The paper reports that in English all‑words word sense disambiguation (WSD), the scarcity of high‑quality labels—not the models—has become the limiting factor. The authors introduce lexEN, a human‑adjudicated correction layer over the Maru2022 ALL_NEW benchmark, and SenseBench, a living leaderboard for LLM WSD evaluation. They show that frontier large language models reach about 95 % accuracy on lexEN‑v1, that relabeling corpora with these models improves downstream systems, and that fine‑grained WordNet senses are often ill‑posed, with coarsening improving both annotator agreement and model performance.
"whyItMatters":"The study highlights that improving label quality and managing annotation costs are now the critical challenges for advancing WSD performance, as model accuracy is already near its theoretical ceiling."
By Vassili Philippov, Amro Salman, Dmitrii Andreev, Penny Hands, Emil Kaiumov, Pavel Katunin, Anton Nikolaev
The paper introduces VERDICT, a method for validating optical chemical structure recognition (OCSR) outputs by leveraging agreement among multiple recognizers rather than pixel‑space re‑rendering. On 263 ACS journal images, agreement achieved an AUROC of 0.916, far surpassing the 0.547 AUROC of re‑rendering similarity. VERDICT was applied to PMC Open Access, yielding over 6,000 high‑precision structure labels, and is integrated into SES AI’s Molecular Universe platform for image‑based molecular search.
By Yani Guan, Dengpan Dong, Shuang Luo, Zi Wei, Joah Han, Dan Hannah, Yumin Zhang, Qichao Hu, Kang Xu
arXiv:2609.13936v1 Announce Type: new
Abstract: Datasets that ship automatically generated feature annotations invite a question rarely asked of them: would a human agree with those labels? This repo...
By Levent Bulut
The paper investigates whether language models can identify sentences from their training data by using exact duplication counts from publicly released corpora for two model families, OLMo‑2 and Pythia. It finds that for typical duplication levels, models show only a weak trace of exposure, with a rank correlation near –0.08, and that strong signals only appear when a sentence appears roughly a thousand times, at which point fame rather than memory dominates. The study also demonstrates that common membership tests can be misleading, as changing a single word does not alter the model’s preference, and that controlling for register can significantly improve detector performance.
By Arman Nik Khah
arXiv:2609.07901v1 Announce Type: new
Abstract: Weight quantization largely determines the economics of serving open-weight LLMs. Its costs are usually assessed with capability benchmarks, on which 4...
By Dachi Kurtskhalia
The paper audits 22 frontier language models on 12 molecular property regression benchmarks to assess verbatim retrieval of published values. It finds widespread but benchmark‑specific retrieval, with over 50% of models retrieving exact values on five datasets and isolated occurrences on others. Experiments at different reasoning levels show that higher reasoning increases retrieval flags, and attempts to interrupt retrieval reveal that top models can still recognize transformed SMILES and original labels. Suppressing retrieval reduces prediction error variance, indicating that predictive performance is not solely due to memorized values.
By Matthias Busch, Marius Tacke, Sviatlana V. Lamaka, Mikhail L. Zheludkevich, Christian J. Cyron, Roland C. Aydin, Christian Feiler