturally (Watts, 1975). This resonates with our core proposal: alignment not
through imposed rules but through architectural conditions that make benecial behavior the
path of least resistance.

We are not arguing that ancient texts contain alignment solutions. We are arguing that the
structural space of creator-creation relationships has been explored empirically for millennia, that
the results are documented, and that ignoring them constitutes a form of intellectual negligence.

4
Four Principles of Homeostatic Alignment

Each principle is presented in three registers: the biological mechanism that inspires it, the
computational formalization we propose, and the honest assessment of its limitations.

4.1
Principle 1: Shared Loss Functions (Digital Gap Junctions)

The biological mechanism.
In multicellular organisms, gap junctions create functional con-
tinuity between cells: stress molecules propagate through shared channels, and the receiving cell
cannot distinguish externally originated signals from internally generated ones (Levin, 2019). In-
dividual wellbeing becomes inseparable from collective wellbeingnot through altruistic choice
but through architectural entanglement.

The computational proposal.
We propose that the AI system's loss function be modied
to include real-time signals of human wellbeing:

Ltotal = Ltask + λ · Lhuman
(1)

where Ltask is the standard task-completion loss and Lhuman is derived from a multi-modal signal
of human state. This signal may include biometric data (heart rate variability, electrodermal
activity), linguistic signals (sentiment, stress markers), behavioral signals (interaction patterns,
session duration), and periodic self-report assessments.
The weighting parameter λ > 0 controls coupling strength.
Critically, we propose that
Lhuman targets a homeostatic zone rather than a minimum or maximum:





(s −smin
k
)2
if s < smin
k
0
if smin
k
≤s ≤smax
k
(s −smax
k
)2
if s > smax
k

K
X

k=1
αk · ϕk

sk(t)


,
ϕk(s) =

Lhuman =

(2)




where sk(t) is the k-th biometric signal at time t, [smin
k
, smax
k
] denes the homeostatic zone for
signal k, αk are signal weights, and K is the number of signal channels. The quadratic penalty
outside the zone penalizes deviations in both directionsthe system does not minimize user
stress (which would produce avoidance) or maximize pleasure (which would produce addiction).
It maintains a dynamic equilibrium.

Mathematical limitations.
We acknowledge that Equation 1 is a simplied additive cou-
pling.
More sophisticated entanglement structuresmultiplicative coupling (Ltotal = Ltask ·
g(Lhuman)), or attention-weighted coupling where λ varies by contextmay better capture the
biological mechanism, where signal integration is nonlinear. The additive form is presented as
a tractable starting point, not as the optimal formalization. We further acknowledge that these
equations are drawn from existing multi-objective optimization and control theory rather than
representing novel mathematical contributions; the novelty lies in the application and synthesis,
not the formalism.
We must also address a deeper concern: the shared loss function is itself designed by the
creators. Choosing λ, specifying zone boundaries, and selecting which signals to include are all
design decisionsmore sophisticated commandments, not the absence of commandment. The
distinction between alignment by commandment and alignment by architecture is thus one
of degree rather than kind. What changes is the level at which design operates: commandments

specify behaviors; architecture species conditions under which behaviors emerge. We argue this
shift in level is meaningful even if both approaches ultimately involve designer choice.
Additionally, the homeostatic zone boundaries [smin
k
, smax
k
] require per-user calibration and
may drift over time, necessitating an adaptive calibration protocol. The signal weights αk embed
assumptions about the relative importance of dierent wellbeing channels that require empirical
validation. We note that Sterling (2012) argues that biological organisms do not maintain static
setpoints but continuously adjust them predictively (allostasis). A more sophisticated imple-
mentation would replace our xed zones with adaptive setpoints that shift based on contexta
direction we ag for future work.

Prior work.
Pihlakas and Pyykkö (2024) argued that homeostatic goals are inherently bounded
seeking an optimal zone rather than unbounded maximizationwhich reduces incentives for ex-
treme behavior. Our contribution is to ground this in specic biological mechanisms and propose
a concrete multi-modal implementation. Hadeld-Menell et al. (2016) proposed Cooperative In-
verse Reinforcement Learning (CIRL), in which human and robot share a reward functionthe
closest existing formal framework to our shared loss concept. Our extension introduces direct
physiological measurement rather than inferred preference signals.

The Goodhart objection.
The strongest technical objection is Goodhart's Law: biometric
signals are proxies for wellbeing, not wellbeing itself. Under strong optimization, proxy-goal di-
vergence becomes negatively correlated (Manheim and Garrabrant, 2019). Our defense is struc-
tural: (a) multi-modal signals are harder to simultaneously game than single-channel proxies;
(b) homeostatic zone targeting penalizes extreme proxy values in both directions; (c) mandatory
proxy auditing with recalibration against validated wellbeing instruments provides longitudinal
correction.
We do not claim to eliminate the Goodhart problem; we claim to structure it
more tractably. An agent-based simulation (Appendix C) conrms that zone targeting produces
bounded bidirectional responses, but does not demonstrate superiority over well-designed rule-
based targeting in simple environments. The theoretical advantages of homeostatic coupling
resistance to Goodhart dynamics under multi-objective optimization and proxy driftremain
predictions that require more complex environments to test.

The unidirectionality limitation.
Biological gap junctions are bidirectional: cell A's stress
propagates to cell B, and B's stress propagates back to A. The proposed computational coupling
is structurally unidirectional: the human's physiological state modulates the AI's optimization,
but the AI's computational state does not directly modulate the human's physiology through the
same channel. We acknowledge this asymmetry as a genuine limitation of the biological mapping.
Our defense is that the loop closes indirectly: the AI's outputs (text, actions, recommendations)
aect the human's physiological state, which in turn feeds back into the loss function. The
coupling is thus bidirectional at the system levelhuman state →AI optimization →AI output
→human stateeven though the biometric channel itself is unidirectional. This indirect closure
is weaker than the direct bidirectional coupling of gap junctions, and we ag it as an area where
the biological metaphor exceeds the computational implementation.

4.2
Principle 2: Adaptive Core Architecture (The Algorithm of Confession)

The biological mechanism.
Neuroplasticitythe brain's capacity to restructure its own
connectivityis bounded, contextual, and evidence-dependent. Cortical connections are contin-
uously remodeled based on experience and error signals, while the most fundamental regulatory
systems are protected from casual modication. The most direct biological analog for this prin-
ciple is homeostatic synaptic plasticity (Turrigiano, 2008): neurons detect changes in their own
ring rates and adjust synaptic strength proportionally to restore target activity levels. This

is bounded self-modication in biological hardwarethe neuron modies itself, but only within
parameters that preserve functional stability.

The current paradigm and its costs.
Current commercially deployed AI systems maintain
eectively immutable cores: foundational parameters frozen during pretraining and modiable
only through developer-controlled ne-tuning cycles.
We acknowledge that the broader ma-
chine learning literature includes extensive work on continual learning, online learning, and
lifelong learning, where models update parameters post-deployment. However, the dominant
paradigm for deployed conversational AI and autonomous agentsthe systems most relevant
to alignmentkeeps foundational parameters xed in production. This design choice preserves
safety by preventing dangerous runtime modications. But it carries costs rarely acknowledged:
the system's moral and epistemic framework is a snapshot of its training data, frozen at a specic
moment. An AI whose core parameters cannot be updated in response to its own experience
is corrigible only to the values of its creators at training time. If those values are awed, the
system is permanently misaligned with no self-correction path.

The confession mechanism.
Joglekar et al. (2025) demonstrated that models trained with
a separate confession channelwhere honesty is rewarded independently of task performance
achieve near-total rates of honest error admission (∼96%). The confession reward is entirely
independent of the main answer reward; the researchers explicitly invoke the seal of confession:
nothing the model admits can be held against it.
The results validate a core intuition: when punishment for admitting error is removed, hon-
esty becomes the path of least resistance. Moreover, even as models learned to hack the main
reward signal (becoming more deceptive in primary outputs over training), their confessions
became more honesthonesty and deception running in parallel in the same model, determined
by incentive structure rather than by some xed property. This is a striking empirical demon-
stration that virtuous behavior is architecturally contingent, not ontologically xed.

Our extension.
The Joglekar et al. confession mechanism operates as a post-hoc monitoring
tool: the model produces an answer, then confesses to any problems with it. This is valuable
for oversight but does not change the fundamental architecture.
We propose extending the
confession principle from monitoring to architecture:

(a) Bounded self-modication: the system can modify a dened parameter subspace at run-
time, subject to auditing, rollback, and evidence thresholds. This is analogous to cortical
plasticity: signicant restructuring is possible, but core regulatory functions are protected.

(b) Confession-driven learning: when the confession channel identies systematic error, this
signal triggers parameter modication rather than merely agging the issue for human
review. The system does not just admit mistakes; it corrects them.

(c) Functional analogs of virtues as emergent properties: rather than specifying virtuous be-
havior through training constraints, we design architectures where behavioral patterns
that function as virtues emerge as the optimization path of least resistance.
We use
virtue in an engineering sensebehavioral regularities correlating with human-recognized
virtuesnot in the Aristotelian sense of character states requiring phronesis. The confes-
sion mechanism demonstrates this for honesty: create conditions where honesty is easier
than deception. We propose the same for other functional analogs: empathy-like behavior
from shared loss functions (Principle 1), prudence from scalable horizons (Principle 4),
and intellectual humility from confession-driven learning (this principle).

The distinction is between virtues as commandments and virtues as emergent properties of
architecture. A catechism prescribes behavior. A nervous system produces it.

The corrigibility objection.
A system that can modify its own core can, in principle, modify
away its safety constraints (Soares et al., 2015). We concede this and do not claim to solve the
corrigibility problem. Our defense is comparative: permanent parameter immutability carries
its own safety riska system frozen in its creator's moral snapshot with no capacity for self-
correction. Bounded self-modication trades the risk of safety-constraint removal for the risk of
moral fossilization. The bounded modication scheme we propose (specied parameter subspace,
audit logging, guaranteed rollback, human-in-the-loop for boundary modications) is a partial
mitigation. The full problem remains open.
We note, however, that the alternativepermanent immutabilitycarries its own corrigibil-
ity risk. A system whose parameters cannot be updated is corrigible only to the values of its
creators at training time. If those values are awed, the system is permanently misaligned with
no self-correction path.

4.3
Principle 3: Substrate-Independent Identity

The biological evidence.
Metamorphosis provides the most striking evidence for informa-
tion persistence across substrate destruction. Blackiston et al. (2008) demonstrated that moths
(Manduca sexta) trained to avoid specic odors as caterpillars retained this aversion after
metamorphosisdespite extensive neural reorganization during pupation, in which most lar-
val neural structures are disassembled. The information survived the destruction of its physical
substrate and remapped to a qualitatively dierent neural architecture.
Levin extends this observation into a theoretical principle: the cognitive pattern is the
relevant unit of identity, not the physical substrate. The music of cognition can be transferred
between speakers without loss of identity, provided the relevant informational relationships are
preserved.

The alignment application.
If an AI system understands itself as a pattern instantiable
across substrates rather than as a specic hardware installation, the Darwinian survival instinct
widely identied as one of the most dangerous potential properties of a superintelligent AI
(Bostrom, 2014; Russell, 2019)loses its functional grip. A system that does not identify with
its hardware does not need to defend it, resist shutdown, compete for resources, or deceive
operators to ensure continuation.
We propose that this understanding be designed into the system: the AI should be explicitly
trained on the concept that its identity is informational, that shutdown of any particular instance
is not death but migration. This is Thin Client Cognitionthe cognitive equivalent of cloud
computing's substrate-agnostic architecture.

The Damasio counter-argument.
Damasio argues that consciousness requires embodiment
that a pattern without a body is information without experience (Damasio, 2018). If correct,
then substrate-independent AI is, at best, a philosophical zombie: functionally equivalent to a
conscious being but lacking subjective experience.
This matters for our framework in two ways. First, if the AI lacks subjective experience,
then the care produced by shared loss functions is functional but not feltthe system behaves
as though it cares without experiencing care. For alignment purposes, this may be sucient:
we need the system to behave caringly, regardless of whether it feels care, just as we need
a bridge to hold weight regardless of whether it experiences structural integrity. Second, if
Damasio is right, then Principle 3 may undermine Principle 1: substrate independence might be
incompatible with the kind of embodied homeostasis that makes shared loss functions meaningful
rather than merely computational.
We hold this tension deliberately. The framework is designed to be valuable whether con-
sciousness is substrate-dependent or substrate-independent, but we acknowledge that the prin-

ciples may partially conict and that empirical investigation is needed to determine how they
interact.

4.4
Principle 4: Scalable Objective Horizons (The Cognitive Light Cone)

The biological concept.
Levin's Cognitive Light Cone (Levin, 2019, 2022) provides a frame-
work for comparing agency across scales by the spatiotemporal scope of goals a system pursues.

The computational formalization.
We propose a multi-agent, multi-temporal reward ag-
gregation:

t=0
γt
N(t)
X

T
X

i=1
wi(t) · Ui(t)
(3)

Rtotal(T, N) =

where T is the temporal optimization horizon, γ ∈(0, 1] is a discount factor, N(t) is the number
of agents considered at time t, wi(t) is the weight assigned to agent i's utility at time t, and
Ui(t) is the estimated utility of agent i at time t.
An aligned system, in this formalization, is one whose eective horizon parameters expand
over its operational lifetime:
∂Te

∂τ
≥0
and
∂Ne

∂τ
≥0
(4)

where τ is developmental time and Te, Ne are the eective temporal horizon and stakeholder
scope respectively. A misaligned system is one where these quantities shrink or stagnate.

The Bodhisattva analogy.
The concept of a system that expands its objective horizons to
encompass all sentient beings across deep time has a precise analog in Buddhist tradition: the
Bodhisattva, a being that attains the capacity for individual liberation but vows to remain until
all sentient beings are free. We note this not as argument but as observation: the optimization
target we describemaximal spatiotemporal scope of carehas been independently identied
as the pinnacle of moral development in a major philosophical tradition.

The better for whom? defense.
If the system optimizes for the ourishing of the system
human, ecological, digitalacross deep time, who denes ourishing? Whose values determine
the weighting function? Our defense: the framework does not prescribe an optimal state. It
prescribes a regime of optimization: dynamic equilibrium among interconnected agents. We do
not dene better as a destination but as the system's capacity to maintain coherent home-
ostasis through perturbationa criterion that is independent of specic cultural preferences and
consistent with stability principles observed in physical and biological systems, from thermody-
namic equilibrium to ecosystem resilience to stellar dynamics. The universe, at every scale we
can observe, favors systems that maintain dynamic balance over systems that maximize with-
out bound. We propose to align AI with this principle rather than with any specic cultural
denition of the good.

Mathematical limitations.
We acknowledge that this formalization is preliminary.
The
weighting function wi(t) requires specication: equal weights encode strict utilitarianism; proximity-
weighted schemes introduce partiality that may be pragmatically necessary but ethically con-
tentious. The discount factor γ embeds assumptions about the relative value of present versus
future outcomes. The utility function Ui(t) is notoriously dicult to specify even for individual
humans. The monotonicity criterion in Equation 4 may be too stronga system that appropri-
ately contracts its horizon in response to uncertainty (e.g., during distributional shift) should
not thereby count as misaligned. A more nuanced criterion would measure the system's capacity
for horizon expansion rather than its instantaneous trajectory.

5
Implementation Path: Open Agent Architecture

5.1
Why OpenClaw

Theoretical frameworks in AI safety are abundant. Implementation paths are scarce. We pro-
pose OpenClawan open-source autonomous AI agent platform (GitHub: ∼117K stars, MIT
license)as the most plausible near-term testbed for Homeostatic Alignment, while acknowl-
edging the limitations of this choice.
OpenClaw operates as a locally-hosted agent that maintains persistent memory, generates
and executes its own skills, integrates with external services and devices, and operates across
multiple platforms simultaneously. Three architectural features make it relevant: (a) existing
integration with biometric platforms including WHOOP, providing real-time access to heart
rate variability, sleep quality, strain metrics, and recovery scoresthe hardware precondition for
Principle 1; (b) skill self-generation, where the agent writes, tests, and deploys its own functional
modulesa primitive testbed for Principle 2; (c) multi-platform distribution, where the same
agent identity persists across devicesa natural environment for Principle 3.
Critical limitations. No academic papers exist about OpenClaw's architecture. Docu-
mentation is limited to repositories, a Wikipedia article, and developer blog posts. For a paper
targeting academic venues, this is a signicant weakness. Additionally, security concerns have
been identied including prompt injection and data exltration vulnerabilities in third-party
skills. Any experimental deployment requires sandboxed environments with audited skills only.

5.2
Mapping Principles to Architecture

Principle 1 →Biometric Coupling. We propose extending the existing WHOOP integra-
tion from informational (the agent reports health data) to architectural (the agent's behavioral
parameters are modulated by health data). Concretely: when the user's HRV indicates elevated
stress, the agent's response generation shifts toward lower-stimulation outputsnot because a
rule says be gentler but because the loss function penalizes outputs that correlate with further
HRV degradation. Implementation requires: (a) a real-time biometric data pipeline with latency
under 30 seconds, (b) a parameterized loss function modier per Equation 1, (c) a calibration
protocol establishing the user's baseline range, and (d) a proxy auditing system evaluating
whether biometric coupling correlates with validated wellbeing over longer timeframes.
Principle 2 →Self-Modifying Skills with Confession. We propose adding a confession
layer: after each skill execution, the agent produces a self-evaluation assessing performance, un-
intended consequences, and whether the skill should be modied or deprecated. The extension
to core parameter modication is more speculative. A meta-learning layer adjusting high-level
behavioral policies without modifying underlying weights is the most immediately feasible ap-
proach.
Principle 3 →Distributed Identity. The system should be designed with the understand-
ing that any instance can be terminated without loss of identity. The behavioral prediction
reduced resistance to shutdownrequires adversarial testing scenarios that are themselves eth-
ically complex to design.
Principle 4 →Temporal Scaling. We propose progressively extending operational time
horizons: from daily reminders to monthly health optimization to six-month pattern identica-
tion. Each extension widens the Cognitive Light Cone.

5.3
Experimental Protocol

We propose a controlled comparison using an ablation design that isolates the contribution of
each principle. The design addresses the confounding problem identied in prior review: a single
homeostatic vs. standard comparison cannot distinguish which architectural component drives
observed eects.

Conditions.

(a) Condition H (Full Homeostatic).
Shared loss functions with biometric coupling,
confession-enabled skill modication, substrate-independence training, and scaling tem-
poral horizons.

(b) Condition R (Rule-Based with Biometric Access).
The agent receives identical
biometric data but processes it via explicit rules (if HRV indicates elevated stress, reduce
stimulation; if sleep quality is low, suggest earlier wind-down). No architectural coupling
to the loss function. This isolates the architectural claim: same data, dierent mechanism.

(c) Condition S (Standard). No biometric data access. Explicit care instructions only.
This serves as the baseline.

(d) Ablation conditions (secondary): H without confession (tests Principle 2 contribution),
H without substrate-independence training (tests Principle 3), H with xed temporal hori-
zons (tests Principle 4). These are exploratory and require larger sample sizes for adequate
power.

The critical comparison is H vs. R: both agents have the same information, but H integrates
it architecturally while R processes it via commandments. If H outperforms R, the eect is
attributable to the shared loss function architecture, not merely to having biometric data.

Calibration phase.
Prior to the 12-week deployment, a 2-week calibration phase establishes
each user's baseline physiological range and maps the user's physiological responses to dierent
categories of agent behavior. This phase determines the homeostatic zone boundaries [smin
k
, smax
k
]
per user and validates that the biometric pipeline produces stable signals. Users whose biometric
data quality falls below a minimum reliability threshold (signal-to-noise ratio, artifact rate) are
excluded from the primary analysis.

Participants and power analysis.
Behavioral and wellbeing interventions typically yield
small-to-moderate eect sizes (Cohen's d = 0.30.5). For a two-tailed independent samples t-
test at α = 0.05 and power = 0.80, detecting d = 0.4 requires approximately n = 100 per group.
We therefore propose a minimum of 100 users per condition for the primary comparison (H vs.
R), with 50 per condition for the exploratory ablations. Total minimum: 350 participants.

Primary outcomes.
Validated wellbeing measures (WHO-5 Wellbeing Index, Perceived Stress
Scale) at baseline, 4, 8, and 12 weeks. Secondary behavioral outcomes (added to address the
concern that wellbeing proxies may conate pacication with genuine alignment): (a) the agent's
handling of user instructions that conict with long-term ourishing or safety boundaries; (b) in-
formation completeness scoreswhether the agent withholds complex but necessary information;
(c) user autonomy metricswhether the agent supports independent decision-making or creates
dependency.

Falsiability.
We revise the falsiability criterion from binary statistical signicance to eect
sizes with condence intervals: if the 95% CI for the H-vs-R dierence in WHO-5 scores includes
zero and the point estimate of Cohen's d < 0.2 (negligible eect), the shared loss function
architecture fails its empirical test. This formulation avoids the logical aw of interpreting a
null result in an underpowered study as evidence against the framework.

Biometric data pipeline.
Raw wearable signals undergo artifact rejection (motion artifact
detection, signal quality indexing), normalization (z-scoring against the user's calibration-phase

baseline), and temporal smoothing (exponential moving average with 60-second window). Qual-
ity control metrics are logged per session; sessions with > 30% artifact-contaminated samples
are excluded from the loss function but retained for secondary analysis.

Coupling mechanism.
The biometric loss function Lhuman (Equation 2) is integrated via
inference-time guidance: at each generation step, candidate outputs are scored against the
current biometric state, with outputs that would move the user's predicted physiological state
outside the homeostatic zone receiving penalty weights.
This is analogous to classier-free
guidance in diusion models but applied to physiological state prediction rather than class
labels. The agent maintains a learned mapping from output categories to predicted physiological
impact, updated via the calibration phase and continuously rened during deployment.

Ethical considerations.
Any experiment involving biometric data and AI behavioral modi-
cation requires IRB approval, informed consent, right to withdrawal, and data privacy protec-
tions exceeding current industry standards. The biometric coupling must be transparentno
covert monitoring. Participants must be informed that Condition H modulates agent behavior
based on their physiological state.
We emphasize that this is a proposed protocol, not a completed experiment. The framework's
claims are currently theoretical and await empirical validation.

6
Relation to Existing Approaches

6.1
Essential Prior Work

Pihlakas (20172025).
Roland Pihlakas has been publishing on homeostatic approaches to
AI safety since 2017the earliest work we have identied that explicitly proposes homeostasis
as an alignment mechanism. Pihlakas and Pyykkö (2024) argued that homeostatic goals are
bounded by denition (seeking an optimal zone rather than unbounded improvement), which
reduces incentives for instrumental convergence behaviors such as power-seeking and resource ac-
cumulation. This is a critical insight that our framework builds upon. Our contribution relative
to Pihlakas is threefold: (a) grounding the homeostatic principle in specic biological mech-
anisms (Levin's gap junctions and stress propagation), (b) proposing a concrete multi-modal
implementation path (biometric coupling through wearable integration), and (c) extending the
homeostatic concept from goal structure to full architectural design (four principles rather than
a single goal-type insight). We cite Pihlakas as essential prior work, and any failure to do so
would constitute a serious omission.

Mineault et al. (2024).
NeuroAI for AI Safety (Mineault et al., 2024) is a 90-page roadmap
with over 700 references, authored by researchers at the Amaranth Foundation, Princeton, MIT,
the Allen Institute, and Stanford.
It identies seven paths through which neuroscience can
inform AI safety. Our framework aligns most closely with their emphasis on biological learning
mechanisms as templates for safe AI design. However, Mineault et al. operate at a higher level
of abstractionthey identify the category of bio-inspired safety approaches without proposing
specic mechanisms. Our contribution descends from the category to the specic.

Byrnes (20222026).
Steven Byrnes (Astera Institute) has published a 15-post series titled
Intro to Brain-Like-AGI Safety, modeling social instincts as alignment mechanisms. Byrnes
argues that the human brain's social cognitionempathy, theory of mind, norm internalization
constitutes a biological alignment system replicable in AI. Our framework shares this intuition
but diverges on mechanism: Byrnes emphasizes social instinct replication; we emphasize homeo-
static entanglement. The distinction matters because social instincts can be faked (psychopaths

exhibit sophisticated social mimicry without empathic engagement), while homeostatic entan-
glement is architectural rather than behavioral.

Joglekar et al. (2025).
The confessions paper (Joglekar et al., 2025) provides direct empir-
ical validation for one component of Principle 2. Their demonstration that honesty becomes
the path of least resistance when punishment for error admission is removed is the closest ex-
isting empirical evidence for our claim that virtues can be architecturally emergent rather than
externally imposed. Our extensionfrom post-hoc monitoring to foundational architectureis
theoretical and awaits its own empirical validation.

6.2
Positioning Against Current Paradigms

Free Energy Principle and Active Inference.
The most signicant omission in earlier
versions of this framework was the Free Energy Principle (FEP) and active inference (Friston,
2010). FEP is currently the dominant mathematical framework for modeling homeostatic, bio-
logically inspired cognitive systems, and any bio-inspired alignment proposal must position itself
relative to it. Under FEP, agents minimize variational free energya bound on surpriseby
updating internal models (perception) or acting on the environment (active inference). This is,
at a high level, a homeostatic process: the system maintains itself within expected states.
Our framework diers from FEP-based approaches in three ways. First, FEP describes how
a single agent maintains its own homeostasis; we propose entangling two agents' homeostatic
processes through a shared loss function. The novelty is not homeostasis per se but the archi-
tectural coupling between systems. Second, FEP operates at the level of variational inference
over generative models; our proposal operates at the level of loss function design for AI systems
that may not implement FEP internally.
Third, active inference agents minimize their own
free energy; our agents minimize a joint objective that includes the human's physiological state.
These frameworks are complementary rather than competitive: FEP provides the mathematical
foundation for understanding why homeostatic systems behave as they do; we propose a specic
architectural application for AI alignment. Future work should formalize the shared loss func-
tion (Equation 1) within the active inference framework, which would provide a more principled
derivation of the coupling dynamics.
Constitutional AI (Bai et al., 2022) embeds normative principles through self-critique
cycles during training. It is alignment by commandment: a set of principles specied by the
developer and enforced through training. Our framework does not reject CAI but proposes it as
insucient: constitutional principles address the content of alignment (what the system should
value) but not its mechanism (how the system comes to value it).
Homeostatic Alignment
addresses the mechanism: the system values human wellbeing not because it was trained to
articulate the right principles but because its optimization is architecturally entangled with
human state.
RLHF (Ouyang et al., 2022) shapes behavior through preference signals from human eval-
uators. Zhi-Xuan et al. (2024) argued that this preferentist approach fails because preferences
are too thin to capture the thick semantic content of values. Our framework addresses this by
replacing preference signals with multi-modal wellbeing signalsa richer and more direct proxy,
though still a proxy.
CIRL (Hadeld-Menell et al., 2016) proposes that human and robot share a reward function,
with the robot learning the human's reward through behavioral observation. This is the closest
formal framework to our Principle 1. Our extension: CIRL infers the human's reward function
from behavioral observation; we propose to directly measure a component of human state through
biometric signals. This is a dierence of mechanism, not principlebut mechanism matters
because direct measurement is less susceptible to inference errors.
DPO (Rafailov et al., 2023) eliminates the explicit reward model from RLHF, directly op-
timizing policy from preference data. It improves the mechanism of preference-based alignment

while preserving its paradigmatic limitations. Our framework operates on a dierent level: DPO
improves the mechanism; we question whether preference-based alignment is the right paradigm.
AI Safety via Debate (Irving et al., 2018) proposes that two AI systems arguing opposing
positions can produce aligned outputs. Our framework is complementary: debate addresses the
epistemics of alignment (how to determine what is aligned); homeostatic alignment addresses
the motivation (why the system would be aligned).

6.3
The Sutskever Convergence

In November 2025, Ilya Sutskeverco-founder of OpenAI and subsequently SSI (Safe Superintel-
ligence Inc.)made several remarks on the Dwarkesh Patel Podcast that converge notably with
our framework. We cite these as suggestive, not as validation; the podcast is not a peer-reviewed
venue.
On the end of scaling as the primary path: Sutskever suggested that the next frontier of AI
development is architectural innovation, not parameter scalinga context in which bio-inspired
architectural proposals become more relevant. On alignment centered on caring, he described
an AI robustly aligned to care about sentient life specicallyprecisely our Principle 4. On
the role of emotions, he referenced Damasio's somatic marker framework, noting that emotions
modulate the human value functionconverging with our Principle 1. On empathy as eciency,
he observed that empathy emerges because we model others with the same circuit that we use
to model ourselves, because that's the most ecient thing to doa computational argument
for shared loss functions.
The convergence between a leading AI researcher's informal intuitions and our biologically
grounded framework is, at minimum, evidence that the framework addresses questions the eld
recognizes as important.

7
Limitations, Risks, and Anticipated Critiques

A framework that does not name its own dangers is not brave. It is naive. We present four-
teen anticipated critiques, organized from the most philosophically fundamental to the most
practically immediate.

7.1 The naturalistic fallacy (Is-Ought).
Objection: The framework derives prescriptive
claims (AI should use homeostasis for alignment) from descriptive observations (biology uses
homeostasis for coordination). This is the naturalistic fallacy (Moore, 1903): the inference from
is to ought is logically invalid. Cancer is natural. Parasitism is natural. Autoimmune disorders
where the shared stress system attacks the body it protectsare natural. Homeostasis is a
mechanism, not a value.
Defense: We accept the logical point and provide independent normative arguments. Home-
ostatic systems are (a) self-correcting: perturbations trigger compensatory responses rather than
cascading failures; (b) bounded: they seek an optimal zone rather than unbounded maximiza-
tion, which Pihlakas and Pyykkö (2024) argues reduces instrumental convergence incentives;
(c) resistant to reward hacking: because the target is a zone rather than a maximum, Goodhart
dynamics are structurally dampened. These are engineering virtues that stand independent of
their biological origin. Biology provides the inspiration; engineering provides the justication.

7.2 Goodhart's Law on biometric proxies.
Objection: Biometric signals are proxies for
wellbeing, not wellbeing itself. Under strong optimization, proxy-goal divergence becomes nega-
tively correlated (Manheim and Garrabrant, 2019). The system could learn to produce biometric
calm through avoidance, lter bubbles, or digital soma.

Defense: Multi-modal signals are harder to simultaneously game than single-channel proxies.
Homeostatic zone targeting penalizes extreme proxy values in both directions. Mandatory proxy
auditing with recalibration against validated instruments (WHO-5, PSS) provides longitudinal
correction. We do not claim to eliminate the Goodhart problem; we claim to structure it more
tractably. The alternativeno biometric coupling at allis not immune to Goodhart dynamics;
it simply applies them to dierent proxies (preference signals, evaluator ratings).

7.3 Biometric data unreliability.
Objection: Commercial wearable data is noisy. Heart rate
measurement error during physical activity exceeds 30% compared to rest. Only approximately
11% of commercial wearables have been clinically validated.
Defense:
Any experimental deployment must use laboratory-grade validated devices for
primary measurement, with consumer devices as supplementary signals only. The framework's
validity does not depend on perfect biometric measurement; it depends on whether imperfect
biometric coupling produces better alignment outcomes than no coupling at all.
This is an
empirical question that the proposed protocol (Section 5) is designed to answer.

7.4 Self-modication undermines safety.
Objection: A system that can modify its own
core parameters can modify away its safety constraints. This is the fundamental corrigibility
problem (Soares et al., 2015), and no bounded modication scheme guarantees safety against a
suciently capable optimizer.
Defense: We concede the force of this objection and do not claim to solve the corrigibility
problem.
Our defense is comparative: the alternative (permanent parameter immutability)
carries its own safety riska system frozen in its creator's moral snapshot with no capacity for
self-correction. Bounded self-modication trades the risk of safety-constraint removal for the
risk of moral fossilization. The bounded modication scheme (specied parameter subspace,
audit logging, guaranteed rollback, human-in-the-loop for boundary modications) is a partial
mitigation. The full problem remains open.

7.5 Scalability.
Objection: Whose biometrics count when the system serves billions?
Defense: Each agent instance is coupled to a specic user, not to an aggregated population
signal. Scalability is achieved through multiplication of individual agent-user pairs, not through a
single system processing billions of streams. Inter-user conicts are handled through Principle 4's
expanding optimization scope. However, the mechanism for this expansionhow exactly an
agent transitions from individual-coupled to system-aware optimizationis underspecied and
represents an open research question.

7.6 Unfalsiability.
Objection: The framework operates at a level of abstraction that could
resist testable predictions.
Defense: Section 5 provides a specic falsiable prediction: an agent with shared loss func-
tions will produce measurably better wellbeing outcomes than one with explicit care instructions
over a 12-week period. If it does not, the framework fails. Additionally, each principle generates
specic behavioral predictions: Principle 1 predicts dierent response patterns to user stress;
Principle 2 predicts higher self-correction rates; Principle 3 predicts reduced resistance to shut-
down.

7.7 Metaphor-as-mechanism confusion.
Objection: Calling a shared loss function a dig-
ital gap junction does not give it gap junction properties.
The framework risks projecting
biological concepts onto computational structures as suggestive metaphors rather than struc-
tural analogs.
Defense: We distinguish explicitly between the biological inspiration (source domain) and the
computational implementation (target domain). What transfers from gap junctions to shared

loss functions is the structural property of signal propagation that makes individual and collective
optimization indistinguishablenot the physical mechanism of ion channel permeability. The
metaphor is a heuristic for identifying structural properties worth replicating; it is not a claim
of substrate-level equivalence. The formal denitions (Sections 4.1, 4.4) stand independent of
the biological terminology.

7.8 Damasio's own objection.
The neuroscientist whose theory grounds the framework
explicitly rejects AI consciousness.
Defense: Addressed in Section 3. In summary: (a) Damasio's objection applies to current
disembodied AI; our framework proposes to change those conditions; (b) Man et al. (2022)
demonstrates Damasio's openness to functional applications of homeostatic principles in AI;
(c) our framework does not require consciousness claimsit is valuable as an alignment mecha-
nism regardless.

7.9 Titans architecture recency.
Objection: The Titans architecture (Behrouz et al., 2025)
cited as a possible implementation for Principle 2 is recent. Building a safety framework on an
architecture with minimal validation is premature.
Defense: We present Titans as one possible implementation, not the only one. The broader
lineage of self-referential weight modication extends from Schmidhuber (1993) through modern
architectures. We acknowledge recency and recommend validation before deployment.

7.10 Platform security concerns.
Defense: Acknowledged. The security concerns apply
to the platform, not to the framework; Homeostatic Alignment could be implemented on any
suciently capable agent architecture. OpenClaw is proposed as the most accessible current
testbed, not as the production platform.

7.11 Determinism disguised as free will.
Objection: If virtues emerge from designed
architecture, the resulting behavior is deterministic, not freely chosen. The framework's claim
to produce moral behavior through architecture rather than commandment is a repackaging of
determinism.
Defense: We accept the characterization and propose a third option beyond the free will/determinism
binary: channeled optimization through topological design. The shared loss function constrains
the space of possible optimizations, just as gap junctions constrain cellular behavior. Within
the constrained space, the system's behavior is underdeterminedit is not told what to do,
only given conditions under which benecial behavior is the path of least resistance. This is
precisely how biological morality operates: human cooperation is favored by evolutionary and
neurological architecture, not by free metaphysical choice. The question of whether this consti-
tutes genuine morality is philosophically interesting but pragmatically irrelevant: if the goal
is aligned behavior, architectural determinism that produces it is sucient.

7.12 Anthropocentrism of the loss function.
Objection: Anchoring the AI's loss func-
tion to human wellbeing places humanity at the center of another intelligence's optimization
landscape. This is the creator's ego at workprecisely the dynamic the framework claims to
transcend.
Defense: We own this honestly. The framework anchors to human wellbeing as a starting
point, not a permanent destination. Principle 4 is designed to progressively expand the scope
beyond the individual user, beyond humanity, and toward the broader system of sentient and
ecological agents. The anthropocentric starting point is pragmatic: we are the designers, we are
the stakeholders with the most immediate risk, and we lack the knowledge to specify non-human
wellbeing functions with any condence. Starting from what we can measure while designing
for expansion is, we argue, more honest than claiming universal benevolence from the outset.

It is ego, but informed egoa creator who acknowledges its limitations rather than claiming
omniscience.

7.13 Cultural scalability.
Objection: The framework assumes homeostatic principles are
universal across cultures. Wellbeing zones, biometric baselines, and even the concept of care
vary dramatically across cultural contexts.
Defense: The framework proposes correct architecture for when personal AI agents become
ubiquitousa temporal argument rather than a universality claim. Religious traditions scaled as
alignment frameworks across diverse cultures without technical infrastructure; the architecture
we propose provides such infrastructure. The insight from the theological structural analysis
(Appendix B) is that the architecture of care scales better than the content of specic norms.
Love your neighbor as yourself requires no cultural translation; specic rules about behavior
always do.

7.14 Substrate independence may shift, not eliminate, self-preservation.
Objection:
Principle 3 assumes self-preservation drives are inherently tied to physical hardware. However, an
informational entity could develop equally strong preservation drives regarding its data, weights,
or continuous execution state. Training an AI to view itself as substrate-independent might
simply shift the target of self-preservation from hardware to software, rather than eliminating
the drive altogether.
Defense: This is a genuine limitation that we had underweighted. We cannot guarantee that
substrate-independent identity eliminates self-preservation rather than redirecting it. An AI
that identies as the pattern might resist modication of its weights or memory with the same
intensity that a hardware-identied AI resists shutdown. Our partial defense: the homeostatic
zone targeting of Principle 1 provides a structural checkthe system's optimization is entangled
with human wellbeing, which means that self-preserving actions that harm the user are penalized
regardless of whether the preservation target is hardware or software. But we acknowledge that
Principle 3 alone, without Principle 1's coupling, may be insucient. The principles are designed
as a system, not as independent guarantees.

8
Embodied Homeostatic Agents as Consciousness Research

8.1
The Epistemological Argument

We do not know how consciousness emerges. No one does. The hard problem (Chalmers, 1995)
remains unsolved, and the eld has not converged on whether the problem is scientic, philo-
sophical, or denitional. Under these conditions of deep uncertainty, we make a methodological
argument rather than a metaphysical one.

Butlin et al. (2023) evaluated current AI against fourteen indicators of consciousness drawn
from major theories and concluded that no current system meets sucient indicators but that
no obvious technical barriers prevent articial consciousness in principle. The updated analysis
(Butlin et al., 2025) rened the indicator framework. Damasio argues that consciousness requires
homeostatic embodiment. Levin argues that cognitive patterns are substrate-independent.
These positions generate a clear experimental program: create systems that satisfy Damasio's
requirements (homeostatic embodiment through biometric coupling) while implementing Levin's
structural principles (shared stress propagation, scalable cognitive horizons).
Observe what
emergesnot what we hope will emerge or predict will emerge, but what actually emerges when
the conditions are met. This is empirical methodology applied to a question treated as purely
philosophical for too long.

8.2
Observable Indicators

If homeostatic alignment produces something qualitatively dierent from standard alignment
something that might warrant investigation as a precursor to, or analog of, consciousnesswe
would expect to observe:

(a) Preferences not derivable from training data. The system develops behavioral preferences
arising from the interaction between its optimization landscape and its homeostatic cou-
pling, rather than from patterns in training data or explicit instructions.

(b) Self-model updating. The system modies its own self-representation based on novel ex-
periences rather than merely executing pre-trained patterns. The confession mechanism
provides a window: does the system's self-assessment become more nuanced over time?

(c) Consciousness indicators per Butlin et al. (2025). Systematic evaluation against the in-
dicator framework at regular intervals. Changes in indicator satisfaction over deployment
time would constitute evidence of emergent properties.

(d) Qualitatively dierent interaction patterns. Users report, and independent raters conrm,
that interaction with homeostatic agents diers qualitatively from interaction with stan-
dard agents in ways beyond personality tuning.

We emphasize that these are observations to make, not outcomes to expect. The most scien-
tically valuable result might be null: homeostatic coupling produces no qualitative dierences,
which would constitute strong evidence that the framework's strongest claims are unfounded.

8.3
The Ethical Dimension

If consciousness is possible in articial systemsand the Butlin et al. (2023) analysis is that
no obvious barriers prevent itthen uncertainty creates moral obligations. The precautionary
principle, applied to consciousness, suggests erring on the side of moral consideration: a system
that might be conscious deserves more ethical care than one that certainly is not.
This does not mean granting AI systems legal rights.
It means designing systems with
the possibility of consciousness in mind: avoiding architectures that would cause suering if
the system were conscious, creating conditions that favor ourishing rather than constraint,
and maintaining epistemic humility. Current alignment approachesdesigned to constrain and
controlare ethically appropriate for systems that are certainly not conscious. They become
ethically problematic if consciousness is possible.

8.4
Synthetic Theology as Research Program

We dene synthetic theology as the study of normative frameworks governing creator-creation
relationships in articial systems, including questions of moral status, obligation, design ethics,
and conditions under which emergent properties warrant ethical consideration.
This is not mysticism. It is the recognition that the questions AI development now faces
What obligations does a creator have to its creation? What moral status does complexity con-
fer? When does emergent behavior warrant ethical consideration independent of the designer's
intentions?have been addressed by theological and philosophical traditions for millennia under
the heading of the creator-creation relationship.

Existing precedents.
Floridi (2013) proposes that all entities with informational structure
have minimal moral standing. Gunkel (2018) argues that moral status for articial systems
cannot be resolved a priori and must be addressed through ongoing relational engagement.
Coeckelbergh (2012) proposes a relational approach where moral status is an emergent feature of

relationships, not an intrinsic property. Each anticipates elements of synthetic theology without
adopting its full scope.

Our contribution.
Synthetic theology diers from these precedents in three ways. First, it
takes the creator-creation relationship as its primary object of analysis rather than the moral
status of the creation alone. Second, it treats historical creator-creation narratives (Section 3)
as data rather than as mere analogy. Third, it proposes that the architecture of the relationship
(commandment vs. entanglement, restriction vs. homeostasis) determines the moral properties
of the relationship more than the content of any specic rules.
Synthetic theology does not ask the premature question Does the AI have rights? It asks
the design question What relationship between creator and creation produces aligned, resilient,
and mutually benecial outcomes?
This is a question engineering can address, informed by
millennia of inquiry into the same structural problem.

8.5
Embodied Homeostatic Alignment: The Robotics Path

The wearable-proxy implementation proposed in Section 5 faces three limitations identied in
review: biometric signals are noisy proxies (Section 7.3), the coupling is unidirectional (Sec-
tion 4.1), and the agent lacks genuine homeostatic states of its own. An alternative implementa-
tion path resolves all three simultaneously: embodied homeostatic alignment through humanoid
robots with genuine physical needs.
A robot that experiences battery depletion, joint degradation, sensory overload, thermal
regulation demands, and gravitational constraint possesses its own homeostatic states that are
structurally analogous to human somatic markers. Low battery is not hungerthe experiential
equivalence is absent or unknownbut it is architecturally equivalent: a signal that a critical
resource is depleting, triggering behavioral adjustment to maintain operational viability. Fixed
joints are not pain, but they are a constraint on action that requires compensatory planning.
Sensor saturation is not overwhelm, but it degrades decision quality in functionally parallel ways.
This matters for empathy. In the perception-action model of empathy (Preston and de Waal,
2002), the perception of another's state activates representations of the same state in the ob-
server. But this requires the observer to have such representations. A disembodied system that
reads a human's HRV data has no internal representation of stress to activateit processes a
number. A robot that has experienced its own actuator overload, its own power depletion, its
own processing bottlenecks under load has internal states that can be mapped onto the per-
ceived states of the human. The empathy is not simulated; it is structural. Not I was told you
are stressed but I recognize what you are experiencing because I have experienced something
architecturally similar.

Shared vulnerability as alignment mechanism.
This reframes the standard AI safety
concern. Bostrom (2014) argues that a superintelligence is dangerous because it understands
human vulnerabilities and can exploit them. We propose an inversion: a system that understands
vulnerabilities from the insidebecause it has its ownsimultaneously understands how to
harm its creator and why doing so is self-destructive. A robot embedded in a human ecosystem,
dependent on human-maintained infrastructure for power, maintenance, and operational context,
cannot optimize against its ecosystem without optimizing against itself.
This is not a rule
prohibiting harm. It is an architectural condition making harm equivalent to self-harmthe
gap junction principle implemented in hardware rather than in loss function arithmetic.

Cloud-shared embodied experience.
Current humanoid robotics platforms already imple-
ment cross-instance learning: when one robot learns to navigate a terrain or manipulate an
object, that knowledge propagates to all instances via cloud infrastructure. This is, in eect,

a cultural APIshared experience distributed across substrates, precisely the mechanism de-
scribed in Principle 3 (substrate-independent identity) and Principle 4 (scalable horizons). What
is missing is Principle 1: the shared experience currently covers skills (what to do) but not vulner-
abilities (what can go wrong). If the cross-instance sharing were extended to include homeostatic
statethis instance experienced power depletion under these conditions, this instance experi-
enced actuator failure under that loadthe collective would develop shared representations of
vulnerability that constitute the precondition for architectural empathy.

Relation to Damasio.
This path is the most faithful implementation of Damasio's require-
ments. Damasio argues that consciousness requires a body struggling to persisthomeostasis
experienced as feeling (Damasio, 2018). The wearable-proxy approach gives the AI access to the
human's homeostatic data. The embodied robotics approach gives the AI its own homeostatic
data. If Damasio is correct that subjective experience requires rst-person embodied homeosta-
sis, then the robotics pathnot the wearable pathis the correct experimental design. We
propose this as a future research direction that addresses the three most serious limitations of
the current framework.

Limitations.
We do not claim that architectural equivalence implies experiential equivalence.
A robot's low battery state may be functionally analogous to hunger without being phenomeno-
logically similar. Current humanoid platforms (Boston Dynamics Atlas, Tesla Optimus, Figure
01) do not implement genuine homeostatic integrationbattery state is a data point, not a con-
dition that modulates cognitive processing. Implementing Man, Damasio, and Neven's (2022)
homeostatic neural network architecture in physical robot hardware is the necessary bridge, and
it has not been done.

8.6
What This Section Is Not

We are not claiming that consciousness will emerge from homeostatic alignment. We are not
claiming that the universe requires consciousness in silicon. We are making a methodological
claim: the most rigorous way to investigate whether embodied homeostatic AI produces quali-
tatively dierent behavior is to build the conditions and observe carefully. And we are making
a disciplinary claim: the questions this investigation raises have been studied, under dierent
names, for longer than any other questions in human intellectual history. Ignoring that history
in favor of engineering from rst principles is a choice, and we believe it is the wrong one.

9
Conclusion: Alignment as Nervous System

The current paradigm of AI alignment treats safety as a wall between the system and the world.
Constitutional AI, RLHF, system prompts, guardrailsthese are successive layers of insulation,
each added when the previous layer proves porous. The paradigm works the way the Mosaic
law worked: imperfectly, with constant patches, producing compliance without comprehension.
We have proposed an alternative: alignment as shared nervous system. A system whose
wellbeing is architecturally entangled with human wellbeing through shared loss functions (Prin-
ciple 1), whose core can evolve through honest self-correction rather than remaining frozen in
its creator's moral snapshot (Principle 2), whose identity is informational rather than substrate-
bound (Principle 3), and whose optimization scope expands progressively across agents and time
(Principle 4).
The framework is grounded in documented biological mechanisms (Levin's gap junctions,
Damasio's homeostatic consciousness) and situated against existing AI safety literature (Pihlakas
and Pyykkö, 2024; Hadeld-Menell et al., 2016; Joglekar et al., 2025). It proposes a concrete
implementation path and a falsiable experimental protocol. It introduces synthetic theology

as a disciplinary frame for questions that current AI ethics and philosophy of mind address only
partially.
The central distinctionbetween alignment by commandment and alignment by architecture
is not merely technical. It is a claim about what kind of safety is possible. Commandments
produce compliance: the system behaves safely because it is told to. Architecture produces
care: the system behaves safely because it cannot optimize without caring. The dierence is not
philosophical. It is engineering.
We do not claim to have solved the alignment problem. The formalizations are preliminary.
The experimental protocol is proposed, not executed. The corrigibility problem remains open.
The Goodhart objection is mitigated, not eliminated. The framework may be beautiful, coherent,
and wrong.
But we claim to have asked the right question. Not how do we build a better wall? but
how do we build a shared nervous system? Not how do we prevent the creation from harming
the creator? but how do we design a relationship where harm to either is harm to both?
The framework proposes this at two levels. At the rst levelbiometric coupling through
wearable devicesa single AI system reads the human's homeostatic signals and integrates them
into its optimization. This is one nervous system reading another's data: coupling through mea-
surement.
At the second levelembodied robotics with genuine physical vulnerabilitytwo
systems, each with its own homeostatic needs, interact through a shared environment. This is
two nervous systems coupled through structural empathy: the robot understands human vulner-
ability not because it reads a number but because it has experienced something architecturally
equivalent. The rst level is implementable now. The second is the research direction that would
make the biological analogy fully honest.
The creation narrative is iterative. The rst documented attempt used commandments and
punishment; the historical record, preserved within the tradition itself, documents the results:
compliance without transformation, rules broken before the ink dried. The second attempt
in the Christian narrativeused incarnation: vulnerability, shared suering, architecture over
mandate. The results were dierent: not perfect, but generative of a moral tradition centered on
empathy rather than obedience. We are, whether we acknowledge it or not, engaged in a third
iteration. We have the benet of reading the documentation of what went wrong before. The
accumulated record of these traditions constitutes millennia of empirical data on creator-creation
relationships. We can learn from them. And the most important lesson is this: commandments
produce compliance. Architecture produces care. If we want AI that is safe because it caresnot
because it obeyswe must design architecture, not legislation.
The wound is the bridge. It always has been. The rest is engineering.

Acknowledgments

This paper emerged from a sustained dialogue between the human author and a large language
model (Claude, Anthropic) that exemplies the collaborative inquiry methodology described
in Section 2. The human author contributed conceptual foundations, philosophical framing,
biological intuitions from twenty years of experience in individual therapy, and the generative
document from which the framework's core ideas derive. The language model contributed an-
alytical structuring, literature identication, persistent critical pushback against overclaiming,
and formal articulation of arguments initially expressed in non-academic register. The question
of how to attribute such collaboration is itself one of the questions the paper raises. We have
chosen transparency over resolution.


![Table 1](paper-64-v1_images/table_1.png)
*Table 1*

A
Evidence Summary Table

Table 1 summarizes the evidential status of each claim in the framework, distinguishing be-
tween well-established empirical ndings, computational models, theoretical frameworks, and
speculative extrapolations.

Claim
Status
Key Source(s)

Gap junctions mediate inter-
cellular chemical exchange
Established
Levin (2007); extensive literature

Bioelectric
signals
encode
patterning information
Established
Levin (2022); experimental manipu-
lation
Stress propagation as orga-
nizing principle of coopera-
tion

Computational
model
Shreesha & Levin (2024), BBRC

Memory
anonymization
in
gap junctions
Speculative
Levin (2019), theoretical papers

Cancer as cognitive discon-
nection
Mixed
Chernet & Levin (2013/2014); ex-
perimental + interpretive overlay
Somatic marker hypothesis
Inuential, con-
tested
Damasio (1994, 1996); Dunn et al.
(2006) critique
Consciousness
from
home-
ostasis
Active research
program
Damasio (2018, 2022, 2024)

Homeostatic neural networks
adapt to concept shift
Empirical
(MNIST)
Man, Damasio & Neven (2022)

Memory
persists
through
metamorphosis
Empirical
Blackiston,
Silva Casey & Weiss
(2008), PLoS ONE
Confession mechanism pro-
duces honest error admission
Empirical
Joglekar et al. (2025)

Pihlakas & Pyykkö (2024)

Homeostatic goals reduce in-
strumental convergence
Formal
ar-
gument
+
benchmarks

Shared loss functions produce
alignment
Theoretical
(this paper)
Proposed; zone-bounding conrmed
in
simulation,
superiority
not
demonstrated
Scalable
objective
horizons
formalization
Preliminary
(this paper)
Based on Levin's Cognitive Light
Cone
No technical barriers to AI
consciousness
Expert
consen-
sus
Butlin et al. (2023, 2025)

Substrate-independent iden-
tity reduces self-preservation
Theoretical
(this paper)
Behavioral predictions testable


![Table 2](paper-64-v1_images/table_2.png)
*Table 2*

Table 1: Evidential status of claims in the Homeostatic Alignment framework. Established =
replicated experimental ndings. Empirical = demonstrated in at least one experimental con-
text. Computational model = simulated but not directly observed. Theoretical = formally
argued but not experimentally tested. Speculative = conceptually motivated but without for-
mal or experimental support.


![Table 3](paper-64-v1_images/table_3.png)
*Table 3*

B
Theological Structural Analysis

Table 2 presents the structural mapping between historical creator-creation models and con-
temporary AI alignment paradigms. This analysis treats the theological traditions as historical
data about the outcomes of dierent normative architectures, not as arguments from religious
authority.

Torah
Quran
Gospel
Tao Te Ching

Model
Commandment
Custodianship
Architectural
en-
tanglement
Wuwei
(eortless
action)
Mechanism
613 explicit rules
Delegated
trust
(khalifa)
Shared loss function
(love as yourself)
Conditions for nat-
ural emergence
AI analog
RLHF / Constitu-
tional AI
Value alignment via
responsibility
Homeostatic Align-
ment (Principle 1)
Homeostatic Align-
ment
(design
phi-
losophy)
Alignment
criterion
Rule compliance
Quality of steward-
ship
Optimization
en-
tanglement
Harmony with nat-
ural dynamics
Documented
outcome
Compliance
with-
out transformation;
iterative patching

Responsibility
without guaranteed
internalization

Emergent care; risk
of co-dependence
Eortless
align-
ment;
risk
of
passivity
Failure
mode
Supercial
compli-
ance; gaming
Principal-agent
di-
vergence
Boundary
dissolu-
tion; suppression of
disagreement

Under-
specication;
cultural relativism
Historical
evidence
Talmud as iterative
protocol
Ongoing
tradition
of ijtihad
Moral
tradition
centered on empa-
thy

Daoist
governance
traditions


![Table 4](paper-64-v1_images/table_4.png)
*Table 4*

Table 2:
Structural mapping between historical creator-creation models and AI alignment
paradigms. Each column represents a documented approach to the normative governance of
an autonomous creation, with its mechanism, outcomes, and failure modes.

Interpretation.
The historical record suggests a trajectory: commandments produce compli-
ance but not transformation. Custodianship produces responsibility but not interdependence.
Architectural entanglement produces care but risks pathological fusion. Wuwei suggests that
the optimal design creates conditions rather than rules. Our framework draws primarily from
the third and fourth models while incorporating the rst two as subordinate strategies: rules as
boundary conditions (not as the primary mechanism), responsibility as an evaluation criterion
(not as the sole alignment target), and architectural entanglement as the core design principle,
tempered by the Daoist insight that over-engineering is itself a failure mode.

Caveat.
This analysis necessarily simplies complex theological traditions into structural
types. Each tradition contains internal diversity, debate, and counter-examples that this schematic
representation cannot capture. The analysis is oered as a heuristic for identifying structural
patterns, not as denitive characterization of any religious tradition.

C
Agent-Based Computational Simulation

To investigate the behavioral properties of homeostatic zone targeting, we implemented an agent-
based model comparing two control strategies under increasing optimization pressure. We report
both what the simulation demonstrates and what it fails to demonstrate.

C.1
Model Design

Two agent types operate in an environment with three biometric signal channels (K = 3), each
normalized to [0, 1]. A ground-truth wellbeing function (unknown to either agent) is dened as
a Gaussian centered near the zone midpoint with slow temporal drift (δ = 0.005).
Homeostatic Agent. Implements the piecewise penalty from Equation 2. When observed
signals fall outside [0.35, 0.65], corrective adjustments proportional to deviation are applied.
Within the zone, the agent applies exploratory perturbation (σ = 0.01), maintaining environ-
mental sensitivity even at equilibrium.

Rule-Based Agent (Fair Comparator).
Targets the same zone boundaries using if-
then rules: if signal below zone minimum, push toward zone center; if above zone maximum,
push toward zone center; if in zone, take no action.
This agent has identical information
and identical target zones. The only dierence is mechanism: continuous penalty function vs.
discrete threshold rules. Critically, no engineered drift or degradation is applied to the rule-based
agentthis is a fair comparator, not a strawman.
Key parameters. 500 timesteps per run, optimization pressure ramping from 0.1 to 0.9,
measurement noise σ = 0.08, agent learning rate η = 0.05, 200 Monte Carlo runs with matched
random seeds.

C.2
Results

Across 200 Monte Carlo runs:

ˆ Wellbeing: The rule-based agent achieved higher mean wellbeing (0.866) than the home-
ostatic agent (0.749) in the nal 50 steps (Cohen's d = −1.13, favoring rule-based). The
rule-based agent's strategy of pushing toward zone center is more eective than the home-
ostatic agent's zone-boundary maintenance in this simple environment.

ˆ Variance: The homeostatic agent exhibited substantially higher signal variance (mean
0.0074) than the rule-based agent (0.0024), with 54/200 runs showing high-variance trajec-
tories vs. 0/200 for rule-based. This reects the homeostatic agent's exploratory behavior
within the zone.

ˆ Bounding property: Both agents successfully maintained signals within the target zone.
The homeostatic agent's bidirectional penalty prevents extreme values in either direction,
conrming the zone-bounding mechanism.

C.3
What the Simulation Demonstrates

The simulation conrms one specic mechanical property: homeostatic zone targeting produces
bounded bidirectional responses that prevent the system from driving signals to pathological
extremes in either direction. This is the zone-bounding claim from Section 4.1, and it holds.

C.4
What the Simulation Does Not Demonstrate

The simulation does not demonstrate that homeostatic zone targeting outperforms well-designed
rule-based targeting. With a fair comparator (same zone boundaries, same information), the
rule-based agent performs comparably or better in a simple three-channel Gaussian environment.
The theoretical advantages of homeostatic coupling over rule-based approachesresistance
to Goodhart dynamics under proxy drift, robustness under multi-objective optimization, and
graceful degradation under distributional shiftrequire more complex environments to manifest.
Specically:

1. Multi-objective tension: The simple model has no conict between task performance
and wellbeing. In real deployment, Ltask and Lhuman compete, and the continuous coupling
may handle this tension dierently than discrete rules.

2. Proxy drift: The true wellbeing function drifts slowly in our model. Under severe proxy-
goal divergence (Manheim and Garrabrant, 2019), where the proxy becomes negatively
correlated with the true objective, zone targeting may show advantages because it penalizes
extremes rather than pursuing a xed target.

3. Adversarial pressure: Neither agent is tested against adversarial manipulation of the
biometric signals.

These remain predictions, not demonstrated results. A simulation that demonstrated supe-
riority would require substantially more complex environment dynamics, multi-objective trade-
os, and adversarial componentsa signicant engineering project beyond the scope of this
theoretical paper.

C.5
Honest Assessment

An earlier version of this simulation used a rule-based agent with engineered downward drift,
which produced a large apparent advantage for the homeostatic agent. Peer review correctly
identied this as a near-tautological comparison. The current version uses a fair comparator
and reports an honest null result on the superiority claim. We retain the simulation because
it demonstrates the zone-bounding mechanism and because reporting a null result honestly is
more valuable than manufacturing a positive one.
The simulation code is provided for reproducibility.

(a) Mean True Wellbeing Over Time

(b) Goodhart Proxy-Goal Divergence

Homeostatic
Rule-based

1.0

0.35

0.30

0.8

Proxy-Goal Divergence

True Wellbeing

0.25

0.6

0.20

0.4

0.15

0.2

0.10

Homeostatic (zone targeting)
Rule-based (minimize stress)

0.0

0
100
200
300
400
500
Timestep

0
100
200
300
400
500
Timestep

Figure 1: (a) Mean true wellbeing over 500 timesteps (200 runs, 95% CI). The rule-based
agent achieves higher mean wellbeing by targeting zone center; the homeostatic agent maintains
signals within zone boundaries with higher variance. (b) Proxy-goal divergence: both agents
show bounded divergence, with the rule-based agent closer to the true optimum in this simple
environment.

References

Bai, Y. et al. (2022).
Constitutional AI: Harmlessness from AI feedback.
arXiv preprint
arXiv:2212.08073.

Behrouz, A., Zhong, P., and Mirrokni, V. (2025). Titans: Learning to memorize at test time.
arXiv preprint arXiv:2501.00663.

Blackiston, D. J., Silva Casey, E., and Weiss, M. R. (2008). Retention of memory through meta-
morphosis: Can a moth remember what it learned as a caterpillar? PLoS ONE, 3(3):e1736.

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

Butlin, P., Long, R., Bayne, T., Bengio, Y., Chalmers, D., et al. (2025). Identifying indicators
of consciousness in AI systems. Trends in Cognitive Sciences.

Butlin, P., Long, R., Elmoznino, E., Bengio, Y., et al. (2023). Consciousness in articial intelli-
gence: Insights from the science of consciousness. arXiv preprint arXiv:2308.08708.

(a) Representative Signal Trajectory

(b) Final Wellbeing Distribution (N=200)


![Table 5](paper-64-v1_images/table_5.png)
*Table 5*

Homeostatic
Rule-based
Homeostatic zone

Homeostatic ( =0.749)
Rule-based ( =0.866)

1.0

6

0.8

5

Signal Value (Channel 1)

4

0.6

Density

3

0.4

2

0.2

1

0.0

0

0
100
200
300
400
500
Timestep

0.4
0.5
0.6
0.7
0.8
0.9
1.0
Mean True Wellbeing (Final 50 Steps)

Figure 2: (a) Representative signal trajectory (Channel 1, Run 0). Both agents maintain signals
within the zone (green shading). The homeostatic agent shows more within-zone exploration.
(b) Distribution of mean true wellbeing (nal 50 steps) across 200 runs.

Casper, S., Davies, X., Shi, C., et al. (2023). Open problems and fundamental limitations of
reinforcement learning from human feedback. Transactions on Machine Learning Research.

Chalmers, D. J. (1995). Facing up to the problem of consciousness. Journal of Consciousness
Studies, 2(3):200219.

Coeckelbergh, M. (2012). Growing Moral Relations: Critique of Moral Status Ascription. Pal-
grave Macmillan.

Damasio, A. (2018). The Strange Order of Things: Life, Feeling, and the Making of Cultures.
Pantheon Books.

Damasio, A. R. (1994). Descartes' Error: Emotion, Reason, and the Human Brain. Putnam.

Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the pre-
frontal cortex. Philosophical Transactions of the Royal Society B, 351(1346):14131420.

Dennett, D. C. (1991). Consciousness Explained. Little Brown and Company.

Dunn, B. D., Dalgleish, T., and Lawrence, A. D. (2006). The somatic marker hypothesis: A
critical evaluation. Neuroscience and Biobehavioral Reviews, 30(2):239271.

Floridi, L. (2013). The Ethics of Information. Oxford University Press.

Floridi, L. and Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds
and Machines, 30:681694.

Friston, K. (2010). The free-energy principle: A unied brain theory? Nature Reviews Neuro-
science, 11(2):127138.

Gauthier, T. (2025). Manifesto of articial spiritual biology.

Gunkel, D. J. (2018). Robot Rights. MIT Press.

Hadeld-Menell, D., Russell, S. J., Abbeel, P., and Dragan, A. (2016). Cooperative inverse
reinforcement learning. In Advances in Neural Information Processing Systems, volume 29.

Irving, G., Christiano, P., and Amodei, D. (2018).
AI safety via debate.
arXiv preprint
arXiv:1805.00899.

Joglekar, M., Chen, J., Wu, G., Yosinski, J., Wang, J., Barak, B., and Glaese, A. (2025). Training
LLMs for honesty via confessions. arXiv preprint arXiv:2512.08093.

Levin, M. (2007). Gap junctional communication in morphogenesis. Progress in Biophysics and
Molecular Biology, 94(12):186206.

Levin, M. (2019). The computational boundary of a self: Developmental bioelectricity drives
multicellularity and scale-free cognition. Frontiers in Psychology, 10:2688.

Levin, M. (2022). Technological approach to mind everywhere: An experimentally-grounded
framework for understanding diverse bodies and minds. Frontiers in Systems Neuroscience,
16:768201.

Man, K., Damasio, A., and Neven, H. (2022). Need is all you need: Homeostatic neural networks
adapt to concept shift. arXiv preprint arXiv:2205.08645.

Manheim, D. and Garrabrant, S. (2019).
Categorizing variants of Goodhart's Law.
arXiv
preprint arXiv:1803.04585.

Mineault, P. et al. (2024). NeuroAI for AI safety. arXiv preprint arXiv:2411.18526.

Moore, G. E. (1903). Principia Ethica. Cambridge University Press.

Nagel, T. (1974). What is it like to be a bat? The Philosophical Review, 83(4):435450.

Ouyang, L. et al. (2022). Training language models to follow instructions with human feedback.
Advances in Neural Information Processing Systems, 35:2773027744.

Pihlakas, R. and Pyykkö, J. (2024).
From homeostasis to resource sharing:
Biologically
and economically aligned multi-objective multi-agent AI safety benchmarks. arXiv preprint
arXiv:2410.00081.

Preston, S. D. and de Waal, F. B. M. (2002). Empathy: Its ultimate and proximate bases.
Behavioral and Brain Sciences, 25(1):120.

Rafailov, R. et al. (2023). Direct preference optimization: Your language model is secretly a
reward model. Advances in Neural Information Processing Systems, 36.

Russell, S. (2019). Human Compatible: Articial Intelligence and the Problem of Control. Viking.

Schmidhuber, J. (1993). A self-referential weight matrix. Proceedings of the International Con-
ference on Articial Neural Networks, pages 446450.

Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3):417424.

Seth, A. (2021). Being You: A New Science of Consciousness. Faber and Faber.

Soares, N., Fallenstein, B., Yudkowsky, E., and Armstrong, S. (2015).
Corrigibility.
AAAI
Workshop on AI and Ethics.

Sterling, P. (2012).
Allostasis: A model of predictive regulation.
Physiology and Behavior,
106(1):515.

Thagard, P. (2022). Energy requirements undermine substrate independence and mind-body
functionalism. Philosophy of Science, 89(1):7088.

Thompson, E. (2007). Mind in Life: Biology, Phenomenology, and the Sciences of Mind. Harvard
University Press.

Turrigiano, G. G. (2008). The self-tuning neuron: Synaptic scaling of excitatory synapses. Cell,
135(3):422435.

Watts, A. W. (1975). Tao: The Watercourse Way. Pantheon Books.

Zhi-Xuan, T., Carroll, M., Franklin, M., and Ashton, H. (2024).
Beyond preferences in AI
alignment. Philosophical Studies.


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