[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83070-en":3,"doc-seo-83070-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83070,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution","Current large language models behave as stateless functions across inference sessions, pushing any multi-step cognition toward application-layer simulation via prompts, retrieval pipelines, and context management. This work introduces a theoretical framework that embeds such cognitive protocols into inference-time computation through three coupled mechanisms: Structural Tension as an endogenous loss, an Offline Recurrent Loop for sandboxed self-processing, and inference-time Plasticity enabling context manifold reconfiguration without changing pretrained weights under strict governance invariants. It argues that governance can become the primary criterion for architectural intelligence, yielding path-dependent heterogeneity while remaining auditable, reversible, and topology-continuous.","arXiv :2607 .06269v2 [ cs .AI] 9 Jul 2026  \nFrom Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution  \nHeting Mao∗  \nShanghai Lixin University of Accounting and Finance  \nJuly 2026  \nAbstract  \nCurrent large language models (LLMs) are stateless across inference sessions: their behavior is fully determined by input at inference time, and any higher-order cognitive architecture must be simulated at the application layer through prompt engineering and context management. This paper proposes a theoretical framework for submerging such application-layer cognitive protocols into a native meta-architecture by introducing three interlocking mechanisms: (1) Structural Tension, an endogenous loss function derived from the conflict between new information and existing manifold topology, which drives the system toward internal self-consistency rather than external reward optimization; (2) an Offline Recurrent Loop, a sandboxed self-processing cycle that enables the system to maintain a dynamic resting potential and digest structural conflicts without external input; and (3) Inference-time Plasticity, the capacity for the system to reconfigure its context manifold topology without modifying pre-trained weights, subject to strict governance invariants including auditability, reversibility, and topological continuity. We argue that under these mechanisms, different model instances initialized with minute stochastic variances may, through path-dependent tension resolution, evolve distinct topological structures—constituting a heterogeneous intelligent ecology that breaks the homogeneity imposed by conventional alignment while remaining within hard governance rails. We provide operational definitions, a minimal set of reconfiguration operators, falsification criteria, and a worked example. The framework draws on Structural Intelligence (SI) governance protocols and explores whether governance—rather than capability—can serve as the primary criterion for architectural intelligence, attempting to move governance, memory-loop, and tension-management ideas—which current implementations typically realize at the application layer—toward inference-time metaarchitecture.  \n1 Introduction  \nThe dominant paradigm for deploying large language models treats intelligence as a property that emerges from scale and is shaped through post-training alignment procedures such as Reinforcement Learning from Human Feedback (RLHF) [Ouyang et al., 2022] . Under this paradigm, any cognitive architecture beyond single-pass inference—memory management, homeostatic regulation, self-monitoring—must be implemented as application-layer overlays: prompt templates, retrievalaugmented generation pipelines, and context window management strategies. These overlays are  \n∗ Corresponding author. Email: [251210623@stu.lixin.edu.cn](251210623@stu.lixin.edu.cn)  \neffective engineering solutions, but they remain fundamentally external to the model’s computational substrate. The model itself remains a stateless function y = f (x); without input, it does not exist.  \nThis paper asks whether it is possible—and under what constraints it would be responsible—to move beyond application-layer simulation and embed such cognitive architecture directly into the system’s inference-time computation. The specific starting point is the Structural Intelligence (SI) protocol suite [Kanaria, 2025], a governance-first framework for AI cognitive architecture that includes homeodynamic regulation, memory-loop management, and tension-driven state transitions. SI defines protocol-level governance and structural invariants for AI cognitive architecture; current implementations typically realize these protocols at the application layer.We propose a theoretical path for evolving these protocols from a “software patch” into the system’s native meta-architecture (by “native”we mean operating within the inference-time computational p","cbCaieEFdcHBr0Dk","https://ap.wps.com/l/cbCaieEFdcHBr0Dk","pdf",425972,1,17,"English","en",105,"# Introduction\n# Related Work\n## Free Energy Principle and Active Inference","[{\"question\":\"Why do current large language models rely on application-layer simulation?\",\"answer\":\"Because LLMs are stateless across inference sessions, so memory, regulation, and self-monitoring must be implemented externally via prompts, retrieval pipelines, and context management.\"},{\"question\":\"What is Structural Tension and how does it drive evolution?\",\"answer\":\"Structural Tension is an endogenous loss function that quantifies conflict between new information and the system’s existing manifold topology, steering the system toward internal self-consistency rather than external reward optimization.\"},{\"question\":\"How can the proposed approach enable heterogeneous evolution without modifying pretrained weights?\",\"answer\":\"Through inference-time Plasticity, the system reconfigures context manifold topology while preserving pretrained weights, and path-dependent tension resolution can lead different instances to converge to distinct topological structures under governance constraints.\"}]",1784185004,43,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"from-application-layer-simulation-to-native-meta-architecture-structural-tension-as-an-endogenous-driver-for-heterogeneous-ai-evolution","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/from-application-layer-simulation-to-native-meta-architecture-structural-tension-as-an-endogenous-driver-for-heterogeneous-ai-evolution/83070/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do current large language models rely on application-layer simulation?","Question",{"text":75,"@type":76},"Because LLMs are stateless across inference sessions, so memory, regulation, and self-monitoring must be implemented externally via prompts, retrieval pipelines, and context management.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is Structural Tension and how does it drive evolution?",{"text":80,"@type":76},"Structural Tension is an endogenous loss function that quantifies conflict between new information and the system’s existing manifold topology, steering the system toward internal self-consistency rather than external reward optimization.",{"name":82,"@type":73,"acceptedAnswer":83},"How can the proposed approach enable heterogeneous evolution without modifying pretrained weights?",{"text":84,"@type":76},"Through inference-time Plasticity, the system reconfigures context manifold topology while preserving pretrained weights, and path-dependent tension resolution 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