Huiwen Han

Under what conditions does data become information? Shannon’s measure-theoretic account deliberately brackets semantic uptake by treating information in terms of uncertainty, entropy, and transmission; Dretske’s semantic theory and Floridi’s General Definition of Information restore meaning at the cost of leaving the transition itself under-specified. We propose an activation account: information is not a property of data but an event in which data acquires informational status relative to an observer’s context. We formalize activation as a partial function φ_c(d, ω), mapping a datum d, an observer ω, and a context c to an activation outcome, and articulate seven foundational axioms (AX-1 to AX-7) governing partiality, syntactic preconditions, semantic activation, observer-dependence, contextual sensitivity, temporal transience, and mode-sensitive value. The axioms locate Shannon, Bateson, Dretske, Wand–Weber, DIKW, and Floridi PTII as partial accounts or limiting cases of the activation problem, and identify the points at which each prior account collapses two distinct relations into one. The result is a formal-conceptual foundation for information that is neutral between syntactic and semantic readings and that supplies a principled separation between data, information, and downstream epistemic states. We develop the framework as an architectural realization of the DIKCA I-layer.

We develop a sociological account of how AI-mediated cognitive decoupling is insti- tutionalised and rendered invisible through ritual practice. Drawing on Malinowski's phatic communion, Collins's interaction ritual chain theory, Goman's dramaturgi- cal framework, and Baudrillard's theory of simulacra, we argue that the Expansion Compression (EC) loop identied in Paper 0 does not merely fail to transmit se- mantic content it actively substitutes a social ritual for a communicative act in a manner that is experienced by participants as fully equivalent to, or superior to, genuine intellectual exchange. We introduce the concept of cyber-grooming: the use of AI-mediated content pro- duction and consumption as a mechanism for maintaining social bonds, signalling group membership, and accruing social capital, in the absence of genuine cognitive engagement. Like primate grooming which consumes up to 20% of waking time not for hygienic but for social bonding purposes cyber-grooming performs no epis- temic function but sustains the emotional energy that holds intellectual communities together. We propose a formal model of the Interaction Ritual Chain under AI Mediation (IRCAM), adapting Collins's mathematical framework to account for the decoupling of emotional energy ow from semantic content ow. We prove that IRCAM pro- duces a ritual trap: once a community has adopted AI-mediated exchange as its norm, defection to authentic exchange is individually costly and collectively invis- ible, creating a stable ritualistic equilibrium with zero informational content and positive social aect. We further apply Baudrillard's four-stage model of the sign to characterise the simulacrum succession of intellectual content in the AI era. Empirically, we develop six observable signatures of cyber-grooming that distinguish it from genuine intellec- tual exchange, and propose research designs using digital platform data, discourse analysis, and experimental methods.

We construct a game-theoretic account of AI-mediated cognitive decoupling in the production and consumption of knowledge content. Extending Spence’s costly signalling framework to environments where production costs collapse asymmetrically, we prove that AI-mediated decoupling is a strictly dominant strategy under a broad class of utility functions (Dominant Decoupling Theorem). This dominance holds not because agents are deceived, but because the observable signal — a lengthy, wellstructured document — is decoupled from its previously costly production process, rendering the signal cheap for all types. We then model the resulting market for cognitive depth as a dynamic signalling game. We show that as AI adoption increases, the market passes through three distinct regimes: a separating equilibrium (high-depth agents are distinguishable), a pooling equilibrium (all agents produce identical signals), and finally a Lemons collapse in which no credible signal remains and the market for deep content unravels (Signalling Inflation Theorem). We characterise the speed of this collapse as a function of AI adoption rate and derive the critical adoption threshold q∗ beyond which the separating equilibrium is irreversibly destroyed. Finally, we identify two equilibrium escape routes: (i) certified costliness — institutional mechanisms that artificially re-introduce production cost (peer review, Turing-style verification) — and (ii) market stratification — the emergence of a high-trust, low-volume premium market for “AI-free” content. We characterise conditions under which each escape route is stable, and show that without external intervention, the system converges to a pooling equilibrium with socially sub-optimal information production.

We model the Expansion–Compression (EC) loop introduced in the DECO series as a discrete-time closed-loop control system, with the LLM expansion operator E as a forward gain element and the LLM compression operator C as a feedback element. Using the transfer-function formalism of linear systems theory and its nonlinear extensions, we analyse the stability, convergence properties, and phase transitions of a population of N such loops coupled through a shared semantic environment. We establish four principal results. First, the single-agent EC-loop is always asymptotically stable: the semantic state converges to a unique attractor determined by the LLM’s training distribution (Attractor Theorem). Second, for a population of N agents, there exists a critical innovation injection rate λc such that for λ > λc the population-level semantic state is diverse (multi-attractor regime), while for λ ≤ λc it collapses to a monoculture fixed point (Phase Transition Theorem). Third, under bias-amplifying prompts, the system can enter an unstable regime in which small perturbations grow unboundedly, yielding semantic runaway — the control-theoretic formalisation of “information bubble” dynamics (Runaway Instability Theorem). Fourth, we derive the Innovation Preservation Margin (IPM), a scalar index that predicts whether a given seed idea will retain meaningful distinctiveness after n iterations of the population-level EC-loop. These findings characterise the macro-dynamic consequences of AI-mediated cognitive decoupling at the societal scale and ground the signalling inflation and ritual-evacuation analyses of Papers 3 and 4 in a rigorous dynamical framework.

We develop a quantitative information-theoretic account of semantic decay in large- language-model (LLM) mediated ExpansionCompression (EC) loops. Building on the unied framework of DECO Paper 0 (Han, 2026a), we introduce semantic en- tropy HS(X) as the dierential entropy of a random variable distributed over a semantic manifold, and prove that each application of the EC-transform T = C ◦ E is a strictly entropy-reducing operation in expectation (Semantic Entropy Collapse Theorem). We derive closed-form bounds on the mutual information I KA; ˆKB between the originating ideas KA and the recipient's extraction ˆKB as functions of expansion ratio ρE, compression ratio ρC, LLM temperature τ , and iteration count n. We introduce the Semantic Gravity Well model: a potential landscape on the semantic manifold in which the LLM training centroid acts as an attractor, and original content resides at unstable high-curvature saddle points. Under this model, we prove the Dierential Decay Theorem: propositional delity Fprop decays expo- nentially in n, aective delity Faff decays sub-exponentially, and structural delity Fstr converges to a strictly positive constant (the causal skeleton invariant) under mild conditions. Finally, we characterise the Semantic Channel Capacity of the EC-loop and show it is strictly less than the Shannon capacity of the raw linguistic channel, with the gap determined by the curvature of the semantic manifold at the seed point. These results quantify the information-theoretic cost of AI-mediated cognitive decoupling and provide empirically testable predictions for the subsequent papers in the series.

We introduce a unified formal framework for analysing what we term AI-mediated cognitive decoupling: the systematic separation of cognitive process from cognitive product enabled by large language models (LLMs) operating as expansion and compression agents. We formalise two operators — the expansion operator E and the compression operator C — acting on semantic content, and characterise the composition C ◦ E as a lossy endomorphism on a semantic manifold S. We introduce the Decoupling Proposition, which asserts that under AI mediation the product of a cognitive act is no longer a function of its process, and formalise this as conditional independence: Prod(A) ⊥ Proc(A) | θAI. We define three strata of semantic fidelity — structural, affective, and propositional — and show they decay at different rates under iterated C ◦ E-transforms. Finally, we introduce the concept of the Indecomposable Core K∗: a subset of cognitive value whose existence is ontologically tied to the process itself, such that C ◦ E(K∗) ⊊ K∗ regardless of model capacity. This framework provides the conceptual scaffolding and mathematical language for the subsequent papers in the series.

This paper introduces the Algorithmic Leviathan Framework (ALF) by identifying a fundamental "AI Trilemma" between productivity, equity, and sovereignty. We demonstrate through a formal macroeconomic model that when AI-driven automation exceeds a specific threshold ($\alpha^*$), the traditional "production-consumption" loop enters a terminal deflationary spiral. The study concludes that private concentration of computational power necessitates a transition to Public AI Infrastructure to prevent a collapse into digital neo-feudalism.

Prompting has become the primary mechanism for interacting with large language models (LLMs), often accompanied by the implicit assumption that prompts can supply, inject, or constitute knowledge. We challenge this assumption by arguing that prompts do not generate, update, or instantiate knowledge within LLMs; rather, they function as transient control signals that shape inference trajectories grounded in pre-existing parameterized structures [2, 3]. Drawing on epistemology, control theory, and information theory [8, 9], we show that prompt-induced competence systematically fails to meet the criteria for knowledge attribution: persistence across contexts, generalization to novel situations, and counterfactual stability. We further explain why prompting appears to confer knowledge, tracing this to in-context learning effects, linguistic coherence, and human interpretive biases. The paper introduces the PAK(Prompt ̸= Knowledge) thesis, situates it within the DIKCA(DataInformation-Knowledge-Control-Architecture) framework [1, 6], and discusses implications for evaluation, governance, and AI system design.

Foundation-model-based intelligent systems increasingly use natural-language intent as their primary operational interface. In such systems, behavior is conditioned by high-level goals interpreted by learned models at runtime, rather than being fully specified in deterministic source code. This shift creates a systems-level gap that is not fully addressed by either classical software engineering or machine learning theory: how should we design, constrain, evaluate, and govern systems whose behavior emerges from model inference over underspecified intent? This paper proposes Intent-Conditioned Computation (ICC) as a systems-level framework for analyzing and engineering such systems. ICC identifies three structural layers: an intent interface, a model policy layer, and a deterministic infrastructure layer. These layers are connected through an explicit gatekeeper mechanism and an Intent–Action–Observation–Adaptation (IAOA) feedback loop. The framework is grounded in ArchHarness, an open-source enterprise architecture tool that illustrates how stored-program computation and intent-conditioned computation can coexist within a single real-world workflow. The paper derives implications for system design, evaluation, and governance, introduces Realization Fidelity as an ICC-specific evaluation metric, and outlines open research directions for foundation-model-based intelligent systems.

Contemporary artificial intelligence systems are increasingly shaped not only by what they represent, but by how their behavior is governed. Philosophical and technical discourse routinely treats such governing mechanisms — prompts, safety constraints, reward models, alignment protocols — as if they were forms of knowledge or wisdom. This paper argues that such treatment involves a category error in Ryle's sense: predicates belonging to one logical type are ascribed to entities of another, producing systematic conceptual confusion. Drawing on the distinction between truth-apt representation and regulatory structure, the paper demonstrates that knowledge and control belong to different logical types. Knowledge addresses the epistemic question of what is the case; control addresses the normative-operational question of what may be done. No accumulation of representational accuracy yields normative constraint as a logical consequence. A system may encode a norm — represent it accurately — without being governed by it. Governance requires enforcement, not merely representation. To clarify this confusion, the paper introduces the DIKCA framework (Data, Information, Knowledge, Control, Architecture). DIKCA extends the classical DIKW hierarchy by identifying control as an irreducible regulatory layer and architecture as the organizing structural law that constitutes data, information, knowledge, and control as a unified intelligent system. Architecture, on this account, is not an artifact of engineering design but a structural necessity: any system exhibiting intelligence must instantiate some architecture, and this architecture determines which data structures are permissible, which information-processing operations are valid, which knowledge representations are coherent, and which control mechanisms are enforceable. Autonomy — the capacity of a system to modify its own control structures — is reconceptualized as meta-control: an advanced property of the control layer operating under architectonic authorization, rather than a separate cognitive tier. Intelligence is the organization of data, information, knowledge, and control under architectonic law; governance is the institution and maintenance of that law. DIKCA is not a new ethical theory of AI, nor a metaphysical thesis about machine consciousness. It is a diagnostic clarification of the logical dependencies and structural necessities within contemporary AI systems, intended to restore analytic precision to philosophical debates on governance, agency, and the limits of the epistemic metaphor.

This paper introduces Civilizational Control Theory (CCT), a programmatic framework that models civilizations as open, dissipative, multi-layer control systems. CCT conceptualizes civilizational order as a stabilized non-equilibrium process maintained through information-energy regulation. We articulate ten foundational propositions concerning control bandwidth, mutual information, energy allocation gradients, spectral centralization, institutional evolution, and AI-induced compression of decision latency. These propositions reformulate civilizational dynamics as problems of stability, controllability, and adaptive feedback under entropy constraints.

Language, traditionally studied from linguistic and cognitive perspectives, can also be formalized as a physical system constrained by energy, entropy, and information. We propose Linguistic Physics, a unified framework that interprets language as an emergent, energy-constrained, and dynamically evolving system. This perspective integrates concepts from information theory, statistical mechanics, dynamical systems, and cognitive neuroscience to model phenomena such as compression, ambiguity, entropy regulation, semantic topology, phase transitions, alignment, evolution, creativity, multimodal emergence, hyper-language structures, and socio-cultural dynamics. We outline theoretical foundations, methodological strategies, and potential contributions, providing a roadmap for computational modeling, empirical validation, and applications in artificial intelligence.

This paper envisions a hybrid society where humans, robots, and AI agents coexist as intelligent, self-interested entities capable of planning, reasoning, acting, collaborating, competing, and evolving. Drawing on sociological theories (structural functionalism, conflict theory, exchange theory, constructivism), systems theory, economics, psychology, ethics, and management science, we analyze emergent societal structures and interactions. We consider AI traits like vast knowledge, continuous operation, rapid replication, and instant creation. Economics examines resource constraints, such as energy, and disparities in AI resource control. Psychology explores AI behaviors resembling selfishness or tribalism. Ethics addresses equality and moral obligations among humans and AI. Management science investigates coordination and conflict resolution. Systems theory models this society as a complex adaptive system, emphasizing openness, self-organization, and interconnectedness. We propose AI design principles to ensure adaptability, ethical alignment, collaboration, resilience, and systemic integration, fostering a harmonious and innovative society.

This paper introduces an innovative design for robotic operating platforms, underpinned by a transformative Internet of Things (IoT) architecture, seamlessly integrating cutting-edge technologies such as large language models (LLMs), generative AI, edge computing, and 5G networks. The proposed platform aims to elevate the intelligence and autonomy of IoT systems and robotics, enabling them to make real-time decisions and adapt dynamically to changing environments. Through a series of compelling case studies across industries including smart manufacturing, healthcare, and service sectors, this paper demonstrates the substantial potential of IoT-enabled robotics to optimize operational workflows, enhance productivity, and deliver innovative, scalable solutions. By emphasizing the roles of LLMs and generative AI, the research highlights how these technologies drive the evolution of intelligent robotics and IoT, shaping the future of industry-specific advancements. The findings not only showcase the transformative power of these technologies but also offer a forward-looking perspective on their broader societal and industrial implications, positioning them as catalysts for next-generation automation and technological convergence.