arXiv:2609.36550v1 Announce Type: new
Abstract: Retrieval-augmented generation is widely used in professional writing, yet whether retrieval grounds revision or merely injects templates is rarely tes...
By Josepha Michiko Leo, Hyun-seok Min, Yehoon Jang, Irvan Zidny, Jin-Woo Chung, Sungchul Choi
arXiv:2609.15015v1 Announce Type: new
Abstract: Synthetic perturbations appear to offer inexpensive calibration data for LLM evaluators in biomedical ML, where expert review is scarce. Yet a planted...
By Sidi Chang, Peiying Zhu
arXiv:2605. 04135v2 Announce Type: replace-cross Abstract: Readers of applied-domain LLM capability evaluations want to know what AI systems can currently do.
By David Gringras, Misha Salahshoor
arXiv:2607. 29539v1 Announce Type: cross Abstract: Standard AI-text detection benchmarks compare human-written text against text generated directly by large language models (LLMs).
By Gaetano Perrone, Simon Pietro Romano
The paper evaluates unsupervised embedding geometries for patent claim classification, comparing hyperbolic and Euclidean Variational Autoencoders trained on 45,000 US patents. Hyperbolic embeddings retrieve cross-category prior art 52% more often than Euclidean embeddings (18.76% vs. 12.34% of top‑10 results across 3,000 queries). The study also links classification breadth and patents per subgroup to model performance, offering a practical signal for when the method is most useful.
By Sujith Subramanian, P N Bhargav Teja, Susen Sarkar, Rajasekhara Reddy Duvvuru Muni
BLANC (Blank Landscape Analysis through NPMI Conditioning) is a three‑phase pipeline that uses multi‑view neural topic modeling across application/use, novelty, and inventive step, computes Normalized Pointwise Mutual Information (NPMI) to measure cross‑dimensional cluster association, and introduces a conditional detection step that flags combinations whose NPMI drops when the corpus is filtered by a keyword. The drop is quantified by a new metric, ΔNPMI, which identifies combinations that are established globally but unexplored locally. BLANC was evaluated on two USPTO corpora—machine learning/AI and glass compositions—by artificially depleting known technology combinations; it recovered 34.1% and 27.3% of the depleted pairs, respectively, while random removals rarely recovered the target, and it successfully identified a fluorine surface‑treatment × warpage‑suppression candidate in a proprietary float‑glass case.
By Shuichi Miyazawa, Kensuke Fujii