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, wh...
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---
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
