ChainReaction: Causal Chain-Guided Reasoning for Modular and Explainable Causal-Why Video Question Answering
arXiv:2508.21010v3 Announce Type: replace-cross Abstract: Existing Causal-Why Video Question Answering (VideoQA) models often struggle with higher-order reasoning, relying on opaque, monolithic pipelines that entangle video understanding, causal inference, and answer generation. These black-box approaches offer limited interpretability and tend to depend on shallow heuristics. We propose a novel, modular paradigm that explicitly decouples causal reasoning from answer generation, introducing natural language causal chains as interpretable intermediate representations. Inspired by human cognitiv
The increasing complexity and opacity of current AI models necessitate more interpretable and robust reasoning mechanisms, especially in critical applications.
This work directly addresses the 'black-box' problem in AI, particularly for video understanding, which is crucial for higher-order reasoning and trust in autonomous systems.
The explicit decoupling of causal reasoning from answer generation through natural language causal chains offers a more modular and transparent approach to AI model development.
- · AI researchers
- · Developers of explainable AI (XAI)
- · Industries relying on video analytics (e.g., security, autonomous vehicles)
- · Users needing transparent AI decision-making
- · Monolithic, opaque AI model developers
- · Companies relying on shallow heuristic AI for complex tasks
Improved interpretability and reliability of AI systems in complex visual reasoning tasks.
Accelerated development of more robust AI agents capable of higher-order cognitive functions.
Enhanced human-AI collaboration as AI explanations become more intuitive and verifiable.
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Read at arXiv cs.AI