Interoceptive machine framework: Toward interoception-inspired regulatory architectures in artificial intelligence

Computer Science > Artificial Intelligence arXiv:2604.24527 (cs) [Submitted on 27 Apr 2026] Title:Interoceptive machine framework: Toward interoception-inspired regulatory architectures in artificial intelligence Authors:Diego Candia-Rivera (NERV) View a PDF of the paper titled Interoceptive machine framework: Toward interoception-inspired regulatory architectures in artificial intelligence, by Diego Candia-Rivera (NERV) View PDF Abstract:This review proposes an integrative framework grounded on interoception and embodied AI-termed the interoceptive machine framework-that translates biologically inspired principles of internal-state regulation into computational architectures for adaptive autonomy. Interoception, conceived as the monitoring, integration, and regulation of internal signals, has proven relevant for understanding adaptive behavior in biological systems. The proposed framework organizes interoceptive contributions into three functional principles: homeostatic, allostatic, and enactive, each associated with distinct computational roles: internal viability regulation, anticipatory uncertainty-based re-evaluation, and active data generation through interaction. These principles are not intended as direct neurophysiological mappings, but as abstractions that inform the design of artificial agents with improved self-regulation and context-sensitive behavior. By embedding internal state variables and regulatory loops within these principles, AI systems can achieve more robust decision-making, calibrated uncertainty handling, and adaptive interaction strategies, particularly in uncertain and dynamic environments. This approach provides a concrete and testable pathway toward agents capable of functionally grounded self-regulation, with direct implications for human-computer interaction and assistive technologies. Ultimately, the interoceptive machine framework offers a unifying perspective on how internal-state regulation can enhance autonomy, adaptivity, and robustness in embodied AI systems Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.24527 [cs.AI] (or arXiv:2604.24527v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.24527 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Diego Candia-Rivera [view email] [via CCSD proxy] [v1] Mon, 27 Apr 2026 14:28:09 UTC (656 KB) Full-text links: Access Paper: View a PDF of the paper titled Interoceptive machine framework: Toward interoception-inspired regulatory architectures in artificial intelligence, by Diego Candia-Rivera (NERV)View PDF view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What…

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