aligning-agentic-world-models-via-knowledgeable

name	aligning-agentic-world-models-via-knowledgeable
title	Aligning Agentic World Models via Knowledgeable Experience Learning
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.13247
keywords	["Agent","Learning"]
description	Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural grounding to respect the immutable laws of the physical world. Consequently, while these agents implicitly function as world models, their simulations often suffer from physical hallucinations-generating plans that are logically sound but physically unexecutable. Existing alignment strategies predominantly rely on resource-intensive training or fine-tuning, which...