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arXiv Computation and Language By Yucheng Li, Mingyuan Li, Xiaxia Wang, Gong Cheng

OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering

Read the original on arXiv Computation and Language →

OpCredit is a policy‑learning framework for agentic knowledge graph question answering that evaluates individual operations by combining two credit signals: Branch Outcome Credit, which compares terminal rewards after different operations in the same context, and Frontier Transition Credit, which measures changes in answer‑conditioned frontier potential. These signals supervise decision spans during training, while inference relies only on the learned policy and the standard KG interface. Experiments on ComplexWebQuestions, WebQSP, and GrailQA across three backbones show consistent performance gains.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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