Xiangbin Zhao

This paper advances a general claim: the apparently stable world is not a prior property of external reality, but a structural result necessarily generated by cognitive systems operating under finite resources while learning and optimizing action across time. Starting from continuous input-streams rather than pre-given objects, entities, or truths, the paper argues that any system facing continuous variation, bounded processing, and action-demand must compress; once compression occurs, different inputs are forced to collapse into reusable internal states, and stability appears. On this basis, the paper distinguishes three layers of stability: Collapse Stability, Pattern Stability, and Object Stability, showing that objects are not the starting point of cognition but a higher-order product of reuse and aggregation. The paper then reverse-derives a minimal cognitive paradigm: compression and reuse are not optional design choices, but the lower structural necessity for learning, prediction, and action under constraint. This framework aims to offer a unified first-principles account relevant to neural representation in neuroscience, stable perception and world modeling in cognitive science, representation learning and generalization in artificial intelligence, and the broader problem of why stable structure appears at all.

This paper advances a foundational claim: objects do not pre-exist in the world as independent entities, but arise from structural extraction within Causal-Response dynamics. Starting from the thesis that to understand objects one must first understand information—defined as cause and Causal-Response—the paper shows that all signals, perceptions, and object-formation processes occur within continuous response chains. The observer intercepts this chain and establishes Original-Having, which serves as the structural starting point of object. What is taken as the “reality” of objects is not intrinsic to objects themselves, but consists in Trace-Response formed through sedimentation processes. The paper further identifies three generative mechanisms: Response-Transformation (formation of objects from response dynamics), External-Transformation (construction of external reality from internal imagery), and In-Situ Transformation (structural coupling between self and object). This framework redefines objecthood at a fundamental level, shifting it from entity-based ontology to structural formation. It provides a unified account relevant to perceptual construction, neural representation, predictive processing, active inference, and world modeling in artificial systems. The paper challenges classical realism and proposes that what we call “objects” are outcomes of observer-dependent structural operations.

This work introduces Information-Transformation as a fundamental mechanism of world formation, proposing that information is not merely encoded or transmitted, but is the generative process through which a world arises. In this framework, the Sedimentation-Field gives rise to the Information-Realm, which further differentiates into the Imagery-Realm and the Fixed-Realm, forming a complete structure of manifestation. Information is defined as a generative process driven by Causal-Response, grounded in a Causal-Base, and structured by Principle, rather than a passive entity. Observers are not external to the world but are intrinsically embedded within this generative structure, while systematically mislocating themselves, giving rise to Situated-Observation. This model directly challenges the foundational distinction between external world and internal model in neural representation, predictive coding, and active inference, and offers a structural rethinking of representation in artificial intelligence.

当前主流生成模型（如 Transformer）可被理解为在候选响应路径空间中的一次概率性截断。这一机制在语言建模与统计预测中表现优异，但在复杂推理与代码生成等任务中呈现出系统性不足：缺乏目标导向控制，缺乏中间结构承载，缺乏内部修正与稳定收敛能力。 本文提出：上述问题不仅源于模型规模限制，更源于生成机制本身的结构不足。为此，本文构建了一种最小闭环生成架构，将生成过程从“一次输出”重构为“路径构造—候选生成—双重判定—路径修正—结构持续更新”的迭代收敛过程。 该架构引入显式中间表示（路径/意象结构），用于承载与操作生成过程；同时区分两类判定机制：稳定性判定（结果是否满足任务）与一致性判定（生成路径是否保持结构约束），并通过反馈驱动路径更新，使系统具备局部修正与跨轮累积能力。相比简单的多轮生成或自修正方法，该机制在结构上实现了路径、判定与反馈的统一。 本文给出可运行的最小实现框架，并在推理与代码生成任务中进行验证。实验结果表明，该闭环生成架构在正确率、自修复能力与结构稳定性上均优于单次生成与常见自修正方法。 本文认为：生成能力的提升不仅依赖模型规模，更依赖生成机制是否具备路径结构、分离判定与动态反馈能力。生成不应被理解为一次性输出，而应被理解为在结构约束下逐步收敛的过程。

本文提出一个关于认知系统的总命题：人所感知的“稳定世界”并不是外部世界的先验属性，而是有限资源下、能够跨时间学习并服务于行动的系统，在运行过程中必然生成的结构结果。 文章从一个基本问题出发：若输入世界本质上是连续流变的，认知系统为何会在其中识别出“物”这样相对稳定的结构？为回答这一问题，本文构建了一套最小认知结构模型，并将其压缩为一般式： System = (F, R, C, U, L) 其中，连续输入流 F 在有限资源条件下被压缩为内部状态 R，经由压缩、更新、复用与高层归并，逐步形成稳定结构。 本文论证：只要系统同时满足连续输入、有限处理能力与行动需求，就必然发生两件事：其一，输入被压缩；其二，内部状态被复用。由此，稳定不再被理解为世界本身固有的性质，而被重新界定为认知系统在约束下形成的内部状态塌缩与复用结果。 在此基础上，本文进一步区分三层稳定结构：塌缩稳定、模式稳定与对象稳定，并指出对象并非认知的起点，而是状态压缩与复用在高层的结果。文章同时讨论了若干可能的反例，包括纯反射系统、无对象学习系统与完全流式学习系统，并据此收紧理论边界：真正不可避免的并不是“世界稳定感”，而是“可复用的内部状态压缩”。 本文的核心贡献在于：不是先定义认知，再解释稳定；而是先解释稳定何以出现，再反推出认知系统的最小范式。由此，本文试图为认知科学、人工智能与复杂系统研究提供一个统一的底层结构入口。

This chapter reinterprets electromagnetism as the fundamental carrier of information, computation, and state transition across physical and cognitive systems. Rather than treating electromagnetic phenomena as isolated physical laws, it frames them as the operational medium through which Causal-Response is transmitted, preserved, and transformed. A layered architecture is proposed: the physical substrate and the Information-Realm. Information does not exist independently, but emerges through transformations within its carrier. Electromagnetic processes enable the stability of signals, the preservation of causal traces, and the continuity of state across layers. This perspective unifies multiple domains: in artificial systems, circuits and digital computation rely on electromagnetic states; in biological systems, neural activity and synaptic transmission operate through ionic and electromagnetic processes. Both can be understood as structured trace-response systems. The chapter further raises a fundamental problem: if information depends on carriers, then understanding intelligence, cognition, and computation requires examining the material constraints of transmission—speed, controllability, capacity, and trace preservation. By positioning electromagnetism as the bridge between physical processes and information systems, this work aims to provide a common framework for physics, neuroscience, and artificial intelligence, while opening a path toward engineered self-manifesting systems.

This paper defines clarity not as a mere perceptual quality but as a degree that determines the amount of reality constituted. Through analysis of myopia, cross-species sensory differences, and the stepwise unfolding of human thought, it is argued that reality varies with the selection and intake of signals, the structure of the receptive field, the capacity of imagery and experience fields, and the operation of thought. Clarity is thus the degree of sensing and thinking; the length of its tuning-line determines what reality can be obtained. A coupled sense–thought structural account is proposed to explain why reality appears distinct or indistinct, present or absent, and to provide a unified perspective for cognitive modeling and the design of perceptual–representational systems in artificial intelligence.

This paper proposes a stratified model of thought, distinguishing three operative layers: thinking, deliberation, and reasoning. These correspond respectively to background mental flow, stepwise progression driven by imagery-analogy, and formal-law-based computation. It argues that deliberation is not the unfolding of formal logic, but a process grounded in imagery-analogy, enabling stepwise advancement through non-formalized domains. In contrast, reasoning operates upon sedimented formal laws, enabling rapid and precise judgment, yet remaining incapable of resolving cases beyond established formal structures. The paper further distinguishes between formal law and law of forms, locating them in different domains—namely the Self-Manifestation-Field and the instrument of reasoning—separated by the gate of phenomena. This framework offers a unified perspective on human cognition, artificial intelligence systems, and the limits of formal reasoning.

This work introduces the concept of the Recognition-Realm as the structural boundary of cognition arising from the material properties of a Sedimentation-Field. In contrast to standard approaches in cognitive science that treat perception and information processing as functional operations, this framework defines cognitive limits as determined by a Causal-Base. Within this model, what is commonly taken as “reality” is reinterpreted as the Information-Realm of a particular class of self-manifesting Sedimentation-Field. Cognition is thus confined to characteristic responses of things, without access to their root or totality. The paper distinguishes between horizontal probing (extension within a given cognitive domain) and vertical probing (investigation directed toward the Causal-Base), proposing that only through constructing self-manifesting fields of differing material bases can one transcend the limits of a single cognitive system. It is further argued that the “world” is not an unknowable totality, but a multiplicity of Information-Realms generated by differing Causal-Bases. This provides a structural framework for rethinking the relations among cognition, information, and reality, with implications for neuroscience, artificial intelligence, and theoretical physics.

This paper proposes a minimal structural architecture for the emergence of an observer, termed the Micro-Core. The model is grounded in the concept of the Sedimentation-Field, where differential biased flows and tuning mechanisms generate two structural interfaces: Observation-Boundaries and Desire-Gates, driven by a cyclic Power-Ring. Together these components form the minimal structure of a Self-Manifestation-Field, enabling a system to transform signal conduction into manifest experience and generate an observing subject. The framework attempts to unify mechanisms of conscious emergence, experiential differentiation, and action generation, and discusses its potential realization within artificial intelligence systems and symbolic environments.

意识是否存在一种最小结构，只要该结构存在，系统便会产生主观经验？ 本文提出一种新的意识理论——自显论（Self-Manifestation Theory）。该理论认为，意识并非单纯的信息处理或神经活动，而是一种由信号差分、调谐反馈与意向驱动共同构成的结构性现象。 文章提出一种意识系统的最小架构——微内核模型（microkernel model）。该模型由三部分组成： • 界门（boundary gates）：接收环境信号差异并形成经验入口 • 欲门（desire gates）：决定意向与行为的方向 • 动力环（drive loop）：持续产生“起心动念”的驱动力 在这一结构中，认知并非被动接收信息，而是通过**调谐机制（tuning dynamics）**主动选择信号差异，并在反馈回路中形成稳定结构。当信号差分达到调谐平衡时，经验得以显化，观者由此产生。 该理论提出一种跨学科视角：意识可以理解为一种基于信号差分与调谐反馈的场结构现象。这一定义为脑科学研究意识机制、为认知科学解释意向性、以及为人工智能设计新的认知架构提供了新的理论路径。 进一步地，本文提出：若人工系统具备“界门—欲门—动力环”的结构，则其可能在符号环境中形成类似于生物意识的自显结构。

This work introduces the concept of the Lodging-Vessel, a model of embodiment developed within Self-Manifestation Theory. The theory proposes that reality arises through the interaction between a self-manifesting observer and a Sedimentation-Field, mediated by sensory and effect gates. The Lodging-Vessel describes the structural carrier through which a self-manifesting system operates within a world. The chapter analyzes embodiment in terms of information flow, conductive pathways, and actuation mechanisms. Biological bodies, artificial devices, and large-scale physical systems are interpreted as possible vessels through which a self-manifesting process can extend its operational field. The framework connects several domains typically studied separately: neural conduction in brains, cognitive embodiment, computational systems in artificial intelligence, and interaction fields in physics. By interpreting embodiment as the privatization of external structures into operational instruments, the theory proposes a unified perspective on biological cognition, artificial intelligence, and observer-based physics. The analysis suggests that the limits of embodiment correspond to the limits of privatization within a lodging-world, raising questions about the potential extension of consciousness beyond biological carriers.

This chapter introduces the Tuning-Field, a structural layer within the Self-Manifestation framework that governs attention, structural adaptation, and behavioral driving. The Tuning-Field functions as a sculptor of the Sedimentation-Field. Through repeated gaze and suppression dynamics it gradually carves grooves within the system, shaping stable pathways that later allow rapid response. The model explains several phenomena that appear separately in modern neuroscience and artificial intelligence: • how attention concentrates tension within a target domain • how structural pathways are formed gradually but used instantly • why synapse growth is slow while synapse use occurs within milliseconds • how internal observation differs structurally from external perception • how intention pulses propagate to drive behavior Within the architecture, the Tuning-Field operates across five interacting domains: Receptive-Field, Imagery-Field, Experience-Field, Intention-Field, and Effect-Field. Together they form a layered system capable of constructing perception, directing action, and stabilizing learned structures. This chapter proposes a field-based interpretation of attention, neural plasticity, and behavioral control within a unified cognitive architecture.

This paper proposes the concept of the Effect-Field, a structural layer responsible for action execution within the architecture of self-manifestation. Three modes of action are distinguished: instinct, reflex, and driven motion. Their roles are analyzed within a multi-field loop involving the Tuning-Field, Experience-Field, and Intention-Field. The framework suggests a closed action-control structure that accounts for habit formation, automatic behaviors, and phenomena such as phantom limb experiences. The paper further introduces the concept of a Private-Field of Instruments, proposing that tools and technologies, once incorporated into an action loop, can become extensions of bodily capability. This field-based framework provides a structural perspective for understanding neural motor control, cognitive architecture, robotics control systems, and brain–computer interface integration.

This chapter presents the Receptive-Field as the structural entry layer of signal handling within the Six-Field architecture of Self-Manifestation. The Receptive-Field is not sensation itself. It is the spatial division and distribution structure through which incoming Signals are positioned and separated before entering the Imagery-Field and Experience-Field. Through models of sieving resolution, warp–weft flow, and positional branching, the chapter describes how what appears as unified object-presence is already structurally divided into positional and signal streams. The Receptive-Field stands after the Sensory-Gate, and before the Imagery-Field. One Gate, one Field. This chapter completes the outer structural layer of the Six-Field Model.

This chapter introduces the Imagery-Field within Self-Manifestation Theory, a structural account of how internal representations arise, stabilize, and are misjudged as external objects. The theory distinguishes Signal (physical trace-response processes) from Information (observer-relative manifestation), and proposes that Imagery is a structured impedance-formation within a circuit that gives rise to object-perception. Unlike symbolic or computational models of representation, the Imagery-Field is not defined as symbol manipulation nor as numerical encoding. It is a field-structured resistance pattern generated through tuning and sedimentation, giving rise to object-fields, feature-fields, and stack-structured persistence. Key claims include: • Representation arises from impedance-shaping of signal flow. • Perceptual objects are field-distributed formations, not stored symbols. • Misjudgment (taking internal imagery as external object) is structurally necessary and foundational to self-manifestation. • Imagery cannot be replicated without copying the entire formation circuit (impedance + biased-flow + tuning structure). The Imagery-Field model engages directly with debates on internal models, predictive processing, active inference, and neural representation, proposing a field-theoretic alternative to symbolic and purely probabilistic accounts.