Curious Causality-Seeking Agents Learn Meta Causal World

• URL: http://arxiv.org/abs/2506.23068v2
• Date: Fri, 01 Aug 2025 06:33:13 GMT
• Title: Curious Causality-Seeking Agents Learn Meta Causal World
• Authors: Zhiyu Zhao, Haoxuan Li, Haifeng Zhang, Jun Wang, Francesco Faccio, Jürgen Schmidhuber, Mengyue Yang,
• Abstract summary: We introduce the textbfMeta-Causal Graph as world models.<n>A single Meta-Causal Graph is composed of multiple causal subgraphs, each triggered by meta state.<n>Our method robustly captures shifts in causal dynamics and generalizes effectively to previously unseen contexts.
• Score: 33.20398438220672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When building a world model, a common assumption is that the environment has a single, unchanging underlying causal rule, like applying Newton's laws to every situation. In reality, what appears as a drifting causal mechanism is often the manifestation of a fixed underlying mechanism seen through a narrow observational window. This brings about a problem that, when building a world model, even subtle shifts in policy or environment states can alter the very observed causal mechanisms. In this work, we introduce the \textbf{Meta-Causal Graph} as world models, a minimal unified representation that efficiently encodes the transformation rules governing how causal structures shift across different latent world states. A single Meta-Causal Graph is composed of multiple causal subgraphs, each triggered by meta state, which is in the latent state space. Building on this representation, we introduce a \textbf{Causality-Seeking Agent} whose objectives are to (1) identify the meta states that trigger each subgraph, (2) discover the corresponding causal relationships by agent curiosity-driven intervention policy, and (3) iteratively refine the Meta-Causal Graph through ongoing curiosity-driven exploration and agent experiences. Experiments on both synthetic tasks and a challenging robot arm manipulation task demonstrate that our method robustly captures shifts in causal dynamics and generalizes effectively to previously unseen contexts.

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