Grounded Iterative Language Planning: How Parameterized World Models Reduce Hallucination Propagation in LLM Agents
arXiv:2606.27806v1 Announce Type: new Abstract: World models for language agents come in two useful forms. An agent-based world model calls an LLM API and reasons flexibly in language, but its errors appear as hallucinated state changes that are hard to score with ordinary regression losses. A parameterized world model is a trained transition predictor; its errors are easier to measure with quantities such as NodeMSE, delta accuracy, and validity accuracy, but it is usually weaker as a standalone planner. We compare these two families on four graph-structured planning benchmarks and introduce
The paper addresses a critical limitation of current LLM agents—hallucination—which is a major hurdle to their reliable deployment and wider adoption, becoming more urgent as agentic systems mature.
Improving world models and reducing hallucination propagation in LLM agents directly enhances their reliability and capability, accelerating the development of truly autonomous systems that can collapse complex workflows.
This research suggests a pathway to more robust and less error-prone AI agents by combining flexible linguistic reasoning with quantifiable, trained transition predictors, leading to more trustworthy automated decision-making.
- · AI agent developers
- · Automation software providers
- · Industries relying on autonomous systems
- · Tasks requiring manual oversight due to agent unreliability
- · Service sectors prone to automation
More reliable and capable AI agents will be deployed in a wider range of high-stakes applications.
Increased trust in AI agents could lead to faster adoption and integration into critical business processes, collapsing some white-collar workflows.
The enhanced autonomy and reduced error rates of agents could accelerate general AI progress towards AGI, further disrupting traditional economic structures.
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Read at arXiv cs.AI