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...

Overview

This skill covers aligning agentic world models via knowledgeable experience learning. It addresses critical challenges in autonomous agent development.

Key Concepts

The paper introduces novel approaches to:

• Agent evaluation and benchmarking
• Improving agent efficiency and reasoning
• Designing robust agent systems

When to Use

Use this when working on:

• Agent-based systems and evaluation
• Autonomous reasoning and planning
• Multi-agent frameworks

When NOT to Use

• Non-agent applications
• Tasks requiring implementation code (see the paper)

References

• Paper: https://arxiv.org/abs/2601.13247
• PDF: https://arxiv.org/pdf/2601.13247