CASSANDRA: Programmatic and Probabilistic Learning and Inference for Stochastic World Modeling

2026-01-26

AI Summary

This work addresses the challenge of modeling complex action effects and causal relationships in data-scarce yet semantically rich real-world domains, such as commercial environments, where effective planning is critical. The authors propose CASSANDRA, a neuro-symbolic world modeling approach that leverages large language models to provide knowledge priors, guiding the generation of procedural transition rules. These rules are integrated with probabilistic graphical models for structural learning, enabling joint modeling of deterministic action effects and stochastic variable dependencies. Evaluated in simulated coffee shop and theme park environments, CASSANDRA significantly outperforms existing baselines, achieving marked improvements in both state transition prediction accuracy and success rates on downstream planning tasks.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Building world models is essential for planning in real-world domains such as businesses. Since such domains have rich semantics, we can leverage world knowledge to effectively model complex action effects and causal relationships from limited data. In this work, we propose CASSANDRA, a neurosymbolic world modeling approach that leverages an LLM as a knowledge prior to construct lightweight transition models for planning. CASSANDRA integrates two components: (1) LLM-synthesized code to model deterministic features, and (2) LLM-guided structure learning of a probabilistic graphical model to capture causal relationships among stochastic variables. We evaluate CASSANDRA in (i) a small-scale coffee-shop simulator and (ii) a complex theme park business simulator, where we demonstrate significant improvements in transition prediction and planning over baselines.

Problem

• world modeling
• stochastic dynamics
• causal relationships
• planning
• limited data

Innovation

• neurosymbolic
• large language model
• probabilistic graphical model
• world modeling
• causal reasoning