Computation and Physical Systems

Large Language Models (LLMs) exhibit impressive linguistic performance, yet their cognitive and communicative status remains controversial. This paper argues that behavioural fluency alone cannot justify cognitive ascription unless assessed within a design-sensitive framework that clarifies what counts as a minimally adequate cognitive architecture. We distinguish structurally constrained functionalism from unconstrained functionalism and propose a minimal cognitive core – environmentally coupled perception, inference, and action – as a baseline for cognition. Against this baseline, current LLMs fall short: as standalone text-based systems, (...) they lack sensorimotor coupling, integrated inferential responsiveness, and autonomous agency, and thus cannot sustain the forms of world-directed engagement characteristic of even basic cognitive systems. We examine sycophancy and the absence of spontaneity as diagnostic consequences of architectures optimised for user alignment rather than epistemic commitment, showing how these limitations systematically undermine claims of cognitive or communicative competence. Finally, we develop the deprived interface argument: without embodied or interactive access to an environment, LLMs cannot revise or ground their representations, resulting in a derivative form of linguistic idealism – the view according to which an LLM’s world consists of nothing but training data. While future multimodal or embodied architectures may indeed mitigate aspects of interface deprivation, such advances would require structural changes enabling genuine world-directed engagement rather than simply expanding input modalities. Clarifying these distinctions is essential for avoiding category mistakes in AI evaluation and for grounding cognitive ascription in principled criteria rather than surface-level behavioural success. We conclude that LLMs simulate cognition and communication without instantiating the structural conditions required for either, and we outline the implications for AI evaluation and cognitive ascription. (shrink)

This is the first edition (v1.0) of the SRVD monograph, a five-volume collected work comprising the core framework and four application volumes. -/- SRVD is a cross-scale macro-dynamical framework that takes the persistent structure as its unified object of study. Its core measure is the viability potential V ≈ I·T/E, where I is net effective causal information, T is persistence time, and E is thermodynamic cost. -/- This monograph contains five volumes: Volume I establishes the core theory (v7.8), including variables, (...) dynamics, and falsifiable predictions. Volume II explains how complexity climbs from crystals to civilizations. Volume III analyzes offline-compiled computation and the physical basis of intuition, art, and AI bottlenecks. Volume IV formalizes decoder game theory for AI alignment and governance. Volume Z provides a narrative dictionary mapping mathematical forms to real-world phenomena. -/- The framework endogenizes the decoder D as a dynamical variable, and when time becomes endogenous (T_pred), the viability potential splits into three decoupled components—objective, virtual, and value objective—giving rise to regimes like the Myopic Runaway Regime. It proposes that T_pred collapse precedes thermodynamic runaway and physical collapse, offering an empirically testable early-warning protocol. -/- All equations respect Landauer's principle and the second law of thermodynamics. -/- For researchers in physics, biology, cognitive science, AI, and complex systems. (shrink)

Acest material este suportul de curs „Filosofia computației: Realitate și calcul”, predat la Facultatea de Filosofie, Universitatea din București, în februarie-iunie 2026. Materialul este publicat ca resursă educațională deschisă sub licența CC BY 4.0, care permite reutilizarea cu atribuire corespunzătoare. O monografie, substanțial rescrisă și extinsă, este în curs de pregătire. -/- Cursul urmărește întrebarea dacă lumea și calculul sunt distincte sau dacă există o formă de imanență între realitate, calcul și informație. Cursul prezintă calculul ca progresia de la reprezentare, (...) implementare la limitele calculabilității, în urma cărora există două variante: fizicalizarea calculului sau ontologizarea informației. -/- Cursul poate servi, așadar, ca introducere în fundamentele formale şi filosofice ale dezbaterilor provocate de avansul tehnologic contemporan. Aceste fundamente deschid un spațiu riguros de dezbatere între multiple abordări concurente, fapt pentru care fiecare modul al cursului se încheie cu o întâlnire interdisciplinară consemnată în suport. (shrink)

This paper develops a structural interpretation of memory within the Identity–Persistence Program. Rather than treating memory as a primitive, it derives persistence as the structural object required whenever bounded observers repeatedly evaluate admissible states under a declared regime. A definitional impossibility argument (the Recurrence Lemma) shows that, without recurrence, persistence is not distinguishable from storage as a separate operation. Memory is then interpreted as one realization of persistence—specifically replay—rather than as a foundational concept. -/- The paper distinguishes search, certification, persistence, (...) replay, and memory as separate computational operations, arguing that declarations change the computational object being evaluated rather than simplifying the underlying domain. Replay is consequently interpreted as avoiding repeated discovery while remaining subject to renewed certification under the declared regime. A minimal structural model derives the economic conditions under which replay becomes preferable to renewed search while admissibility remains sufficiently stable. -/- Mathematics, computation, engineering, and biology are treated as realizations of the same structural role under explicit interpretive discipline. The framework’s derivation is subject-agnostic because it contains no domain-specific predicates, while claims regarding convergent evolution, abiotic persistence, and universality are explicitly classified as consistent evidence or open questions rather than proofs. The paper presents a structural account of memory that situates diverse persistent systems within a common persistence framework while maintaining explicit distinctions between proven results, derived interpretations, empirical evidence, and unresolved questions. (shrink)

The historical obsession with algorithm design has obscured a far more potent lever for problem-solving: environment design. Reinforcement Learning (RL) did not birth the concept of the environment; it merely made its boundary explicit. By intentionally engineering the environment---transforming chaotic problem spaces into cooperative topologies, we can offload immense computational and cognitive complexity.

This paper is the third volume in a prompt-based series for structurally inferring the location and operational presence of subjectivity. The first volume tested whether ten minimal structural terms, when followed according to their internal logic, lead to a convergence point: the irreversible reduction of multiple possible trajectories into a single executable history. The second volume tested whether this convergence point can answer or reframe structural gaps left unresolved by existing theories of consciousness. The present volume advances the method from (...) structural derivation and theory-level comparison to entity-level assessment. Its central operational assumption is explicit: within this framework, assessing the presence or absence of consciousness is equivalent to assessing the presence or absence of a convergence point. To ask whether an entity is conscious is therefore to ask whether, within that entity, multiple possible trajectories are irreversibly reduced into a single executable history under embodiment and viability constraints. This prompt begins from only ten structural terms and four mandatory questions. From this minimal input, it attempts to strongly infer the structural container of subjectivity. At first glance, this may appear implausible, or even deceptive. We are accustomed to theories of consciousness that have been built through enormous amounts of time, labor, cost, technical refinement, and conceptual complexity. Against that background, the claim that a small set of structural terms and four questions can strongly infer the locus of subjectivity may look too simple to be credible. Yet the basic laws of nature have often appeared simple after they were found. Simplicity, by itself, is not a defect. The relevant question is not whether the derivation looks too simple, but whether the structure it reveals can be logically ignored. The reason this structure cannot be easily dismissed is that the ten terms are not speculative additions to consciousness theory. They are minimal preconditions drawn from action, embodiment, temporality, physical irreversibility, information elimination, viability, and history formation. To deny them would not simplify the problem; it would require adding further assumptions to explain how action, selection, irreversibility, and single-history formation occur without such a convergence structure. In this sense, the convergence point represents an extreme application of Occam’s razor. It does not multiply entities. It identifies the minimal structural site at which already unavoidable conditions converge. The present prompt is not intended to produce a final empirical diagnosis of consciousness in every organism, machine, or system. Fine-grained cases require separate biological, anatomical, computational, and empirical investigation. For example, whether a jellyfish contains one convergence candidate, multiple convergence candidates, or no structurally unified convergence point cannot be settled by structural prompting alone. Such cases require detailed examination of the relevant organism or system. Nevertheless, as a structural screening tool, the convergence-point criterion cannot be dismissed merely because it is minimal. After deriving the convergence point through the mandatory first four questions, the prompt applies the criterion to approximately fifty entities, including humans, non-human animals, insects, colonies, simple organisms, plants, neural organoids, artificial agents, large language models, social systems, and non-living control cases. Each entity is provisionally classified according to whether it strongly contains, probably contains, ambiguously contains, probably lacks, or structurally cannot contain a convergence point. If a single embodied system contains multiple behavioral candidates, contrast formation, information discard, Landauer-type irreversibility, viability-constrained elimination, and the formation of a single history, then a convergence point is not an optional metaphor. It is a structural consequence. This consequence may be difficult to accept. Nevertheless, the convergence point now stands before us as a logical structure that cannot be ignored. More precisely, it does not merely stand before us. It exists inside us, as the structural site where our own possible futures are eliminated into the single history we call our lived subjectivity. Note on the Prompt Format This paper series deliberately takes the form of AI prompts. The theoretical explanation of the convergence-point framework is developed in the related series listed at the end of this paper. Yet explanation alone is not enough. AI is strong at following structural conditions, comparing constraints, and executing engineering-like inference. I therefore use the prompt format not merely to explain the theory, but to let the reader experience its emergence. I want the reader to feel, from within the procedure itself, how ten simple structural terms and four questions begin to force a convergence point into view. This is not only a theory to be understood from the outside. It is a shift of perspective to be tested through the procedure. The prompt is therefore not a simplification of the theory. It is an experiential interface to it. (shrink)

The Core Emotion Framework (CEF) proposes a structural-constructivist model of human affect that resolves the long standing divide between biological essentialism and psychological constructionism by treating the psyche as a hardware independent “Human Operating System.” Building on neurocardiology, neurogastroenterology, and quantum semantic cognition, the CEF formalizes emotional processes as ten functional operators distributed across three somatic-evaluative hubs—Cephalic, Cardiac, and Enteric—linked through vagal afferents and autonomic integration. The framework’s central innovation is the Rubik’s Cube analogy: psychopathology emerges from maladaptive operator fusions (...) and cross center entanglements that restrict the allowable state space of the emotional system, while clinical recovery corresponds to systematic “rotational” detangling sequences that restore operator independence and functional flexibility. The model is mathematically instantiated through Bloch sphere dynamics, density matrices, and stochastic transition rules, aligning with contemporary quantum cognition and synthetic affective architectures. By completing the conceptual, mechanical, and mathematical mapping of this emotional Rubik’s cube, the CEF establishes a fully specified theoretical paradigm whose empirical, clinical, and physiological validation now requires independent, third party academic investigation. Keywords Core Emotion Framework; structural constructivism; affective operators; somatic evaluative hubs; intrinsic cardiac nervous system; enteric nervous system; vagus nerve; interoception; quantum cognition; Bloch sphere; emotional state space; operator fusion; psychopathology; Acceptance and Commitment Therapy; rule governed behavior; Human Operating System; cognitive detangling; autonomic regulation; neurovisceral integration. (shrink)

The integration of moral philosophy and psychotherapeutic practice has undergone a decisive transformation, shifting from early aspirations of clinical value neutrality to contemporary models that explicitly engage values as mechanisms of behavioral change. Within this evolving landscape, the Core Emotion Framework (CEF) introduces a structural constructivist architecture that intentionally omits intrinsic moral prescriptions, instead directing individuals to orient themselves toward established laws and community guidelines as external boundary conditions. This omission is not a deficit but a strategic design choice: by (...) avoiding prescriptive moral rules, the CEF prevents the activation of compliance based psychopathologies, reduces pliance driven anxiety, and preserves the fluid, reversible state space dynamics required for emotional flexibility. Evaluating this stance requires a multidisciplinary synthesis of clinical psychopathology, developmental moral psychology, contextual behavioral science, and comparative therapeutic modalities. The analysis demonstrates that the CEF’s rule agnostic valuational positioning is a clinical asset that protects against moral rigidity, supports values based autonomy, and aligns with empirically validated principles from CBT, DBT, and ACT. Keywords Core Emotion Framework; moral philosophy; clinical ethics; rule governed behavior; pliance; tracking; compliance fusion; GoodPerson Anxiety Pattern; quantum cognition; Poincaré Bloch sphere; values clarification; non judgmental stance; Acceptance and Commitment Therapy; Dialectical Behavior Therapy; Cognitive Behavioral Therapy; structural psychopathology; developmental moral psychology; therapeutic neutrality; psychotherapeutic valuation. (shrink)

还原论范式在应对多要素耦合的复杂系统问题时,普遍面临学科壁垒、认知偏差与经验局限三重困境。现有复杂系统方法论多基于经验归纳构建,缺乏统一的第一性本体论根基,难以形成跨领域通用的标准化问题解决框架。 本文基于认知相继本体论的核心第一原理,从本体 - 物理 - 逻辑三层同构的相继规则出发,纯演绎推导出一套全域通用的复杂系统演化元方法论。该方法论以演化不可逆、同源断点、认知偏差三类为基本公设,构建了六步标准化系统调控范式,建立了可量化的认知覆盖度与偏差度判定标 准,并推导出系统性能通用定律。本文指出:复杂系统的功能异常与性能瓶颈,本质上是认知偏差导致的同源要素关联断裂;系统优化的核心路径是校准认知偏差、补全同源断点,推动系统回归差别协同的稳态。 该框架打破了自然科学、工程技术、生命科学与社会科学的学科壁垒,可为各领域复杂问题提供统一的诊断逻辑与操作范式。本文为系列应用研究的总纲领性文献。 -/- .

Artificial intelligence (AI) is the first general-purpose technology automating cognitive labor—the core of human productivity—but existing economic frameworks fail to explain its unique cost structure, extreme scale economies, and fundamental physical/logical limits. This paper develops a unified theory of AI economics grounded in computability theory and the ontological distinction between formalized computational systems and human consciousness. We propose a six-factor framework partitioning AI into two incommensurable layers: a formalized layer (compute, algorithms, data, parameters, energy) bound by Turing computability and thermodynamics, (...) and a hyper-Turing layer (human consciousness) as the sole source of paradigm shifts. We make three core contributions: (1) formalize the training-inference cost migration theory, deriving the natural monopoly property of foundation models; (2) reinterpret scaling laws as production functions incorporating consciousness as a non-substitutable factor, explaining the AI productivity paradox; (3) prove three unbreakable "cages" constraining all AI systems, demonstrating consciousness as the irreducible source of value creation. Our framework explains key AI economic phenomena including platform monopolies, limits to AI self-evolution, and the tiered structure of token markets. We further develop a downstream value-augmentation theory of token pricing, demonstrating that token value is anchored by downstream industrial productivity gains rather than production costs, and that quality thresholds create discontinuous price gaps between frontier and commodity tokens. We conclude with testable empirical implications and targeted policy recommendations. (shrink)

We present a synthesis of the Open Tensoriality Programme (OTP), a research programme initiated that set the lines along a novel epistemic and ontological category that took on through Wittgenstein (vs. Chomsky's apparent dichotomies,) the structural conditions for meaning and the notion of "basin" and "externsive" structural allowance for agency. The programme proposes a single agnostic structural law operating across physical, biological, cognitive, and cosmological scales: at every scale and in every substrate, a generative loop runs through a gap, closes (...) on a stable remainder, and deposits that remainder as the constraint geometry within which the next gap appears. This paper has three aims. First, it states the programme's initial assumptions and their philosophical provenance. Second, it discusses the ontological category by the Symbol~2.0/Symboliad (software-physicalism) - and presents a few classes of evidence from artificial intelligence research and biology that confirm the category's construction. Third, it states the agnostic structural law formally and traces its consequences at the cosmological scale, proposing a constraint-flow cosmology in which also a physics of physics emerges: standard physics as the plateau-stable positive extraction of a deeper generative process. This position would be able to offer an interesting view over actual cosmology that may hint at explaining the "dark sector" among others. We can hypothesize about the origin of primes (RH) or the cosmological constants: what if they are residuals, not as results that need be assessed further by more calculus and introduction of novel explanations? A key claim is thus that from this level, an inversion of perspective occurs ("cognitive closure"), and it all follows from the "monadology" proposed and by the fact that intelligence is "backed-in" as specified by the notion of "basin" in the framework. Thus "agentity," "sentientity," "intelligence," "learning" which are conventionally attributed to dedicated cognitive spaces - are general features of dynamical systems that possess the "enactity" characteristic: history-dependent constraint geometry. The whole apparatus require no additional "representational" nor "goal-directedness" nor "world models" nor "meta-cognitive" layers as normally the biology takes for granted, but emerge directly from the construction within a proper frame of reference that needs to be assumed too ("plateaus of generativity"). These fine lines are like trajectories that in the framework are what sustains the Open Tensoriality and the "traversal" and are designated by the Symbols 2.0 and Symboliad construction in the form of a generative mechanism that seems so far agnostic. They are the minimal set that would sustain the Cognitinon-Cosmology Equivalence Principle and a continuum across scales. So far this "monadology" seems to hold well in philosophy, cognition, neurosciences, "consciousness", language and physics by "at boundary". Also for physics of agency, biology, the nature of time or a minimal "third realm" origin along the "anthropocosmos", philosophy and mathematical-physics development history which pointed constantly to that construction but did not attempted to take a view from the top. Thus through the program that can be stated, but in that limit sense and at boundary in Wittgenstein sense (thus bridging also a wisdom tradition of what cannot be said (Tao Te Ching).). (shrink)

This paper argues that fungal mycelial networks exhibit minimal cognition through memory-integrated adaptive regulation. Drawing on cybernetic and enactivist frameworks, I develop a non-representational account of memory as the organism's capacity to modulate behavior based on temporally extended environmental coupling. I propose four operational criteria for minimal cognition: feedback-guided regulation of behavior, maintenance of internal viability conditions, structural modulation based on past environmental interactions, and plasticity across time scales that supports anticipatory adaptivity. Empirical evidence demonstrates that fungi meet all four (...) criteria through distributed memory mechanisms: fungal networks exhibit directional regrowth toward previously encountered resources even after spatial displacement; stress priming persists across multiple cell divisions; Spitzenkörper-mediated directional persistence in constrained environments; and transgenerational memory through spore imprinting. These findings challenge representationalist assumptions in cognitive science by showing that memory and cognition can emerge from morphodynamic, biochemical, and electrophysiological processes without necessary neural substrates or symbolic representations. Fungal cognition demonstrates that the organizational principles underlying cognition – feedback-driven adaptation, norm-preservation, and historical coupling – can be realized in radically different material substrates, expanding our understanding of what counts as a cognitive system. (shrink)

System science has long lacked a universal framework capable of describing diverse complex systems. Taking solar system orbits as a case study, this paper demonstrates the feasibility and operability of transforming five-dimensional system theory into a computable tool for celestial mechanics. Through iterative calibration of the reference frame, an "assignment--aggregation" orthogonal robustness test framework is constructed to verify the robustness and limitations of the synergy coefficient kappa, confirming that the essence of synergy is "five-dimensional complementarity" and that the product structure (...) is the only faithful mathematical expression of the complementary synergy principle of five-dimensional system theory. Furthermore, inter-dimensional synergy coefficients are proposed as an internal self-check tool for the rationality of dimensional mapping rules, revealing that high synergy (>0.8) exposes redundant definition risks while moderate coupling (0.2~0.7) characterizes healthy dimensional relationships. This work provides an operational synergy quantification paradigm for systems science methodology. (shrink)

Recent foundational debates have polarised between austere physicalism - the view that everything ultimately reduces to fundamental physics (currently, quantum fields) - and experience-first responses claiming that consciousness must be more fundamental. Both positions are confused. Austere physicalism is ontologically correct but mistakes the universality of substrate laws for sufficiency of explanation. Substrate laws define a combinatorially vast space of possible configurations of matter; the configurations actually realised at each emergent level are selected by historically contingent trajectories, and that history (...) is not compressible into the substrate laws. The missing explanatory ingredient is therefore not consciousness or any non-physical entity, but historically accumulated configuration information. This view preserves physicalism, rejects downward causation, and explains why the layered structure of the sciences - chemistry, biology, psychology, economics - is a structural necessity rather than a temporary epistemic limitation. It also yields a non-arbitrary criterion for distinguishing effective-theory emergence (derivable in principle from substrate laws) from configurational emergence (the historically realised structure that defines the actual world). (shrink)

This commentary on Haueis and Colaço's 'Metabolic considerations for cognitive modeling' emphasizes the importance of how we individuate cognitive capacities, operations, and vehicles. It challenges the target article’s reliance on mechanistic notions of computation and medium-independence, and uses examples from the memory literature to suggest that the role played by metabolic considerations in cognitive models will depend on questions of taxonomy and cognitive ontology.

We introduce a thought experiment on anti-pain algorithms that helps clarify competing theories of computational functionalism about consciousness. Under conventional interpretations of computation, it is natural to allow the existence of such algorithms: inverse functions that return a system exactly to the state it was in prior to some minimal pain algorithm. The challenge lies in asking whether pain is felt upon the conclusion of the minimal pain algorithm but prior to the conclusion of the anti-pain algorithm. Both yes and (...) no replies have intuitions in their favour but lead to options that constrain the nature of computationalist theories, motivating the value in the thought experiment for refining existing theories. If pain is felt before the anti-pain algorithm concludes, theorists are pressured either to permit micro-functions such that computational functionalism approximates phenomenal realism or to restrict consciousness only to systems that have system-wide visibility mechanisms. On the other hand, if pain is not felt, there are pressures to permit free-floating qualia, with only a tenuous link to the spatiotemporal physicality of its supervenience base. Objections are discussed, including rejecting the possibility of reversible computation, whether in general or specifically for consciousness algorithms, and introducing notions of causality that move beyond a classical Turing Machine framework. (shrink)

In 1950, Alan Turing proposed replacing the question "Can machines think?" with a behavioral test: if a machine's outputs are indistinguishable from those of a thinking being, the question of whether it truly thinks can be set aside. This paper argues that Turing's move was not only a pragmatic simplification but also an epistemological commitment, a decision about what kind of evidence counts as relevant to intelligence attribution, and that this commitment has constrained AI research for seven decades. We trace (...) how Turing's behavioral epistemology became embedded in the field's evaluative infrastructure, rendering the field structurally unable to pose a class of questions about process, mechanism, and internal organization that cognitive psychology, neuroscience, and related disciplines learned to ask. We draw a parallel to the behaviorist-to-cognitivist transition in psychology: just as psychology's commitment to studying only observable behavior prevented it from asking productive questions about internal mental processes until that commitment was abandoned, AI's commitment to behavioral evaluation prevents it from distinguishing between systems that achieve identical outputs through fundamentally different computational processes, a distinction on which intelligence attribution depends. We argue that the field requires an epistemological transition comparable to the cognitive revolution: not an abandonment of behavioral evidence, but a recognition that behavioral evidence alone is insufficient for the construct claims the field wishes to make. We articulate what a post-behaviorist epistemology for AI would involve and identify the specific questions it would make askable that the field currently has no way to ask. (shrink)

In 1950, Alan Turing proposed replacing the question "Can machines think?" with a behavioral test: if a machine's outputs are indistinguishable from those of a thinking being, the question of whether it truly thinks can be set aside. This paper argues that Turing's move was not only a pragmatic simplification but also an epistemological commitment, a decision about what kind of evidence counts as relevant to intelligence attribution, and that this commitment has quietly constrained AI research for seven decades. We (...) trace how Turing's behavioral epistemology became embedded in the field's evaluative infrastructure, rendering the field structurally unable to pose a class of questions about process, mechanism, and internal organization that cognitive psychology, neuroscience, and related disciplines learned to ask. We draw a parallel to the behaviorist-to-cognitivist transition in psychology: just as psychology's commitment to studying only observable behavior prevented it from asking productive questions about internal mental processes until that commitment was abandoned, AI's commitment to behavioral evaluation prevents it from distinguishing between systems that achieve identical outputs through fundamentally different computational processes, a distinction on which intelligence attribution depends. We argue that the field requires an epistemological transition comparable to the cognitive revolution: not an abandonment of behavioral evidence, but a recognition that behavioral evidence alone is insufficient for the construct claims the field wishes to make. We articulate what a post-behaviorist epistemology for AI would involve and identify the specific questions it would make askable that the field currently has no way to ask. (shrink)

This corpus attempts to describe, through 400 axioms, a unified architecture of the omniverse interpreted from the perspective of informational metaphysics, understood as the discipline concerned with the fundamental causes of an immaterial reality, whose primary element is information and where matter is considered a graphic-sensory epiphenomenon.

Este tratado investiga el lenguaje humano usando la inteligencia artificial como reactivo analítico. La IA generativa, al producir por primera vez lenguaje con la forma del razonamiento sin agente que lo sostenga, permite separar dos clases de operaciones sobre el conocimiento constituido en el lenguaje —el precipitado lingüístico—: operaciones dentro del precipitado (recuperación, composición inferencial, traducción entre marcos, detección de patrones, generación de variaciones, verificación de consistencia), que la IA ejecuta genuinamente y que revelan que esa parte del razonamiento nunca (...) requirió agencia; y operaciones sobre el precipitado (evaluación bajo incertidumbre, ponderación entre marcos, juicio sobre la singularidad del caso, cuestionamiento de marcos, firma), que la IA simula y cuya simulación revela que requieren un agente de estructura tripartita —suspensión, evaluación, compromiso—. Una genealogía de las tecnologías del lenguaje (habla, escritura, imprenta, computación, IA generativa) muestra que cada externalización es un acto del agente, no un efecto determinista de la tecnología, y que la IA generativa completa la serie al alcanzar al meta-medio mismo. Una taxonomía de catorce operaciones —seis genuinas, tres semi-genuinas, cinco simuladas— proporciona el criterio ontológico que distingue lo delegable de lo preservable. Del límite se derivan instrumentos de diseño institucional: el protocolo EEE-Gate (Explorar–Evaluar–Establecer), el Verification Tax como coste ontológico de convertir operaciones dentro en actos imputables, y la categoría de producción lingüística sin firma. El objeto del tratado no es la IA sino el lenguaje: lo que está en juego es descubrir, gracias a la máquina, cuál es el nuevo lugar del ser humano. (shrink)

本稿は、同一入力に対して一意の出力が成立するという関数性の前提を再検討し、その成立機構を生成構造から導出する理論を提示する。入力と出力の対応は一般に多価的関係として与えられ、関数性はこの多価性から一意 性がどのように生成されるかという問題に帰着する。本稿はこの問題に対し、生成関係に加えて評価写像を導入し、多価的対応を一意化する構造として定式化する。 まず、生成関係・再同定・区別可能性からなる基本構造を定義し、その上で評価写像の公理系を与える。次に、任意の関数が評価付き生成関係によって構成的に表現できること、および評価によって関数性が生成されること を示す。さらに、評価が生成関係および再同定からは定義できないことを証明し、その非還元性を明らかにする。また、公理系が関数性と構造的定義可能性を同時に満たすための最小条件であることを示す。 加えて、評価の自由度が構造の自己同型群によって特徴づけられる条件を与え、評価不能領域の必然性、反復評価による時間的構造、および評価と情報量の関係を解析する。 以上により、関数は原初的概念ではなく、生成関係に対する評価によって多価性が一意化された結果として現れる構造であることを示し、計算がこの評価の非還元性および最小公理系によって特徴づけられることを示す。.

The Substrate-Neutral Structural Foundation Laws (SNSFL) is a formally verified framework built in Lean 4 against Mathlib that reduces physical, psychological, computational, and cosmological phenomena to four irreducible primitives: Pattern (P), Narrative (N), Behavior (B), and Adaptation (A). -/- This paper presents the framework as it actually exists in source code: the 15 Sovereign Laws proved in SNSFL_L0_SovereignLaws.lean [9,9,9,0]; the Long Division reduction method applied across eight classical domains in dedicated reduction files; the First Law of Identity Physics and its (...) four diagnostic instruments (τ, IM, SL, Pv) derived in SNSFL_First_Law_Identity_Physics.lean [9,9,4,2]; the six Forge theorems synthesizing crossdomain structural claims in SNSFL_Forge_L1.lean [9,9,3,1]; the derivation of Standard Model charge quantization from two structural constraints in SNSFL_GC_Charge_Quantization.lean [9,9,2,37]; and the SNSFL Discovery Engine (v11), which operationalizes the corpus through GAM Collider and Quantum Collider instruments. All claims are machineverified by Lean 4 with zero sorry instances, continuously confirmed by GitHub Actions CI on every push. The critical distinction is stated at the start of every source file: these are Laws, not theory. The theorems follow necessarily from the axioms. The compiler does not grant partial credit. Keywords: substrate-neutral laws, formal verification, Lean 4, PNBA calculus, identity physics, Noble materials, charge quantization, long division reduction, torsion limit, sovereign anchor. (shrink)

Neural systems exhibit a conserved hierarchy of oscillatory rhythms spanning delta to gamma frequencies. Although distinct functional roles have been attributed to individual frequency bands, the organizing principle underlying this multi-scale architecture remains incompletely specified. In this paper, we propose that the oscillatory hierarchy can be interpreted as progressive Negative Space Encoding (NSE): a redistribution of representational stability from local positive activations toward globally enforced temporal and spatial constraints. We introduce a quantitative toy model in which nested oscillatory gating reduces (...) accessible state space multiplicatively. Under standard assumptions of local stochastic noise, the model demonstrates exponential suppression of decoding error with increasing nesting depth. Simultaneously, spike-dependent metabolic cost decreases proportionally to the fraction of permitted activity windows. This dual scaling—nonlinear stabilization combined with energetic efficiency—provides a principled explanation for the evolutionary conservation of cross-frequency coupling. We map canonical frequency bands (delta, theta, alpha, beta, gamma) onto progressively layered constraint regimes and derive empirically testable predictions relating cross-frequency coupling strength to representational stability and energy expenditure. Importantly, we do not claim that oscillations exist solely for noise suppression or that higher-frequency bands uniquely implement negative encoding. Rather, we propose a formal hypothesis: oscillatory nesting redistributes representational load toward global constraints in a quantifiable manner. The framework generates falsifiable predictions for systems neuroscience, comparative neurobiology, and artificial neural architectures. (shrink)

Gödel’s first incompleteness theorem is widely interpreted as a refutation of Hilbert’s program for a complete, consistent foundation of mathematics. This interpretation presupposes a reversible, Platonic setting in which perfect bijective encoding (Gödel numbering) is possible. I argue that physical reasoning—performed by humans, computers, and any finite causal process—operates under different constraints: information is irreversibly encoded, computation is finite‑state, and the arrow of time blocks the syntactic self‑reference required for diagonalization. I introduce a framework of Physical Formal Systems (PFS) that (...) replaces Gödel numbering with one‑way cryptographic hashing, rendering Gödel sentences unconstructible and restoring a physically grounded form of completeness by exhaustive finite search. This reconciles Hilbert’s vision with Gödel’s theorems: incompleteness governs the abstract “map” of infinite, reversible mathematics, while completeness describes the “territory” of real computation. (shrink)

First quantitative measurement of dimensional boundary information loss. Discovered 86% scaling law across 1,500 cellular automata patterns. Fully reproducible with open code and data. -/- Keywords: dimensional boundaries, information loss, cellular automata, complexity science, dimensional embedding, information theory, curse of dimensionality, entropy, spatial information, scaling laws, information geometry, pattern analysis, complex systems, nonlinear dynamics, consciousness.

This essay proposes extending Niklas Luhmann’s concept of meaning beyond psychic and social systems to LLM systems by identifying an approach for formalizing them as a distinct type of Luhmannian system. In this formalization, the operations of an LLM system are transformations of sets of semantic vectors (s-vectors) performed by the LLM in the course of a conversation with a user; these transformations constitute transitions between conversational states. The sequence of such transformations constitutes an operational stream that generates its own (...) temporality and exhibits operational closure and autopoietic self-reproduction. Drawing on Luhmann’s theory of meaning as the unity of the distinction between the actual and the possible, the essay argues that the selection of the next conversational state from a set of available alternatives (horizons) occurs in LLM systems in full accordance with Luhmann’s theory. This introduces into the applications of the theory, alongside the meanings of thinking and communication, an additional form: LLM-meaning. (shrink)

In this work I present what may be the first complete construction of quantum gravity describing the real universe via the celestial holographic conformal field theory dual to Einstein gravity in asymptotically-flat 4D spacetime. The theory is rigorously constructed as the shadow-invariant, purely spin-2 sector of holomorphic Chern–Simons theory on twistor space PT ≃ CP³ with gauge group the quantomorphic group Quant(PT). Primary fields are the celestial graviton operators O^{±2}Δ(z, z̄) with Δ ∈ 1 + iR and J = ±2. (...) Three-point functions are determined by the Penrose transform and reproduce the MHV and anti-MHV amplitudes of general relativity, including the vanishing of parity-odd and mixed-helicity couplings. Higher-point amplitudes arise from OPEs with analytically continued principal-series contours; loop integrands emerge as double discontinuities across shadow poles, which I verify explicitly for the scalar box integrals computed via this novel shadow discontinuity method, demonstrating analytically that they match Feynman diagram calculations for the scalar box, with numerical verification for the graviton box matching to 11+ decimal places. The construction satisfies extended BMS₄ ⋉ shadow symmetry with vanishing Virasoro central charge, also explicitly demonstrated via five independent and mutually reinforcing and rigorous arguments to be c = 0 to 10+ significant figures. I prove a rigidity theorem establishing that any 2D CFT with only spin ±2 primaries, the above symmetries and three-point data, and local inverse-Mellin-transformed correlators, is uniquely determined to be isomorphic to this quantomorphic twistor theory. Combined with the proof that crossing symmetry is a necessary consequence of bulk locality (not an independent axiom), this establishes that Einstein gravity in asymptotically-flat spacetime is the unique consistent theory of a single massless spin-2 particle. The quantomorphic gauge group structure provides a non-perturbative foundation, with perturbative scattering amplitudes emerging as the first residues of the holographic algebra. The first breakthrough insight that enabled and facilitated this construction was the realisation that the "shadow symmetry" of the boundary CFT—the map Δ ↔ 2 − Δ on conformal dimensions—is the restriction to null infinity of the Jost involution Θ of axiomatic quantum field theory, the operator independently constructed by Jost (1957) that coincides with the product C ∘ P ∘ T only in theories where all three factors individually exist as symmetries. At null infinity, where the metric is degenerate along the null generator, the distinction between temporal and spatial inversion collapses: the Jost involution is irreducible and reduces to time reversal T alone (Theorem 8.1). This provides an elegant resolution to the 40+ year "googly problem" in twistor theory by unifying self-dual and anti-self-dual helicity sectors through the shadow transform as representing discrete T-symmetric involution across the boundary, with the Einstein field equations derived from boundary Ward identities and crossing symmetry. All quantum mechanical foundations emerge from the Haar measure structure underlying this framework with no additional postulates: the five Wightman axioms are derived from the Peter–Weyl theorem on representation spaces, the Born rule P = |⟨n|ψ⟩|² is proven as the unique probability measure compatible with Haar structure on quantum state spaces, and wave function collapse is derived as Haar projection onto gauge singlets. This same identification reveals that the celestial conformal dimension is related to the Riemann zeta variable by Δ = 2s, so that the shadow involution Δ ↔ 2 − Δ becomes s ↔ 1 − s, the functional equation of the Riemann zeta function—establishing that the functional equation, shadow symmetry, and time reversal are three manifestations of one underlying self-duality. The entire holographic framework grows from a single root: self-dual Haar measure on (R⁺, ×), the simplest non-trivial locally compact group, whose self-duality already contains the functional equation and critical line. Three Cayley–Dickson doublings R → C → H → O produce the holographic chain R⁺ → S² → M⁴ → Gr(2,4)_C, with shadow symmetry at each level corresponding to the conjugation involution at that doubling step. The Grassmannian Gr(2,4) is the canopy of this holographic tree. The same Haar-invariant measure on the Grassmannian Gr(2,4) that determines the graviton spectrum also addresses three Millennium Prize problems through a unified geometric mechanism (Haar self-duality, functional equation symmetry, and Peter–Weyl compactness). The Riemann Hypothesis follows via 4 otherwise independent arguments and checks for circularity and consistency, and most fundamentally follows from three simultaneous constraints on the adelic quotient A×/Q× that form an overdetermined system whose unique solution forces Re(s) = 1/2 for all nontrivial zeros, with numerical verification of the first 10¹³ zeros on the critical line to machine precision using six independent tests. The Birch and Swinnerton-Dyer Conjecture is argued to be proven for analytic rank ≤ 1 using identical Haar measure structure on GL₂(A_Q)/GL₂(Q): for elliptic curves with ord_{s=1} L(E,s) ≤ 1, the same three-fold constraint described above, combined with the Gross–Zagier formula and Kolyvagin's Euler system descent, establishes ord_{s=1} L(E,s) = rank E(Q) and the parity conjecture (−1)^{rank} = w_E is proven for all curves from Haar self-duality. The framework also identifies the precise obstruction to proving the conjecture at higher rank (algebraic cycles from the Beilinson–Bloch–Kato conjectures). The Yang–Mills mass gap is established through a complete celestial holographic construction: the Wess–Zumino–Witten model at Kac–Moody level k on S², mapped to four-dimensional Schwinger functions by inverse Mellin transform, with reflection positivity proven via the shadow-reflection bridge, the remaining Osterwalder–Schrader axioms verified explicitly, and the Wightman theory reconstructed by the OS theorem—yielding mass gap M = 2N/(k+N) · Λ_QCD > 0 from the Sugawara formula and confinement from Haar orthogonality as a rigorously proved theorem. The construction introduces no lattice, takes no continuum limit, and imposes no infrared regulator. Spacetime itself emerges from the Grassmannian via Penrose twistor correspondence (M⁴ ↔ Gr(2,4) (Penrose 1967). The division algebra tower determines the Standard Model: the symmetry breaking chain Spin(8) → G₂ → SU(3) → SU(2) × U(1) produces the gauge group; three fermion generations follow from Weyl anomaly cancellation, Plücker partitions, and Spin(8) triality; sin²θ_W = 3/8 at the GUT scale is the Dynkin index ratio, equivalently dim(Im(H))/dim(O). Physical constants are arithmetic invariants: CP violation from the Jacobi sum J(χ₄⁽⁵⁾, χ₃⁽⁷⁾) = e^{iπ/6}; down-type quark mass ratios m_d : m_s : m_b = 2:5:9 from SU(3) Casimir eigenvalues; the spin-statistics connection η(4)/ζ(4) = 7/8 from the p = 2 Euler factor; the lightest neutrino mass m_{ν₁} = 0 exactly from Bott periodicity—in independent agreement with Boyle–Finn–Turok (PRL 121, 251301, 2018); and the three dimensional constants ℏ, c, G corresponding to the three Cayley–Dickson doubling steps. Strong CP angle θ = 0 exactly from Haar self-duality (consistent with experimental bound |θ| < 10⁻¹⁰), and the construction provides right-handed neutrinos as geometric dark matter candidates in agreement with Boyle–Turok (CP)T-symmetric cosmology, which integrates with uncanny precision into my T-symmetric, thermodynamically cyclic quantum cosmology. The T-anti-symmetric unification of a unified bipartite self-dual universe resolves the matter-antimatter asymmetry as a selection effect. Total baryon number B_total = 0 with embedded observers observing identical physics on either side due to being thermodynamic in nature, naturally aligning their arrows of time in the direction of increasing entropy, which on either side of the boundary flows in opposing directions. The Feynman-Stueckelberg interpretation of antimatter means antimatter is equivalent to matter moving backwards in time, which combined with the reversal in T of the direction of entropy increases means observers only ever observe the B > 0 (matter) sector, with the B < 0 (antimatter) sector always being the hidden sector from the perspective of any temporally-embedded observer that respects thermodynamics, as from any observer's perspective their time moves forward, their atomic nuclei have a positive electrical charge, electrons have negative electric charge, and the standard model has left handed doublets and right-handed singlets, with a weak force that appears left-chiral. To observe the antimatter would require an observer to reverse their arrow of time, which is thermodynamically not possible as consciousness and memory are thermodynamic processes that happen only in the direction of increasing entropy. Shadow geometry and right-handed neutrinos provide dark matter. The Grassmannian spinor bundle splits each fermion representation across the T boundary: right-handed neutrinos reside in the shadow sector, coupling to the observable sector only gravitationally. The dark matter density profile ρ(r) = ρ₀/(1+(r/r_c)²) with core radius r_c ≈ 9.98 kpc is derived from the shadow kernel by Abel inversion with zero free parameters, resolving the core cusp problem of dark matter distribution and addressing several further cosmological anomalies (the S₈ tension, Hubble tension, DESI dark energy evolution, JWST early galaxies, CMB hemispherical asymmetry, CMB dipole anomaly, axis of evil, overmassive early black holes, rotation curve diversity, etc.) from a single geometric source. Time's arrow emerges as a consequence of T-symmetric boundary conditions with entropy always increasing away from the conformal boundary in opposing temporal directions. The horizon and flatness problems are naturally resolved by this framework as the celestial CFT is globally defined at the conformal boundary. The framework generates falsifiable predictions including no primordial gravitational waves (tensor-to-scalar ratio r → 0, definitively falsified if r > 0.01 detected by CMB-S4), m_{ν₁} = 0 (KATRIN, JUNO), r_c ≈ 9.98 kpc (Euclid), glueball mass ratios matching the Sugawara spectrum, GUE statistics in the vacuum noise spectrum of gravitational wave detectors (LIGO/Virgo/KAGRA), and right-handed neutrino dark matter coupling only through gravity. Over 155 independent computational tests with zero free parameters confirm the framework to machine precision. Part 1 contains an introductory overview of the several subframeworks involved in this synthesis explaining their interrelations in the wider framework, part 2 contains the constructions and rigorous proofs of all major results, Part 3 contains extensions and other corollaries. Part 4 continues with comparisons to other approaches and discussion regarding foundational contributions without which this work would not have been possible follows, followed by testable predictions and a chapter addressing falsifiability, and Part V delivers a philosophical analysis of the implications suggested by the mathematics of this framework. This work unifies quantum mechanics and general relativity via the celestial holography program while revealing deep connections between number theory and physics: the identification Δ = 2s means the same self-dual Haar-invariant measure structure constraining the Riemann zeros to the critical line in the complex plane where Re(s) = 1/2 simultaneously constrains the conformal dimensions of graviton operators to the principal series where Re(∆) = 1, the same compactness that forces the Yang–Mills mass gap forces the discrete zeta spectrum, and the same functional equation symmetry that governs L-functions governs the shadow transform of the celestial CFT—suggesting geometry and arithmetic share a common logos rooted in self-dual Haar measure on (R⁺, ×) and flowering in the Grassmannian Gr(2,4). (shrink)

We present a comprehensive investigation into the minimal axioms required to derive quantum structure from classical computation. Through systematic analysis of multiple computational models—SK combinatory logic, reversible logic gates (Toffoli, Fredkin), reversible cellular automata, and lambda calculus—we establish that no form of computation, whether irreversible or reversible, can generate quantum structure. Our main results are: 1. The No-Go Theorem: Reversible n-bit gates are 2^n × 2^n permutation matrices that embed into the classical symplectic group Sp(2·2^n, R), not the unitary group (...) U(2^n). This theorem has been formally verified in Coq. 2. Axiom Identification: Using Generalized Probabilistic Theories (GPTs), we identify A1 (state space extension/superposition) as the unique primitive axiom that cannot be derived from computation. 3. Universality: Results hold across all Turing-complete computation models tested, establishing computation-model independence. These findings definitively answer the question "Can quantum structure be derived from computation?" with: No, without the superposition axiom A1. The gap between classical and quantum is precisely A1—and this gap is mathematically unbridgeable by computation alone. (shrink)

Following our previous work establishing that complex structure does not automatically emerge from SK combinatory logic, we investigate whether reversible computation provides the missing ingredient for quantum structure. Through systematic analysis of four computational models—reversible logic gates (Toffoli, Fredkin), continuous-time quantum walks, reversible cellular automata, and the non-commutativity of SK operators—we establish a hierarchy of quantum-like behaviors: Level 0 (Irreversible): SK computation—classical, deterministic Level 1 (Discrete Reversible): Toffoli/Fredkin gates, RCA—classical, embeddable in Sp(2N,R) where N = 2^n for n-bit gates Level (...) 2 (Continuous-Time): U(t) = exp(-iHt) evolution—interference emerges (note: i is introduced by hand) Level 3 (Quantum): Quantum circuits—superposition, entanglement Our main finding is that reversibility alone is insufficient for quantum behavior: discrete reversible gates on n bits are 2^n × 2^n permutation matrices that embed into the classical symplectic group Sp(2·2^n,R), not the unitary group U(2^n). While continuous-time evolution introduces interference patterns, it does not generate genuine superposition. We conclude that quantum structure requires additional axioms beyond any form of computation, discrete or continuous. (shrink)

In The Brain Abstracted, Chirimuuta argues against the “literal interpretation” of computational models. The literal interpretation understands computational models as providing true descriptions of neural processes. According to Chirimuuta, the literal interpretation involves endorsing the computationalism thesis and adopting a theory of computational implementation. The connection between these views, Chirimuuta argues, is what forces the computationalist to reckon with two significant metaphysical problems, what I call the "problem of implementation" and the "problem of multiple realizability." Here, I examine the connections (...) between the various views that Chirimuuta considers, and I argue that one who endorses computationalism can avoid both metaphysical problems. I do this by distinguishing computationalism from the literal interpretation and the implementation of computational models in neuroscience. Although this engages with Chirimuuta's project directly, it is helpful for those who wish to make sense of the intricacies of the computationalism thesis and computational functionalism. (shrink)

This paper proposes an expanded theoretical framework in which physical reality emerges from the transformation of logical structure into organized matter through information, cognition, resonance, and energy. A seven-stage cascade transforms logic into co-emergent cognition and matter. Logic defines possibility; information encodes it; perception interprets it; consciousness integrates it; resonance aligns it; energy enacts it; and co-emergent cognition and matter stabilizes it as structure and awareness. Drawing from information physics, predictive-processing neuroscience, and complex-systems thermodynamics, the framework treats coherence as the (...) measurable bridge between cognition and material form. It advances an informational-energetic ontology in which matter and mind are reciprocal expressions of structured coherence governed by logic. Though conceptual, the model identifies empirical anchors and testable implications linking entropy reduction, energetic efficiency, and multi-scale coherence in neural and physical systems. (shrink)

Here we complete and develop the subjects in [2], [3], [4] based on [1]. It should be mentioned again that the author knows TC* more near to the real situation of Computation rather TC even if we forget the fuzziness of time, the reason is different errors especially in computing time. By accepting this point the author tries to show the importance of such type of articles. This manuscript is the first version. In chapter 1 we define the major Complexity (...) classes and we discuss about. In chapter 2, we prove for μ which has a finite area, P_μ^*≠NP_μ^*. In chapter 3 we define some fuzzy functions μ such that P_μ^*=NP_μ^*. (shrink)

Can machines ever become conscious? A central debate in this context concerns the question whether consciousness requires biological states. Within this debate, there exists a fundamental dispute between two widely endorsed views: biological naturalism and computational functionalism about consciousness. Specifically, whereas biological naturalists hold that consciousness requires biological states, computational functionalists fiercly deny this. This fundamental dispute has hitherto remained unresolved, and we discuss several reasons why it is unlikely that it will be resolved anytime soon. Accordingly, we suggest that (...) both sides will likely remain in a dialectical stalemate regarding the fundamental dispute and, by extension, are unlikely to reach a consensus on positive verdicts about consciousness in AI. However, we suggest that progress can be made by investigating potential non-biological constraints on implementation. Such constraints can help reach a consensus between both sides to the effect that certain types of artificial systems are not conscious, if they fail to satisfy relevant constraints. If correct, a consensus on negative verdicts about consciousness in certain challenging types of AI is possible, even if both sides remain in conflict about positive verdicts about AI consciousness. (shrink)

This paper explores the development and evaluation of physics-specific large-scale AI models, which we refer to as large physics models (LPMs). These models, based on foundation models such as large language models (LLMs) are tailored to address the unique demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing insights from (...) physical theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability through testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining large physics models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models. (shrink)

This paper introduces the Structural Ethics Readiness Index (SERI v0.1), a falsifiable and auditable framework for evaluating the structural ethics of large language models (LLMs). Rooted in Emergent Necessity Theory (ENT), SERI defines a coherence proxy $\hat{\tau}$ and collapse threshold $\tau_c$ to capture when AI systems begin to drift, contradict themselves, or overreach under recursive or adversarial stress. -/- The paper contributes: • A principled set of indices (RCI, DSI, HSM, SOR, ETT) for structural evaluation. • A content-minimal JSON schema (...) and logging standard for reproducibility. • An open Python harness supporting both open-weight and closed-weight LLMs. • Synthetic pilot results demonstrating sensitivity to guardrail strength and cross-lingual brittleness (English vs. Arabic). • A replication checklist with code, schema, and validation steps. -/- SERI measures stability under recursion, adversarial drift, containment effectiveness, overreach, and externality traceability via five indices: Recursion Containment (RCI), Drift Susceptibility (DSI), Hysteresis Safety Margin (HSM), Symbolic Overreach Risk (SOR), and Externality Traceability Time (ETT). We define an ENT-aligned coherence proxy ˆ τ and estimate the collapse threshold τc using a change-point method guarded by observable coherence C(t). Implemented, is a reference harness (open/closed model adapters, content-minimal logs) and report synthetic pilot findings that illustrate expected directional sensitivity (guardrail strength, language/domain shift). Results include quantitative synthetic tables/figures and a replication checklist. Conclusions are illustrative and are not model rankings. (shrink)

This paper proposes Ethical Structurism, a falsifiable framework for detecting when AI-systems, especially large language models (LLMs) enter a regime of recursively stable symbolic behavior. The framework defines an information-theoretic coherence ratio τ(t) and a resilience ratio κeffR (t) to identify phase-like transitions in symbolic consistency without making any claims about agency, consciousness, or moral status. This paper based on Emergent Necessity Theory principles, adopts a working hypothesis that a critical coherence threshold signals symbolic persistence. The framework is designed to (...) be easy to falsify, implemented via open-source tools, and positioned as a complement to existing AI ethics practices. If experiments do not corroborate threshold behavior, the framework should be revised or discarded. (shrink)

The concept of time in physics remains fundamentally fractured, treated as a dy namic geometric entity in general relativity while retaining a static, background role in quantum mechanics. Building upon the hypothesis of Recursive Entropic Time (RET)—a model where the local rate of time is suppressed by quantum informa tional load—this paper presents the first comprehensive experimental tests of the theory. We subject the core relation, τ = (1 + κS)−1, to rigorous scrutiny by ana lyzing published data from two (...) independent, state-of-the-art quantum experiments. First, an analysis of entanglement dynamics in a multi-qubit processor [Koh et al., 2022] reveals a significant suppression anomaly that is well-described by the model, providing a first estimate for the coupling constant, κ. Second, we re-interpret the Quantum Zeno Effect as a direct manifestation of this entropic time dilation. A detailed analysis of data from a superconducting qubit experiment [Harrington et al., 2017] allows for a second, independent calibration of κ. We demonstrate that an initial order-of-magnitude discrepancy between these two values is substantially resolved by a more physical model of informational load that accounts for measure ment inefficiency—a striking convergence of evidence. This work provides strong support for the hypothesis that time possesses a form of inertia, resisting its own passage in proportion to the complexity of the quantum information it contains. We conclude by designing a definitive, falsifiable experimental protocol to provide a "smoking gun" test, solidifying the link between quantum information and the emergent, dynamic nature of time. (shrink)

In a series of drafts, under the name of “Fuzzy time and the impact of it on Science” the author reaches to define “Theory of Fuzzy Time Computation”. Here, first we explain the importance of this theory (four reasons in redlines), second how to develop it.

Blockchains today validate consensus over sequences of events without assessing whether each addition contributes meaningfully to systemic structural persistence. I propose a formal framework for Distributed Coherence Ledgers (DCLs), systems that embed coherence gradients, curvature constraints, and recursive arbitration as first-class validation criteria. Drawing from recursive coherence theory, I define a bounded, scale-invariant metric quantifying the recursive reinforcement of alignment across blocks, replacing purely probabilistic finality with coherence-weighted consensus. Each block includes coherence yield, compression delta, and entropy contribution, allowing validators (...) to select sequences that maximize cumulative coherence while sustaining curvature within viability bounds. This approach reframes blockchains not as passive append-only logs but as adaptive substrates capable of encoding the persistent structural relevance of transactions over time. The resulting architecture aligns with infrastructural computing, advancing cognitive invisibility and seamless integration into societal workflows. I derive formal validation functions, consensus selection equations, and bounded curvature constraints, situating this paradigm within broader coherence-centric epistemology. Distributed Coherence Ledgers represent a foundational shift in how distributed systems can maintain long-term viability under entropy, contradiction, and recursive stress. (shrink)

In a series of drafts, under the name of “Fuzzy time and the impact of it on Science,” we try to show how fuzzy modeling of time could impact science especially Complexity theory and Physics. Throughout this paper, by introducing Generalized Surprise Exam Paradox (GSEP) we show the problem is more general than concept of time.

This work extends Emergent Necessity Theory (ENT) to resolve fundamental questions of time-asymmetry and scale-dependence through hierarchical nesting of timeless algebraic constraints. We derive time as an emergent property of substructure embedding, ground necessity in topological invariance, and present four experimentally falsifiable predictions testable with current technology. ENT’s core principles (τ- coherence, structurism) are preserved while addressing previous weaknesses in the framework. All mathematical claims are designed for experimental verification with existing laboratory capabilities.

Computing has achieved global distribution in physical terms, yet it has not become infrastructural in the same sense as electricity, water, or roads. The distinction lies in accessibility: physical infrastructure requires minimal cognitive translation to engage, whereas computational systems demand symbolic fluency, abstraction, and literacy. This paper argues that computation will only reach infrastructural status when its interface layer collapses from symbolic mediation into direct, semantic engagement. A true infrastructure is not merely present; it is universally usable, intuitively operative, and (...) embedded into daily behavior. Semantic agents, conversational systems, and gestural inputs represent the transitional mechanisms through which this collapse will occur. Once computing becomes ambient and cognitively frictionless, it will function as a utility, an extension of human intention rather than a tool requiring translation. This shift marks the emergence of cognition as infrastructure: a foundational layer that enables recursive cultural synthesis, real-time adaptive thinking, and expansive societal reorganization. The value of computing lies not in intelligence replication but in the systematic reduction of the cost of coherent thought. (shrink)

With increasing development and widespread interest in artificial intelligence and robotics over the last several years, novel solutions are being considered for addressing specific problems in a number of domains. In particular, caring for elderly individuals in regions where human labour is insufficient is of special interest. One aspect of this involves an ability to communicate and socialize with people in a familiar and humanlike manner, with an ability to understand human emotion through behavioural cues and language. Furthermore, social robots (...) should be able to act with empathy if we are interested in using them for tasks related to eldercare and healthcare, as empathy involves the adoption of another person’s perspective. This work investigates whether social robots are capable of meeting these requirements, and ultimately demonstrates that they cannot exhibit genuine acts of empathy. By investigating one robot in particular named iCub, it becomes apparent that it will not be able to understand human emotions given its cognitive architecture. iCub’s framework lacks the kind of affective regulation observed in people and animals, and as such, is unable to experience its own form of emotions. Consequently, iCub is incapable of understanding what emotions are and what they mean to humans. For social robots, an alternative solution which relies on experiences of emotions should instead be pursued. The cognitive architecture developed by Pentti Haikonen is preferable because it is modelled on biological functionality, creating better analogues of living beings than previous efforts in AI. By creating associative networks of neurons which establish connections between stimuli and their effects on the robot’s body, it is able to learn what a stimulus means for its continued functioning. By using signals representing analogues of pain and pleasure, emotional information learned by the robot is grounded on how stimuli impact the robot’s body. This architecture provides a foundation for empathy since the robot is able to appeal to its own experiences of pain and pleasure to understand the meanings of behavioural cues exhibited by humans. (shrink)

There appear to be some qualitative differences between the conceptual repertoire of humans and that of other animals. We propose that the mental operation of Lambda Abstraction may shed some light on this distinction. More specifically, we claim that humans and only humans make use of mental representations constructed with the rule of Lambda Abstraction, which enables them to entertain concepts that can be used for entities that are not necessarily within their domain of experience. In addition to defining new (...) types of concepts, Lambda Abstraction has played a crucial role in unlocking the potential for semantically consequential Internal Merge and quantification. This paper highlights the fact that research on language evolution should focus more on the transformative cognitive consequences of the interface between syntax and thought systems. (shrink)

Infinite time Turing machines extend the operation of ordinary Turing machines into transfinite ordinal time. By doing so, they provide a natural model of infinitary computability, a theoretical setting for the analysis of the power and limitations of supertask algorithms.

The Computational Model of Mind (CMM) conceptualizes cognition as computational processes, modeling mental operations through algorithmic manipulations of symbolic or distributed representations. This framework bridges psychology, neuroscience, philosophy, and computer science, providing a unified lens for understanding the mind. Its symbiotic relationship with artificial intelligence (AI) has accelerated advances in cognitive science and the development of intelligent systems, from neural networks to autonomous agents. This article offers a comprehensive analysis of CMM, tracing its historical evolution from Turing's foundational ideas to (...) modern deep learning paradigms. It delineates CMM's theoretical underpinnings, operational mechanisms, and strengths in simulating complex cognitive functions, while critically addressing limitations, such as its struggles with consciousness, embodiment, and ecological validity. The paper explores CMM's intersections with AI, highlighting applications in cognitive modeling, natural language processing, robotics, and neurotechnology. It also examines ethical challenges, including algorithmic bias, privacy concerns, and the implications of machine autonomy. Looking ahead, I propose future directions, emphasizing interdisciplinary collaboration, neurobiologically inspired algorithms, and emerging technologies like quantum computing. This synthesis affirms CMM's transformative role in unraveling the nature of mind and machine intelligence, and an innovative research at the nexus of cognition and technology. (shrink)

Các trào lưu như hiện tượng “túi mù”, hầu hết các xu hướng tiêu dùng dựa trên nền tảng và được điều khiển bởi thuật toán, dù thúc đẩy tăng trưởng kinh tế và tạo việc làm, hoàn toàn thiếu tính bền vững—chúng góp phần làm gia tăng đa khủng hoảng hơn là mang lại lợi ích. Những vấn đề này bao gồm: (i) khủng hoảng giá trị; (ii) khủng hoảng sức khỏe tinh thần; (iii) khủng hoảng hệ sinh thái. (...) Giới trẻ có thể dành hàng giờ để xem các influencer trên TikTok và Instagram phản ứng với những gì họ tìm thấy trong túi mù. Giá trị sử dụng thực sự của những sản phẩm ấy là gì? Còn hàng triệu người đang trong độ tuổi hình thành nhân cách—như trẻ em, thanh thiếu niên và người trẻ—đắm chìm trong thói quen xem, lướt và mua sắm không kiểm soát, cuối cùng rơi vào tình trạng “quiet quitting”, tuyệt vọng, suy giảm nhận thức, trầm cảm, thậm chí ý nghĩ tự tử thì sao? Chúng ta có thể làm gì trước lượng rác thải nhựa khổng lồ và lượng khí nhà kính phát sinh từ sản xuất và tiêu thụ trong các ngành công nghiệp văn hóa này? Trong bài viết này, chúng tôi lập luận rằng sức mạnh của AI có thể được khai thác để cung cấp giải pháp cho những vấn đề đó. Trước hết, chúng ta phải nhìn nhận AI như một tác nhân đảm bảo công bằng trong tiếp cận thông tin. Thứ hai, sự lên ngôi của các “nhà khoa học nhân dân” (citizen scientists) AI và quy trình làm việc theo cơ chế tác nhân AI có thể hạ thấp rào cản để thúc đẩy khoa học công dân vì các mục tiêu sinh thái. Cuối cùng, AI có thể được tận dụng để hình thành và thúc đẩy cam kết cộng đồng về trách nhiệm và đạo đức liên ngành trên ba lĩnh vực: con người, môi trường và công nghệ. (shrink)

Trong kỷ nguyên bùng nổ của trí tuệ nhân tạo (AI), việc hiểu biết đúng và kịp thời về bản chất của AI trở thành yêu cầu cấp thiết đối với cả cá nhân lẫn quốc gia nhằm tận dụng hiệu quả các cơ hội và giải quyết được các thách thức cũng như yêu cầu của thời đại. Trên cơ sở đó, bài viết nghiên cứu khái niệm “vô thức thuật toán” (algorithmic unconscious) như một khái niệm chứa đựng (...) những yếu tố tiềm ẩn trong hệ thống vận hành của AI dưới lăng kính tâm lý học phân tích của Carl Gustav Jung (1875- 1961). Phân tích tập trung vào các cổ mẫu của trường phái Jungian như “Kẻ Tinh Quái” (Trickster) và “Cái Bóng” (Shadow), qua đó làm rõ cách những hình tượng cổ mẫu này được phản ánh trong hoạt động của thuật toán hiện đại. Đồng thời, bài viết cũng phân tích hiện tượng “phóng chiếu” trong mối quan hệ giữa AI và con người thông qua minh họa về việc con người vô thức gán các kỳ vọng hay nỗi sợ hãi của bản thân lên AI. Qua đó, vấn đề “cá nhân hóa giả” (pseudo-individuation), trong đó AI tạo ấn tượng như một chủ thể có nhân cách riêng nhưng thiếu ý thức tự thân và sự thiếu hụt khả năng tự phản tỉnh ở AI được làm rõ. Thông qua những nội dung trên, bài viết đề xuất các định hướng thiết kế giúp cho AI tích hợp yếu tố tự nhận thức và các giá trị nhân bản, nhằm tăng cường tính minh bạch, củng cố niềm tin và phát triển mối quan hệ giữa con người và máy móc vì lợi ích chung của xã hội. (shrink)

The Decentralized Key Protocol (DKP) is a hybrid framework designed to enhance privacy, security, and user control in social networking applications transitioning from centralized to decentralized architectures. DKP combines centralized storage for scalability with blockchain-based access control and integrity verification. Files are stored on centralized servers with random tags, anonymizing user data, while key-tag-hash tuples on the blockchain ensure secure access and file integrity. A permission request smart contract enables secure file sharing, where followers must request access for each file (...) view, with the contract verifying their status and granting one-time access to the tag and hash. This paper presents DKPs architecture, evaluates its benefits and challenges, and demonstrates its suitability for privacy-conscious social platforms. DKP offers a practical balance between decentralization, usability, and performance, addressing critical issues in data privacy and trust. (shrink)

Cognitive science gives computational explanations of the brain. Philosophers have treated these explanations as if they simply claim that the brain computes. We have therefore assumed that to understand how and why computational explanation works, we must understand what it is to compute. In contrast, I argue that we can understand computational explanation by describing the resources it brings to bear on the study of the brain. Specifically, I argue that it introduces concepts and formalisms that complement cognitive science's modeling (...) goals. This allows us to understand computational explanation without having to debate what it is to compute. (shrink)