Toward Causal-Visual Programming: Enhancing Agentic Reasoning in Low-Code Environments

• URL: http://arxiv.org/abs/2509.25282v2
• Date: Wed, 08 Oct 2025 06:59:21 GMT
• Title: Toward Causal-Visual Programming: Enhancing Agentic Reasoning in Low-Code Environments
• Authors: Jiexi Xu, Jiaqi Liu, Lanruo Wang, Su Liu,
• Abstract summary: Causal-Visual Programming (CVP) allows users to define a simple "world model" for workflow modules through an intuitive low-code interface.<n>This causal graph acts as a crucial constraint during the agent's reasoning process, anchoring its decisions to a user-defined causal structure.<n>CVP offers a viable path toward building more interpretable, reliable, and trustworthy AI agents.
• Score: 6.0333285356716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (LLM) agents are increasingly capable of orchestrating complex tasks in low-code environments. However, these agents often exhibit hallucinations and logical inconsistencies because their inherent reasoning mechanisms rely on probabilistic associations rather than genuine causal understanding. This paper introduces a new programming paradigm: Causal-Visual Programming (CVP), designed to address this fundamental issue by explicitly introducing causal structures into the workflow design. CVP allows users to define a simple "world model" for workflow modules through an intuitive low-code interface, effectively creating a Directed Acyclic Graph (DAG) that explicitly defines the causal relationships between modules. This causal graph acts as a crucial constraint during the agent's reasoning process, anchoring its decisions to a user-defined causal structure and significantly reducing logical errors and hallucinations by preventing reliance on spurious correlations. To validate the effectiveness of CVP, we designed a synthetic experiment that simulates a common real-world problem: a distribution shift between the training and test environments. Our results show that a causally anchored model maintained stable accuracy in the face of this shift, whereas a purely associative baseline model that relied on probabilistic correlations experienced a significant performance drop. The primary contributions of this study are: a formal definition of causal structures for workflow modules; the proposal and implementation of a CVP framework that anchors agent reasoning to a user-defined causal graph; and empirical evidence demonstrating the framework's effectiveness in enhancing agent robustness and reducing errors caused by causal confusion in dynamic environments. CVP offers a viable path toward building more interpretable, reliable, and trustworthy AI agents.

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