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

• URL: http://arxiv.org/abs/2604.24527v1
• Date: Mon, 27 Apr 2026 14:28:09 GMT
• Title: Interoceptive machine framework: Toward interoception-inspired regulatory architectures in artificial intelligence
• Abstract summary: This review proposes an integrative framework grounded on interoception and embodied AI-termed the interoceptive machine framework.<n>The proposed framework organizes interoceptive contributions into three functional principles: internal viability regulation, anticipatory uncertainty-based re-evaluation, and active data generation through interaction.<n>Ultimately, the interoceptive machine framework offers a unifying perspective on how internal-state regulation can enhance autonomy, adaptivity, and robustness in embodied AI systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

Related papers

• Neuro-Symbolic Agents for Regulated Process Automation: Challenges and Research Agenda [43.328395470827665]
  We argue that symbolic structures embedded in regulated industries should be treated as core architectural components.<n>We propose compliance-by-construction as a complementary paradigm to guardrail-based monitoring.<n>We call on the neuro-symbolic community to engage with regulated process automation as a high impact research domain.
  arXiv  Detail & Related papers  (2026-06-11T14:34:11Z)
• Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration [28.35417287973668]
  No integrated account explains how affective cues operate within agentic collaboration.<n>We trace how model-generated affective signals enter interaction loops that govern reliance, repair and oversight.<n>We propose a framework that treats affect not as an internal property of AI but as a coordination layer through which humans and agents negotiate capability, uncertainty and responsibility.
  arXiv  Detail & Related papers  (2026-05-08T05:10:38Z)
• A Survey of Reasoning in Autonomous Driving Systems: Open Challenges and Emerging Paradigms [49.66022971508878]
  We argue that reasoning should be elevated from a modular component to the system's cognitive core.<n>We derive and systematize seven core reasoning challenges, such as the responsiveness-reasoning trade-off and social-game reasoning.<n>We identify a fundamental and unresolved tension between the high-latency, deliberative nature of LLM-based reasoning and the millisecond-scale, safety-critical demands of vehicle control.
  arXiv  Detail & Related papers  (2026-03-11T07:40:53Z)
• Learning Beyond Optimization: Stress-Gated Dynamical Regime Regulation in Autonomous Systems [0.0]
  We propose a framework for learning without an explicit objective.<n>Instead of minimizing external error signals, the system evaluates the intrinsic health of its own internal dynamics.<n>Our results suggest a possible route toward autonomous learning systems.
  arXiv  Detail & Related papers  (2026-02-20T19:39:56Z)
• Agentic AI for Cybersecurity: A Meta-Cognitive Architecture for Governable Autonomy [0.0]
  This paper argues that cybersecurity orchestration should be reconceptualized as an agentic, multi-agent cognitive system.<n>We introduce a conceptual framework in which heterogeneous AI agents responsible for detection, hypothesis formation, contextual interpretation, explanation, and governance are coordinated through an explicit meta-cognitive judgement function.<n>Our contribution is to make this cognitive structure architecturally explicit and governable by embedding meta-cognitive judgement as a first-class system function.
  arXiv  Detail & Related papers  (2026-02-12T12:52:49Z)
• Institutional AI: A Governance Framework for Distributional AGI Safety [1.3763052684269788]
  We identify three structural problems that emerge from core properties of AI models.<n>The solution is Institutional AI, a system-level approach that treats alignment as a question of effective governance of AI agent collectives.
  arXiv  Detail & Related papers  (2026-01-15T17:08:26Z)
• Active Thinking Model: A Goal-Directed Self-Improving Framework for Real-World Adaptive Intelligence [0.11844977816228043]
  We propose a unified cognitive framework that integrates goal reasoning, dynamic task generation, and self-reflective learning into an adaptive architecture.<n>A mathematically grounded theoretical analysis demonstrates that ATM can autonomously evolve from suboptimal to optimal behavior without external supervision.
  arXiv  Detail & Related papers  (2025-11-02T01:13:12Z)
• A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence [87.08051686357206]
  Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static.<n>As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck.<n>This survey provides the first systematic and comprehensive review of self-evolving agents.
  arXiv  Detail & Related papers  (2025-07-28T17:59:05Z)
• AI Agent Behavioral Science [29.262537008412412]
  AI Agent Behavioral Science focuses on the systematic observation of behavior, design of interventions to test hypotheses, and theory-guided interpretation of how AI agents act, adapt, and interact over time.<n>We systematize a growing body of research across individual agent, multi-agent, and human-agent interaction settings, and demonstrate how this perspective informs responsible AI by treating fairness, safety, interpretability, accountability, and privacy as behavioral properties.
  arXiv  Detail & Related papers  (2025-06-04T08:12:32Z)
• Situationally-Aware Dynamics Learning [57.698553219660376]
  We propose a novel framework for online learning of hidden state representations.<n>Our approach explicitly models the influence of unobserved parameters on both transition dynamics and reward structures.<n>Experiments in both simulation and real world reveal significant improvements in data efficiency, policy performance, and the emergence of safer, adaptive navigation strategies.
  arXiv  Detail & Related papers  (2025-05-26T06:40:11Z)
• Neural Brain: A Neuroscience-inspired Framework for Embodied Agents [78.61382193420914]
  Current AI systems, such as large language models, remain disembodied, unable to physically engage with the world.<n>At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability.<n>This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges.
  arXiv  Detail & Related papers  (2025-05-12T15:05:34Z)
• Active Inference in Robotics and Artificial Agents: Survey and Challenges [51.29077770446286]
  We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
  arXiv  Detail & Related papers  (2021-12-03T12:10:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.