# ROIF and Active Inference: The Formal Boundary of History-Conditioned Anticipatory Systems. History-dependent reconfiguration, structured memory, and predictive preconfiguration in prestressed complex systems Presenter: Vitalii Shapovalov Contribution: Pre-recorded Video presentation Public ID: talk-877eaccf6e0007ec ## Submitted abstract Recursive Organic Integration Framework (ROIF) is a deterministic, domain-agnostic computational architecture for complex systems whose present organization and subsequent responses may be shaped by prior interactions, distributed reconfiguration, and anticipatory preconfiguration. Rather than treating history as an additional input variable, ROIF represents how prior events may become embodied in the organization through which subsequent events are processed. The framework distinguishes experimentally separable routes through which the past and represented possible futures may affect subsequent dynamics: history embodied in present organization; structured memory conditioning a subsequent transition; and anticipatory preconfiguration based on a represented possible future Temporal Image. Matched-current-state controls found no independently identifiable history-specific transition effect for the tested physical-history pathway. In contrast, a separate structured-memory experiment produced different subsequent physical transitions under matched represented physical state and adaptive connection state. The predictive architecture allows possible future SystemImages to participate in present preconfiguration. Current experiments, however, test only a restricted one-step preconfiguration within the implemented prestress representation. This experimental restriction does not define the broader architectural role of prestress in ROIF, where prestress reflects an already organized relational system state shaped by interacting internal and external conditions, constraints, capacities, history, and time. Objective-independence tests further prevent interpreting the present result as evidence for general predictive stabilization. These results motivate a boundary question rather than a claim of equivalence: when does a history-conditioned anticipatory architecture constitute Active Inference? ROIF does not presently require a variational free-energy functional, Bayesian generative model, or particular neurobiological implementation. The presentation therefore uses ROIF as a computational case study to ask which mathematical conditions distinguish Active Inference from a broader class of history-conditioned anticipatory systems, and whether these formalisms should be understood as nested, neighbouring, or formally distinct. Source: [submitted source]() Abstract SHA256: 60aa6b1470e641034d9dc4496bc2ffef2d3eb25dd5a745561ef9ea2dc5c378c9 ## Organizer review checklist - [ ] Confirm the submitted title, presenter, and abstract with the presenter. - [ ] Confirm contribution format and scheduling needs. - [ ] Confirm public permissions for any additional material before publication. - [ ] Prepare discussion questions grounded in the submitted abstract. ## Public learning resources These approved event resources are starting points; topic relevance and author identity have not been inferred. - Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology - Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations - GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER - Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation - pymdp: https://github.com/infer-actively/pymdp - Reproducible publication template: https://github.com/docxology/template OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=ROIF%20and%20Active%20Inference%3A%20The%20Formal%20Boundary%20of%20History-Conditioned%20Anticipatory%20Systems.%0A%0AHistory-dependent%20reconfiguration%2C%20structured%20memory%2C%20and%20predictive%20preconfiguration%20in%20prestressed%20complex%20systems