Abstract

In many real-world environments, data is often incomplete, noisy, or contradictory, posing challenges for models that rely on structured and perfect information. Addressing this issue, a novel approach to grounding has been developed, focusing on aligning internal model representations with external imperfect world data through adaptive mechanisms. The technique explored here integrates a probabilistic world model with LLaMA, enabling the model to infer missing data and resolve inconsistencies while maintaining a high degree of accuracy in decision-making tasks. Experimental results demonstrate the model's capacity to remain robust under conditions of fluctuating data quality, highlighting its potential for deployment in environments that demand contextual awareness and adaptability. This research shows that LLMs can maintain effective performance even in the presence of substantial uncertainty, offering promising insights into the practical application of models that must operate in real-time with incomplete or conflicting information.