arXiv (All)
open access
Renormalising Generative Models for Active Inference: Foundations, Derivations, and Verification
Active inference offers a unified framework for perception, learning, and action, but scaling discrete active-inference models to rich spatial and temporal domains remains difficult. Renormalising generative models (RGMs) address this challenge by composing discrete generative models across spatial
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
- Renormalising Generative Models for Active Inference: Foundations, Derivations, and Verification#789834
- EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models#630018
- EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models#628156
- PMCTS: Principled Parallelized Inference Time Scaling with Particle Monte Carlo Tree Search#974416
- Staircase Policy: Streaming Inference for World-Action Models with Large Action Chunks#1131313
Record · ID 793840
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