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arXiv Computation and Language By Rongjin Li, Yuanxin Liu, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

Not Just Reason, Not Just Scan: Reinforcement Learning for Proactive Scientific Error Verification over Academic Paper

Read the original on arXiv Computation and Language →

The paper introduces VERA-RL, a reinforcement‑learning framework for proactive scientific error verification in academic papers. It builds on a Reason–Verify–Scan workflow and presents VERA‑13K, a 12,900‑sample dataset with 4,300 matched reasoning chains covering six error categories across natural‑science domains. The authors also define fine‑grained rewards for reasoning completeness, evidence alignment, and error precision, and show that training Qwen3‑VL‑8B with VERA‑RL improves verifiable reasoning to levels comparable with flagship multimodal large language models.

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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