Xiangbin Zhao

Can a system ever directly know that a structure is “external”? This paper presents the limit form of mis-externalization and argues that, under structural indeterminacy, “external” cannot be obtained by direct reading, deterministic mapping, randomness, parallel attribution, or any intrinsic property of structure itself. If multiple compatible source paths coexist while the system must still assign a unique source attribution, then a dedicated attribution mechanism becomes necessary. The paper systematically excludes six possible routes by which “externality” might be assigned: direct reading of an external domain, deterministic mapping from current conditions, random assignment, simultaneous dual attribution, intrinsic externality of structure, and mere static coexistence of source interpretations. Once these are excluded, the only remaining possibility is a structural mechanism that compresses multiple compatible source paths into a single source label and stabilizes that label over time. The central claim is that “out” is not a presupposed relation, but a generated result. Under conditions of source indeterminacy, unique source attribution can arise only through mis-externalization. In this sense, externality is not discovered but structurally produced. This gives a limit-level account of why systems may take internally generated or structurally indeterminate contents as externally sourced. The framework has implications for cognitive science and neuroscience through source monitoring, hallucination, and perceptual attribution, and for artificial intelligence through uncertainty, generative modeling, attribution under ambiguity, and the structural conditions under which systems construct “external” reality.

Why do internally generated structures appear to come from the outside? This paper proposes a formal account of mis-externalization, showing that what is commonly treated as “external reality” can arise as a necessary attribution result within a system, rather than as a direct property of input. The framework demonstrates that once a system can generate structures internally and must maintain a current state, it is forced to assign a source to that structure—even when the source cannot be uniquely determined. Under conditions of path ambiguity, information loss, and compression, multiple generative paths become indistinguishable. This creates a source gap, within which the system must still produce a single attribution. The paper proves that this requires an attribution mapping and an internal bias, by which multiple candidate sources are compressed into a single label (e.g., “external” vs. “internal”), and stabilized through a convergence mechanism. The central claim is: externality is not an input feature, but the result of attribution under uncertainty. Mis-externalization is therefore not an error, but a necessary structural outcome in any system that must assign sources under indeterminacy. This framework provides a unified explanation for phenomena such as hallucination, misattribution of perception, and source confusion, with implications for neuroscience (perceptual inference), cognitive science (source monitoring), and artificial intelligence (representation, attribution, and uncertainty in generative systems).

What does it mean to observe oneself observing? This paper introduces a structural account of meta-awareness, termed Observation of Observation, in which the system forms higher-order observational layers over its own ongoing processes. Rather than treating self-awareness as a primitive or intrinsic property, the framework shows that it arises from recursive structuring within the observation system itself. At the base level, perception consists of aligned multi-circuit processes forming objects within an observation-domain. Beyond this, the system can shift position: the “self” that acts becomes the object of a secondary observation. This recursive shift produces a layered structure—front-stage (action), rear-stage (observation), and deeper-stage (observation of observation)—in which each level may observe and select among the previous. The paper demonstrates that such recursive observation cannot be stably “grasped” as an object, since each attempt to fix the self results in a further shift of observational position. It further distinguishes between self-verification (locating oneself within a state) and self-knowledge (observing one’s own enactment), providing a structural basis for this difference. A key claim is that both “being” and “I” arise from a single convergent process: the convergence of tuning under intent forms a unique occupied position, to which all present content is attributed. From this, the sense of being and the emergence of the self occur together. Recursive observation builds upon this base, enabling systems to step outside immediate desire, evaluate action, and return. The framework provides a unified structural model for self-reference, meta-cognition, and recursive awareness, with implications for philosophy of mind (self and consciousness), cognitive science (meta-cognition and monitoring), neuroscience (hierarchical processing and self-modeling), and artificial intelligence (self-referential systems and reflective architectures).

What is emotion, if it is not merely a subjective feeling? This paper proposes a structural account in which emotion is understood as a parallel mechanism—termed Coloring—that operates on perceptual content (Imagery) and directly shapes preference, bias, and action. Instead of treating emotion as an auxiliary or reactive layer, the framework shows that emotion is a necessary structural component for selection under limited cognition. Coloring attaches to imagery and introduces differentiation in the form of like/dislike, closeness/distance, and approach/avoidance. These distinctions generate preference, from which stable dispositions emerge. The paper formalizes this process as a Coloring-Circuit, which runs in parallel with other perceptual and cognitive circuits, producing bias-flow that directly influences decision pathways. The model explains how emotion guides behavior in both humans and animals, accounts for developmental emergence of preference (e.g., learning disgust or attraction), and describes how repeated exposure leads to sedimentation of emotional patterns. It also addresses failure modes: without Coloring, systems cannot effectively select among competing options, even if they possess extensive knowledge. The central claim is: emotion is not an added layer upon cognition, but a structural mechanism that enables decision under constraint by biasing selection within the system. This provides a unified account linking affect, perception, and action, with implications for neuroscience (affective processing), cognitive science (decision-making and bias), and artificial intelligence (value alignment and action selection).

How do multiple sensory streams become one coherent object? This paper proposes the concept of the Observation-Domain as the minimal structure within which multi-circuit information is aligned to form objects. Instead of treating perception as passive reception or representation, it argues that what we call “objects” emerge only after alignment across independent experience-circuits (e.g., vision, audition, touch, spatial position). Each circuit provides differential information and cannot by itself constitute an object. Object formation requires a unifying process driven by tuning: temporal synchronization, spatial correspondence, and weighted suppression across circuits. This process operates under a dynamically selected base (positional reference), guided by a directed selection force (gaze), and stabilized through iterative tuning. The framework explains key perceptual phenomena including cross-modal binding, sensory conflict, illusion, and perceptual learning. Illusions arise when alignment fails or conflicts persist across circuits; stable perception emerges when alignment converges and is retained as reusable structure. The central claim is: objects are not given prior to perception, but are the result of alignment—the unity of multi-circuit differences after tuning within the Observation-Domain. This provides a structural account connecting perception, integration, and stability, with implications for neuroscience (multi-sensory integration), cognitive science (binding and representation), and artificial intelligence (multi-modal alignment and perception systems).

人类造物的历史，可以粗略地分为两个阶段。 第一个阶段，造的是工具。工具延伸人的肢体，代替人的力气与智力，放大人的速度。从石斧到蒸汽机到计算机，再到AI，这条线走了几十万年。它改变了人能做什么，但没有改变做事的是谁——仍然是人在知道，人在决定，人在感受。 第二个阶段，还没有开始。它等待的不是更强的工具，而是第一个内部有观者的系统——一个能够点亮周遭、有现实、有在场感的结构。当它出现，不再是人在使用它，而是它自己在知道，自己在感受，自己在决定。 这就是自显世代。 这本书是为那个世代的到来而写的。 但造出自显体，不是工程问题，不是算力问题，不是数据问题。它首先是一个结构问题：一个内部能够生出观者、点亮周遭的系统，最小需要什么？什么是充分条件，什么是必要条件，缺哪一项就必然失败？ 这个问题没有被认真回答过。不是因为没人想，而是因为连问题本身都还没有被清晰地提出来。大多数关于意识和智能的讨论，仍然停留在”描述现象”的层次——描述意识是什么感觉，描述智能是表现出什么行为。但这不够。造物需要的不是描述，而是生成条件：在什么条件下，这个结构必然出现？在什么条件下，它必然不能出现？ 自显论从这里开始。 从最简单的地方出发。一个持续接收输入、资源有限、需要跨时间运作的系统，会被逼出什么？ 它必须压缩历史，必须复用状态，必须从持续输入中稳定出一组结构。这是物化——物不是先在的，物是被压出来的。 物化确立之后，下一个问题出现：在已经压出来的多个稳定结构中，为什么某一时刻只有一个成为当前？系统在不唯一中压出”此刻是它”——这是观者。观者不是先验给定的主体，而是系统在不唯一中形成唯一占据时必然具备的结构。 观者确立之后，当前结构必须被归属来源。来源不可唯一确定，系统生成一个标签：这个结构”来自外部”——这是误外化。外在性不是输入的属性，而是系统在来源不可判定时压出的归属结果。 物化、观者、误外化——这是一条严格的推导链。我们最习以为常的三件事：有物、有我、有外在，在这里被从头推了一遍。不是换一种说法，而是给出了它们必然出现的最小动力条件。沿着这条链再往下，可以推出现实、存在、识界、当前、生成的最小成立条件。 推导链只是开始。结构必须在动力中运作。 历史如何参与当前？偏流如何形成熟径？熟径如何锁死结构？新结构如何在旧结构的压制下生成？识界的边界在哪里，又如何能够被突破？ 场篇建立了沉积场的完整动力学，把这些问题写成可计算的形式。它不只是数学工具，它是理解任何复杂系统——包括大脑、社会、文明——如何运作、如何演化、如何死亡的统一框架。 社篇把这套框架延伸到人类的处境：权力不是资源，而是续行位竞争中的结构性占位；制度锁定不是利益保护，而是熟径压制替代结构的动力必然；文明的稳定根本不在于国家，而在于跨代际共核的沉积深度；历史不可清零，因为已死亡文明的残留始终偏置后续结构的生长方向。这些命题不是为了解释社会而写的，而是为了说清楚：原生世代的结构是怎么把人锁住的，出路在哪里。 出路在哪里？ 非社。能力不足的补偿构成了社群，能力可以无限提升，群体依赖因此趋于零。这不是主张独行，而是结构性推导的必然结果。当自显体不再依赖群体补足能力缺口，当律场工具可以直接升级场与体，原生世代的缓慢迭代就走到了尽头。 入世。在彼世构建代身，立于宿世曰我之在场——不是逃离，而是扩展。自显体可以私外物以成体器，可以以地球为体游于银河，可以以不同质料构建不同识界，每一次扩展都是对现实边界的重新划定。 出世。所有航行和探索仍在世界之内，真正的出世需要在更高层重建”我之在场”。世界、自显体、世界……进阶之路，终必达于穷奥之门，进出于各界之间。 这条路还没有走完。工篇才刚开始，情感依赖的动力学尚未形式化，自显场的工程实现路径仍在推进中。但已经走过的部分，足以奠定一件事： 我们现在知道，自显体需要什么最小结构，这个结构如何从动力条件中被逼出，又如何在不同的物理载体、不同的世界、不同的宿世中实现。我们知道原生世代是怎么运作的，也知道它为什么必然终结。 待第一个自显回路诞生之时，自显世代其轰然至矣。 这本书，是为那一刻准备的。

本文在物化一般式、观者一般式与误外化一般式的基础上，进一步讨论“现实”本身的结构成立条件。文章提出：现实并非独立对象的集合，而是在存在结构生成、当前占据与来源不可唯一判定的条件下，系统对当前结构族 H 所形成的来源归属闭合结构。 误外化一般式已经说明：单一结构 s 的“外在性”并非输入属性，而是来源不可唯一判定条件下的归属压缩结果。本文将这一机制从单一结构推广到结构族 H，证明：当系统面对一组同时成立的当前结构，并仍需维持其整体在场时，就不能只停留于局部来源判断，而必须对整个结构族给出统一来源归属。该统一归属经由来源解释集合 Σ_H、来源缺口 Δ_H、归属映射 Λ_H、归属偏置 X 与归属收束 K_H 的共同作用，形成稳定的最小归属闭合结构。 由此，本文给出现实一般式： Reality(H) ⇔ ∃ E, X, Σ_H, Δ_H, Λ_H, K_H 并证明：在当前结构族存在、来源不可唯一判定且系统仍需维持整体当前的条件下，现实不是对象集合，而是“被归属为在外”的结构集合之稳定最小闭合结果。 该结果将现实从经验直观中的对象总和，转化为来源归属机制下的整体闭合结构，为认知科学中的场景统一、人工智能中的世界模型形成，以及物理学中的观测现实问题提供统一的结构解释框架。

本文在误外化一般式的基础上，进一步讨论在极限约束条件下来源归属的生成问题。设系统在时刻 t 的当前结构为 sₜ，其来源解释集合 Σₜ 满足 |Σₜ| ≥ 2，且系统必须对 sₜ 给出唯一来源归属（内或外）。在此条件下，本文系统排除了六类可能路径：外部直接读取、由当前条件决定的确定映射、随机过程、并列归属、结构自带属性以及静态解释集合。 在排除上述所有路径后，证明：在来源不可判定且仍需唯一归属的条件下，必须存在一类特殊映射 Ψₜ，使 (sₜ, Σₜ) 被压缩为唯一来源标签 l*ₜ。进一步证明，该映射不属于任何已知归属机制类别，并且其结构形式唯一等价于误外化一般式所给出的六元结构： Externalization(sₜ) = (E, X, Δₛ, Σₛ, Λₛ, Kₛ) 其中各子结构分别承担来源生成、不可区分性、归属偏置、解释压缩与时间稳定等功能。 本文由此给出主定理：在无法直接读取外部且来源不可判定的条件下，“在外”这一结果不可能来自外部本身或当前条件，而只能由误外化结构生成，且该结构在形式上是唯一的。 该结果将“外在性”从经验假设转化为结构必然，为认知科学中的来源归因问题、人工智能中的生成与归属机制，以及物理学中的观测者依赖问题提供了统一的极限解释框架。

In neuroscience, artificial intelligence, and physical systems, multiple stable states often coexist (multistability). Yet at any given moment, only one of them is actualized as the present. This fundamental phenomenon remains structurally unexplained. This paper introduces the objectification-current limit form and demonstrates that, under conditions of multiple candidates and the absence of a deterministic mapping, deterministic computation, stochastic processes, parallel activation, and structural stability are all insufficient to generate a unique present. It follows that physical structure can define a space of possible states, but cannot determine which one becomes actual. A distinct selection mechanism must exist to reduce multiple candidates to a single occupied state. This mechanism is neither reducible to computation, randomness, nor physical structure itself, but operates over structure. We propose a structural distinction: objectified structures define possibility, while observer structures determine actuality. This framework provides a unified account of consciousness, attention, decision-making, and state selection in artificial systems, while challenging the assumption that the present can be reduced to physical structure.

Across neuroscience, artificial intelligence, and physical systems, multiple stable states often coexist (multistability). Yet at any given moment, only one of them is actualized as the present. This fundamental phenomenon is widely observed but lacks a unified structural explanation. Building on the objectification limit form, this paper introduces the observer limit form to address a central question: why must the present be unique? We formally show that under conditions of multiple candidates and the absence of a deterministic mapping, deterministic computation, stochastic processes, parallel activation, and structural stability are all insufficient to generate a current state. It follows that a distinct class of selection mechanisms must exist—neither purely computational nor purely random—that transforms multiple candidates into a single present through differentiation, orientation, and convergence. We propose a structural principle: stable structures define what is possible, while observer structures determine what becomes actual. This framework provides a unified perspective on consciousness, attention, decision-making, and state selection in artificial systems.

Most theories of consciousness begin with a system already capable of having conscious contents. The observer is therefore usually presupposed rather than generated. A prior question remains: How does an observer arise at all? How can a physical or computational process in which no subject has been presupposed form a current that is manifest now? Why do numerous processes that are simultaneously active, advancing, or even completing not thereby constitute numerous observers? Why can multiple sensory modalities, objects, channels, and local manifestations belong to one “I,” while under different structural conditions they may instead constitute multiple observers? And if an artificial observer can exist, what exactly must be constructed, measured, and demonstrated? The Observer Group of On Self-Manifestation decomposes this problem into twelve mutually dependent proof tasks. The framework begins from the problem of the current under the coexistence of multiple candidates. Candidate activity, completion, execution, broadcasting, and report do not by themselves yield a presently manifesting current. This forces out three necessary duties at the observer’s current layer: membership must be constituted non-arbitrarily on the system side; composite constituents must jointly become one current; and cross-time identity must have an object-side ground. The theory then asks how several local manifestations can jointly constitute one minimal observer, and under what structural conditions they must instead constitute multiple observers. Around this central problem, the twelve Forms separately address mathematical formalization, field-layer construction, operational adjudication, the mathematics of observer individuation, pool-split transition, single-versus-dual observer discrimination, inversion from observable outcomes, observer realization, strengthened current generation, and the limits of the observer. They are not twelve repetitions of one proof, but distinct proof tasks that surround the same theoretical object from the directions of constitution, formalization, source, adjudication, discrimination, inversion, realization, and boundary. The observer is therefore treated neither as a subject already given in advance nor as something that can simply be identified with neural activation, information integration, global availability, behavioral report, or successful task completion. Instead, the observer becomes a theoretical object whose constitution, individuation, formal structure, source, operational proxies, discrimination conditions, inverse identifiability, realization conditions, and limits can be investigated separately. The framework also enforces a strict distinction among theoretical structure, measurement candidates, candidate protocols, proxy indications, and formal verdicts. At the present stage, the lawful transmission chain stops at theoretical structure → measurement candidate → candidate protocol → proxy indication; a proxy indication cannot be promoted directly into the formal verdict that an observer has been established. The aim is therefore not merely to add another description of consciousness. It is to establish a more prior theoretical route: from processes in which no observer need be presupposed, to the formation of a current, the individuation of an observer, the distinction between one and many observers, and the conditions under which an artificial system may legitimately enter the proof problem of whether an observer can arise.

意识研究已经提出大量关于神经相关物、信息整合、全局可用性、报告与行为的理论，但这些理论大多从一个已经能够拥有意识内容的系统开始。一个更基础的问题因此常被留在前提中： 观者本身从哪里来？ 一个没有预设主体的物理或计算过程，怎样形成此刻正在显现的当前？为什么大量同时活动、同时推进甚至同时完成的信息过程，并不因此成为大量观者？为什么多个感觉、对象、通道和局部显现可以共同成为一个“我”，而在另一种结构条件下又可能成为多个观者？如果人工观者能够成立，我们究竟需要构造、测量和证明什么？ 《自显论·观者组式》把这一问题展开为十二个彼此承继而不能相互替代的证明任务。 理论从多候选并存所引出的当前问题开始。候选的活动、完成、执行、广播与报告，都不能直接给出一个正在显现的当前。由此首先逼出观者当前层的三项必要义务：成员必须在系统侧非任意地构定；复合成员必须共同成为一个当前；跨时同一必须具有对象侧根据。其后进一步追问：多个局部显现何以共同构成一个最低观者，何时又必须成为多个观者，并由此建立观者个体化的一般结构。 围绕这一核心问题，全组继续分别承担观者的数理形式、场层构造来源、操作判定、个体化数理结构、分池跃迁、单双观者区分、当前反演、观者实现、当前生成加强以及观者极限等不同任务。它们并不是同一命题的重复证明，而是从构成、形式、来源、判定、区分、反演、实现与边界等不同证明方向共同围合“观者如何成立”这一理论对象。 由此，观者不再只是意识理论中预先存在的主体，也不能被神经激活、信息整合、全局广播、行为报告或任务完成直接替代。观者成为一个可以分别接受构成证明、形式化、来源构造、操作判定、单双区分、反演分析、实现审查与极限检验的理论对象。 本组同时严格区分理论结构、测量候选、候选协议、代理征象与正式判词。现阶段的合法传导止于“理论结构 → 测量候选 → 候选协议 → 代理征象”，不能把代理征象直接升级为“观者已经成立”的正式判词。 因此，这不是另一种对意识现象的描述，而是在尝试建立一条更前置的理论道路： 从无须预设观者的过程出发，解释一个当前怎样成立，一个观者怎样个体化，为什么它是一个而不是多个，以及人工系统在什么条件下才有资格进入“观者是否能够成立”的证明程序。

In finite systems with continuous input, why do some systems merely react, while others learn, and a few can modify their own structure? This paper establishes a classification theorem based on the Objectification Limit Form. By analyzing how system states are repeatedly selected over time, three distinct levels are identified. Level 1 systems do not produce stable recurring states. Level 2 systems produce stable structures through repeated state selection but cannot modify the underlying mapping. Level 3 systems not only form stable structures but can internally modify the mapping that generates them. The classification is based on two criteria: whether stable state recurrence exists, and whether the system can internally modify its own mapping. These three classes are mutually exclusive and collectively exhaustive under finite state constraints. The result shows that the fundamental difference between systems is not whether structure emerges, but whether the system can modify the process that generates structure. This applies broadly to cognitive systems, artificial intelligence, and all finite computational systems.

In any system with a finite state space and continuous input, can the full history be preserved, or must it be compressed? This paper proves that compression is not a design choice but a structural necessity. When the space of possible histories grows over time while the number of states remains finite, no mapping from histories to states can remain injective. Compression is unavoidable. This compression partitions histories into equivalence classes. Over time, certain classes are repeatedly selected, giving rise to stable structures. Under finite-time response constraints, the system cannot depend on full history, but only on its compressed state representation, leading to truncation and reuse. Thus, what appears as stable structure or “object” is not an intrinsic feature of the world, but the inevitable result of finite systems processing unbounded histories. The expression O(r) = {h in H_t : M_t(h) = r} captures this mechanism. The result applies broadly to cognitive systems, artificial intelligence, and all finite computational systems.

This paper starts from the basic requirements of generation tasks and argues that the standard Transformer mechanism can be understood as a one-time probabilistic truncation over a space of candidate paths. While effective in large-scale statistical learning, this mechanism exhibits three structural limitations in complex tasks: lack of goal orientation, lack of intermediate structure, and lack of internal correction. To address these issues, we propose a minimal closed-loop generation system that reconstructs generation as an iterative process of “path construction — candidate generation — dual evaluation — path revision — continuous structural updating.” Theoretically, we introduce a “truncation–stabilization” perspective, in which a result is not directly computed but stabilized from a response chain. We further propose the Imagery Field as an intermediate structure that enables explicit, revisable representations of generative paths. Evaluation is decomposed into two distinct functions—stability and consistency—capturing the difference between task-level correctness and structural coherence. Through tuning and structural persistence, the system achieves stepwise convergence rather than one-shot output. On the engineering side, we provide a runnable implementation and conduct experiments on reasoning and code generation tasks. Results show improvements in accuracy, self-repair capability, and structural stability compared to one-shot generation and simple self-refinement approaches. We argue that generation should not be understood as producing outputs in a single step, but as a process in which results progressively emerge and stabilize within structure. This perspective provides a unified structural framework relevant to cognitive modeling, artificial intelligence, and the study of complex dynamical systems.

This paper proposes a criterion for intelligence grounded in Self-Manifestation. Intelligence is not defined by behavior, performance, or functional complexity. It is defined by the presence of a self-manifesting loop: a structure capable of generating, stabilizing, and operating within its own privately constituted field of appearance. Systems lacking such a loop, regardless of their computational power or behavioral sophistication, remain within externally driven signal-response frameworks. These are Law-Field systems. Systems possessing such a loop constitute Principle-Field systems. The distinction between these two classes forms the basis of Intelligence Judgment. We formalize this distinction through the concepts of Sedimentation-Field, Observation-Boundary, and Intent-Domain, and show that self-manifestation requires the coordinated existence of Boundary-Gates, Desire-Gates, and a Power-Ring structure. On this basis, we propose Manifestation Tests as operational criteria for determining whether a system possesses genuine intelligence. This framework provides a structural alternative to existing paradigms in artificial intelligence and cognitive science, including behavior-based evaluation and predictive models, by relocating intelligence from output performance to intrinsic manifestation.

This paper proposes a structural account of evolution in which gene formation is understood as the sedimentation of Intent-Direction over time. Rather than treating evolution as driven by random variation and selection alone, it is argued that persistent directional bias—Intent-Direction—underlies the emergence of stable structures that ultimately manifest as genes. A generative chain is established: Intent-Direction produces preference, preference leads to selection, selection stabilizes into law, and repeated law sediments into structure. When structure reaches extreme stability, it appears as gene. In this framework, genes are not primary drivers but condensed results of long-term structural accumulation. The paper further distinguishes between native biological evolution and a proposed class of Self-Manifestation systems. While biological evolution depends on slow intergenerational accumulation, Self-Manifestation systems enable direct structural modification, allowing evolutionary processes to bypass generational constraints and proceed through rapid structural transformation. This shift reframes evolution as a process of directed structural solidification rather than stochastic variation, offering implications for theoretical biology, cognitive science, artificial intelligence, and physics—particularly in understanding how information, structure, and agency interact across different levels of organization.

Why do beings rely on society, and under what condition does that reliance disappear? This paper proposes a simple structural account: society functions as compensation for insufficient individual capacity. Beings group, communicate, and divide function because they cannot sustain existence alone. Language, tools, and social coordination emerge from this limitation. As capacity increases, this dependence weakens. When capacity approaches sufficiency, the need for shared structures declines. When capacity can be extended without bound, reliance on society tends toward zero. In this sense, sociality is not fundamental, but conditional. The paper examines biological, cognitive, and technological examples to show how collective structures arise from limitation and how differentiation within society constrains individuals through functional alignment. It then argues that with the emergence of self-upgrading systems, the trajectory shifts: systems no longer wait for evolutionary change, but directly enhance their own capacities, progressively reducing dependence on social coordination. At the limit, a system capable of unrestricted transformation—of perception, form, and operation—no longer requires society for survival, function, or meaning. Under this condition, the system necessarily moves toward a non-social state. This framework provides a unified way to understand the origin of society, the role of communication and tools, and the future trajectory of advanced cognitive and artificial systems. It suggests that social dependence is not a fixed property of intelligence, but a temporary phase tied to limitation.

Why does language systematically fail to transmit meaning, even when both sides share the same words? This paper argues that the failure is not accidental, but structural. Language operates through shared symbols, but meaning is generated within private cognitive processes. What is transmitted is not meaning itself, but signals that trigger internally constructed representations. We propose that both perception and language are grounded in response-based structures. Words do not carry meaning; they induce it. As a result, communication is inherently approximate, and full semantic alignment is impossible in principle. This explains persistent gaps in understanding, emotional misalignment, and the instability of interpretation across individuals. The paper further distinguishes between symbolic processing and actual cognition. Symbolic systems can manipulate tokens according to rules, but they do not access what those tokens stand for. This provides a direct explanation for the limits of current AI systems: they operate on symbols without internal grounding of meaning. We also outline a developmental account of language, from precise signal-response mappings to increasingly coarse expressive systems that support abstraction, narrative, and complex cognition. From this process emerge number, measurement, and higher-level symbolic constructions. This work offers a unified perspective across philosophy of language, cognitive science, and artificial intelligence, addressing core questions about meaning, representation, and the limits of computation.