arXiv (All)
open access
Towards Interaction Regulation from Human Feedback via Free Energy Minimization
A central challenge across control and learning is the design of mechanisms regulating the interactions between humans and autonomous agents. Inspired by the free energy principle from computational neuroscience, we introduce a control-theoretical framework to integrate human preferences online into
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning#670473
- E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning#668408
- “Active Inference. The Free Energy Principle in Mind, Brain, and Behavior”.#1277
- The Free Energy Principle: A Unifying Framework Connecting Individual Existence, Collective Order, and Social Value Judgments, and Its Implications for Decision Support Systems#718194
- The Emergence of Artificial Subjectivity: LMT Persona, Quantum Meta-Cognitive Operator, and Relational Collapse in Human-AI Systems#705242
Record · ID 1013011
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive