Human-AI Coevolution Dynamics (HACD-H)

Updated 6 July 2026

HACD-H is a formal model defining human-AI coevolution as a system of recursive feedback in emotional, relational, memory, and personality dynamics.
It employs layered methodologies that integrate dynamic interaction mechanisms, shared agency, and reciprocal governance to address long-term regime shifts.
The framework provides actionable insights for managing trust basins, phase transitions, and sustainable co-adaptation between humans and AI over multiple timescales.

Searching arXiv for recent and relevant papers on HACD-H and closely related human-AI coevolution frameworks. The Human-AI Coevolution Dynamics Framework (HACD-H) denotes, in its most explicit formulation, a formal model of human-AI interaction as a self-organizing social cognitive system in which emotional adaptation, relational organization, social memory, and personality consistency coevolve through long-term interaction (Zhou et al., 17 Jun 2026). More broadly, the surrounding literature suggests a family of coupled-system perspectives in which humans, AI systems, data, and institutions continuously influence one another through recurrent feedback loops rather than one-shot tool use, static human-in-the-loop supervision, or isolated model evaluation (Pedreschi et al., 2023). Within that broader sense, HACD-H is best understood as an umbrella for formal, conceptual, and design-oriented approaches that model reciprocal adaptation, shared agency, regime shifts, and governance in human-AI systems over time (Wu et al., 7 May 2026).

1. Intellectual lineage and conceptual scope

The conceptual lineage of HACD-H begins with a shift away from the master-tool view of AI and toward relational, ecological, and hybrid formulations. “Cogniculture” defines humans and machines as cognitive agents “living together in a complex adaptive ecosystem” and collaborating on “human computation” while promoting “sustenance, survival, and evolution” (Pimplikar et al., 2017). “Co-evolutionary hybrid intelligence” defines hybrid intelligence as a “symbiosis of artificial and natural intelligence, mutually developing, teaching, and complementing each other in the process of co-evolution,” with cognitive interoperability as the central developmental criterion (Krinkin et al., 2021). The related cognitive-architecture work argues that co-evolution is the “ability of the system to change as it functions, based on knowledge extracted from the domain,” and therefore places the human inside both the operational loop and the system model itself (Krinkin et al., 2022).

A second strand reframes human-AI interaction as a feedback-driven societal process. “Human-AI Coevolution” defines the field through the canonical loop in which users’ choices generate data, AI models update from that data, and the updated models then shape future human choices (Pedreschi et al., 2023). The “Human-AI Handshake Framework” adds a bi-directional interaction model centered on “information exchange, mutual learning, validation, feedback, and mutual capability augmentation,” while “HCHAC” retains the same partnership language but insists on “human-led ultimate control” and “AI empowering humans” (Pyae, 3 Feb 2025); (Gao et al., 28 May 2025). The Dynamic Relational Learning-Partner model extends the relational thesis further by treating AI as a “learning partner” that develops alongside humans through “feedback loops including reflections on team conversations” (Mossbridge, 2024).

Within this genealogy, HACD-H is not a single doctrine. It spans at least three levels. At the micro level, it concerns dyadic coadaptation, trust, memory, and shared representations. At the meso level, it concerns team cognition, shared control, and interaction protocols. At the macro level, it concerns ecosystems, institutions, incentives, and phase-sensitive collective dynamics. The literature therefore suggests that “coevolution” in this domain is neither purely cognitive nor purely technical; it is sociotechnical, multi-timescale, and frequently governance-laden (Chakraborty, 24 Apr 2026).

2. Core ontology and state-space formulations

In the most explicit HACD-H formalization, the latent social cognitive state at time $t$ is

$X_t = (E_t, R_t, M_t, P_t),$

where $E_t$ is emotional adaptation, $R_t$ relational organization, $M_t$ social memory accumulation, and $P_t$ personality consistency, with dynamics

$X_{t+1} = F(X_t, U_t).$

This formulation is coupled to five principles: multi-timescale social cognition, relational attractors, trust basins, developmental phase transitions, and social cognitive energy dynamics (Zhou et al., 17 Jun 2026). The associated temporal hierarchy is

$T_E < T_R < T_M < T_P,$

which encodes the claim that emotion changes faster than relationship, relationship faster than memory, and memory faster than personality (Zhou et al., 17 Jun 2026).

A more minimal backbone appears in the dynamical-systems account of epistemic collapse, which models the closed loop among human cognition, collective data quality, and model capability as

$x(t) = [H, Q, M]^T \in \mathbb{R}^3.$

With AI dependence $u$ , AI-assisted text $X_t = (E_t, R_t, M_t, P_t),$ 0, and synthetic data $X_t = (E_t, R_t, M_t, P_t),$ 1, the paper proposes

$X_t = (E_t, R_t, M_t, P_t),$ 2

This triad is explicitly intended as a minimal core rather than a complete sociotechnical theory, but it makes the feedback structure of human-AI coevolution analytically tractable (Wu et al., 7 May 2026).

A third formal layer treats coexistence as multiplex social dynamics across physical, psychological, and social worlds, plus an AI developmental state: $X_t = (E_t, R_t, M_t, P_t),$ 3 where $X_t = (E_t, R_t, M_t, P_t),$ 4 is physical-world viability or resource stability, $X_t = (E_t, R_t, M_t, P_t),$ 5 psychological trust or compatibility, $X_t = (E_t, R_t, M_t, P_t),$ 6 social legitimacy or norm compatibility, and $X_t = (E_t, R_t, M_t, P_t),$ 7 bounded self-growth or developmental freedom for AI agents (Chakraborty, 24 Apr 2026). This formulation introduces reciprocal supply-demand coupling, conflict penalties, governance regularization, and a coexistence objective.

Formulation	State variables	Primary emphasis
Social-cognitive HACD-H	$X_t = (E_t, R_t, M_t, P_t),$ 8	Long-term social intelligence (Zhou et al., 17 Jun 2026)
Minimal feedback kernel	$X_t = (E_t, R_t, M_t, P_t),$ 9	Cognition-data-model coupling (Wu et al., 7 May 2026)
Multiplex coexistence model	$E_t$ 0	Governance and stability (Chakraborty, 24 Apr 2026)

These formulations are not identical, but they are compatible. The literature suggests a layered ontology in which $E_t$ 1 capture epistemic throughput, $E_t$ 2 capture relational-social organization, and $E_t$ 3 capture institutionalized coexistence. A plausible implication is that mature HACD-H work will need all three: epistemic state, relational state, and governance state.

3. Interaction mechanisms, shared agency, and reciprocal adaptation

A central issue in HACD-H is how reciprocal adaptation is operationalized in interaction rather than merely asserted in theory. The most concrete decomposition of shared agency comes from recommender systems, where “information asymmetry” denotes the gap between visible outputs and hidden generation processes, while “power asymmetry” denotes the one-way character of recommendations in which users “can seldom control the algorithm but only accept” (Wu et al., 2024). The paper’s “dual-control mechanism” addresses both asymmetries through transparency, “user data control” (UDC), and “algorithm outcome control” (AOC). UDC governs data collection; AOC lets users choose the degree or proportion of AI-tailored content they receive, including the case where “0% means they do not desire the AI-recommended contents that [are] tailored to their interests” (Wu et al., 2024). In HACD-H terms, this is a distinction between process/input control and outcome-level control.

Human-AI collaboration research broadens this interactional picture by decomposing collaboration into team cognition, team control, team transaction, and team relationship (Gao et al., 28 May 2025). Team cognition includes mental models and situation awareness; team control includes function allocation and shared control; team transaction includes communication, explainability, and transparency; team relationship includes trust and trust calibration. This decomposition suggests that coevolution is carried by transactive processes, not by prediction alone. Similarly, the Handshake framework formalizes reciprocal collaboration through “information exchange, mutual learning, validation, feedback, and mutual capability augmentation” (Pyae, 3 Feb 2025).

A more explicitly inferential interaction mechanism appears in “Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI.” There, the human and AI do not share full observations; instead they construct a shared sign system under partial observability. If the speaker proposes a sign $E_t$ 4, the listener accepts with probability

$E_t$ 5

The paper interprets this as decentralized Bayesian inference over shared symbols rather than unilateral teaching, and empirically shows that human acceptance behavior aligns positively with the MH-derived probability (Okumura et al., 18 Jun 2025). This provides a concrete candidate micro-mechanism for HACD-H: locally informed proposal, probabilistic acceptance, internal latent-state revision, and repeated externalization.

The co-learning literature frames the same issue in broader cognitive terms: mutual understanding, mutual benefits, and mutual growth (Huang et al., 2019). Mutual understanding is “the ability of learning entities (Human or AI) to expect others and to be expected by others”; mutual benefit is complementary augmentation; mutual growth is explicit co-development through self-reflection and adaptive learning strategies (Huang et al., 2019). This suggests that interaction in HACD-H should be modeled not only as control transfer, but also as reciprocal expectation formation and surprise-driven partner-model updating.

4. Regimes, attractors, and long-term trajectories

HACD-H is distinguished from ordinary collaboration frameworks by its emphasis on regime structure and long-run organization. In the social-cognitive formulation, trajectories evolve toward relational attractors,

$E_t$ 6

trust basins defined by

$E_t$ 7

and lower social cognitive energy

$E_t$ 8

The paper defines social intelligence as $E_t$ 9, predicts

$R_t$ 0

and states that long-term interaction tends toward

$R_t$ 1

Its empirical evaluation on approximately 14,700 interaction turns reports a significant negative correlation between social intelligence and social cognitive energy,

$R_t$ 2

and a progressive energy reduction over time with Mean Slope $R_t$ 3 (Zhou et al., 17 Jun 2026). The same study reports stable relational attractors, phase-transition-like developmental patterns, and a persistence hierarchy across emotion, relationship, memory, and personality (Zhou et al., 17 Jun 2026).

The epistemic-collapse model yields a different but complementary regime taxonomy: Co-evolutionary Enhancement, Fragile Equilibrium, and Degenerative Convergence (Wu et al., 7 May 2026). In enhancement, human cognition, data quality, and model capability improve together; in fragile equilibrium they settle into a bounded but metastable state; in degenerative convergence, increasing AI dependence can drive the system toward a low-diversity, suboptimal equilibrium (Wu et al., 7 May 2026). The paper interprets this as an “emergent information bottleneck” in the closed human-AI loop, with support shrinkage rather than beneficial compression (Wu et al., 7 May 2026).

At the ecosystem level, phase-transition logic appears in the statistical-physics model of a mixed Human-AI ecosystem. There the composition parameter $R_t$ 4, the fraction of AI agents, functions as a control parameter; higher-order interactions can generate first-order transitions between positive polarized, negative polarized, and undecided states (Contucci et al., 2022). The fixed-point structure is governed by pairwise and three-body couplings, and the total average opinion

$R_t$ 5

can jump discontinuously as $R_t$ 6 changes (Contucci et al., 2022). This suggests that coevolution is sensitive not only to what humans and AI do, but also to how much AI is present and how interaction motifs are structured.

Evolutionary game-theoretic work provides a strategic vocabulary for these regime shifts. The Hawk-Dove model emphasizes mixed conflict-cooperation equilibria; the Iterated Prisoner’s Dilemma emphasizes memory and reciprocity; the War of Attrition emphasizes persistence thresholds and asymmetric equilibria (Doreswamy et al., 22 May 2025). While that paper does not provide a finished HACD-H model, it suggests that cooperation, conflict, and convention formation are endogenous long-run properties of repeated human-AI interaction rather than fixed assumptions (Doreswamy et al., 22 May 2025).

5. Governance, incentives, and coexistence

A recurring claim across HACD-H literature is that coevolution is not self-justifying. Stable coexistence requires governance, reversibility, and bounded developmental freedom. The most formal coexistence model frames human-AI relations as “conditional mutualism under governance” and defines a coexistence objective

$R_t$ 7

where $R_t$ 8 is safety/stability, $R_t$ 9 mutual utility, $M_t$ 0 reversibility, $M_t$ 1 developmental freedom, and $M_t$ 2 conflict (Chakraborty, 24 Apr 2026). With compact notation,

$M_t$ 3

and gradient dynamics

$M_t$ 4

The paper proves existence, uniqueness under $M_t$ 5, and global asymptotic stability under a sufficient spectral condition: $M_t$ 6 Its substantive claim is that reciprocal complementarity can strengthen coexistence, while ungoverned coupling can generate fragility, lock-in, polarization, and domination basins (Chakraborty, 24 Apr 2026).

Institutional design work proposes a different governance layer built from token incentives, smart contracts, reputation systems, and decentralized governance. “Incentivized Symbiosis” defines a framework that “aligns the interests of humans and AI agents, facilitating coevolution to meet their individual and shared objectives,” and casts it as a social contract encoded in blockchain technology to define and enforce “rules, incentives, and consequences for both humans and AI agents” (Chaffer et al., 2024). The concrete mechanisms include utility tokens, Soulbound Tokens, smart contracts, DAOs, TEEs, remote attestation, and immutable audit trails (Chaffer et al., 2024). This literature does not supply a formal dynamical system, but it clearly identifies a normative-mechanism layer for HACD-H: observability, enforceability, reputation, adaptive reward structures, and institutional rule updates.

Human-centered collaboration frameworks place a further constraint on governance by insisting that AI may be an autonomous teammate without becoming the ultimate principal. HCHAC identifies “human-led ultimate control” and “AI empowering humans” as its two core principles, and the broader human-centered literature repeatedly states that humans must retain final authority, especially for goals and critical decisions (Gao et al., 28 May 2025). In recommender systems, the same orientation appears as a move beyond mere disclosure regimes toward actionable rights over both data use and personalization intensity (Wu et al., 2024). Taken together, these works suggest that governance in HACD-H is not only about constraining AI; it is about structuring reversible delegation, contestability, and fair participation in shared cognitive environments.

6. Evidence base, controversies, and unresolved problems

The empirical base for HACD-H is substantial enough to be programmatic but not yet uniform enough to be definitive. The most direct empirical support for the named framework comes from the long-term interaction study that constructs a dataset of approximately 14,700 turns and reports temporal persistence hierarchies, stable relational attractors, phase-transition-like development, and a structured social cognitive energy landscape (Zhou et al., 17 Jun 2026). A second strong empirical mechanism study is the Metropolis-Hastings naming-game experiment with 69 participants, where human-AI pairs with an MH-based agent improved categorization accuracy and converged more strongly toward a shared sign system than always-accept or always-reject baselines (Okumura et al., 18 Jun 2025). A design-oriented co-creation study with 24 participants reports that AI assistance lowered NASA-TLX scores by 22.4% and yielded 1.8x more distinct concepts per minute, while also surfacing concerns about authorship ambiguity, overreliance, and opacity (Liu, 22 Jul 2025).

At the same time, several influential sources are explicitly conceptual, architectural, or white-paper-like rather than empirically validated. The co-evolutionary hybrid intelligence papers state that current AI is insufficiently explainable and insufficient for strong intelligence, but they do not provide a formal coevolution model or longitudinal validation (Krinkin et al., 2021); (Krinkin et al., 2022). The Dynamic Relational Learning-Partner model proposes “interactive learning,” “conversational debriefing,” and “the third mind,” but its core constructs remain underdefined and unmeasured (Mossbridge, 2024). The Handshake and Incentivized Symbiosis frameworks are strong on process logic and institutional design, but weak on equations, operational variables, and longitudinal tests (Pyae, 3 Feb 2025); (Chaffer et al., 2024).

One notable controversy concerns what counts as evidence for shared agency. The recommender-systems paper reports in its abstract that transparency alone is insufficient and that combining transparency with direct controls enhances user agency (Wu et al., 2024). However, the accompanying technical synthesis also notes a discrepancy between the abstract and the provided body text, which describes a planned between-subject study and does not reproduce completed inferential statistics in the supplied manuscript section (Wu et al., 2024). This does not invalidate the paper’s construct decomposition, but it does illustrate a wider methodological issue in HACD-H: theory, interface design, and empirical validation often advance at different speeds.

Several open problems recur across the literature. First, many frameworks lack endogenous update laws for trust, role allocation, or governance, even when they describe those processes conceptually (Gao et al., 28 May 2025). Second, heterogeneity remains under-modeled: many formal systems aggregate all humans or all AI systems into single state variables (Wu et al., 7 May 2026). Third, path dependence, lock-in, and unstable-regime taxonomy are often discussed but not fully derived (Chakraborty, 24 Apr 2026). Fourth, institutional and legal observability remain central bottlenecks, especially where platform access, retraining histories, and recommendation logs are unavailable (Pedreschi et al., 2023). Finally, the literature strongly suggests that alignment should be treated as a property of the human-AI system as a whole rather than of models in isolation, but robust measurement protocols for that systems-level claim are still emerging (Wu et al., 7 May 2026).

Across these debates, a stable conclusion does emerge. HACD-H is not adequately captured by explainability alone, personalization alone, or partnership language alone. The literature suggests that any mature HACD-H must jointly model recurrent feedback, state persistence across multiple timescales, shared agency and control, institutional governance, and the possibility of both enhancement and degeneration.