arXiv AI October 16, 2026 By Weicheng Liu, Xiangcheng Wu, Depeng Xu, Haopeng Zhang, Xi Niu

CARA: Causally Guided Retrieval Agent for Question Answering

CARA is a training‑free, causally guided retrieval agent designed for question answering over text‑rich knowledge graphs. It evaluates retrieved evidence using two causal criteria—consequentiality and essentiality—alongside path consistency, and aggregates results from multiple agents via reciprocal rank fusion. Experiments on three STaRK datasets show that CARA outperforms existing retrievers, improving answer quality and faithfulness while examining fewer graph nodes and requiring fewer retrieval operations.

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