Precise retrieval of scientific information is fundamentally constrained by long-tailed concepts and high fact-sensitivity of scientific corpora. These challenges often limit the effectiveness of dens...
The paper introduces the task of Scientific Claim Unlearning and presents a new benchmark, SciUnlearn, to evaluate it. It highlights that language models trained on static scientific corpora risk disseminating outdated or retracted claims as scientific knowledge evolves. Current machine unlearning methods fail to effectively remove claim-level knowledge, often only suppressing it superficially, underscoring the need for specialized techniques for structured knowledge removal.
By Snigdha Paul, Manasi Patwardhan, Arman Cohan
arXiv:2607. 20926v1 Announce Type: new Abstract: Scientific research involves complex information-seeking and reasoning workflows across heterogeneous sources.
By Yinhao Tang, Youqing Fang, Yanan Sun, Wenran Liu, Weiming Zhang, Bin Liu, Kuikun Liu, Wenwei Zhang, Kai Chen
The paper investigates why retrieval‑based open‑ended evaluation fails in medical fact verification. By creating two detailed taxonomies—one for retrieval‑stage errors across five quality dimensions and another for verifier‑reasoning errors across six steps—the authors automatically label evidence quality and reasoning errors using an LLM‑as‑Judge pipeline. Their large‑scale stress tests across multiple retrieval methods and verifier models show that increasing model size, reasoning effort, source breadth, or medical fine‑tuning does not eliminate these failure modes, indicating fundamental limits of the retrieve‑then‑verify paradigm in open‑ended medical contexts.
By Heyuan Huang, Jirui Dai, Alexandra DeLucia, Sonal Joshi, Mahsa Yarmohammadi, Jie Gao, Bernal Jim\'enez Guti\'errez, Mark Dredze
arXiv:2608. 03860v1 Announce Type: cross Abstract: We introduce SciRet, a compute-aware empirical study of retrieval-augmented generation for scientific question answering over CORD-19.
By Kaysarul Anas Apurba, Md. Hasibul Hasan, Rofiqul Alam Shehab, Asab Azad
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.
By Rongjin Li, Yuanxin Liu, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun