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
arXiv:2609.23231v1 Announce Type: new
Abstract: Cross-lingual information retrieval (CLIR) is increasingly important in multi-national industries, where critical technical evidence may exist in a dif...
By Mahdi Astaraki, Mohammad Khodadad, Reza Namazi, Mohammad Arshi Saloot, Amir Reza Behzad Moghadam, Hamidreza Mahyar, Soheila Samiee
arXiv:2511. 10657v2 Announce Type: replace-cross Abstract: We study self-supervised patent representation learning with contrastive objectives.
By You Zuo (ALMAnaCH), Kim Gerdes (LISN), Eric Villemonte de La Clergerie (ALMAnaCH), Beno\^it Sagot (ALMAnaCH)
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:2606. 01632v1 Announce Type: cross Abstract: Estimating the economic contribution of a single patent inside a product that embodies tens of thousands of patents is a long-standing unsolved problem in intellectual property economics.
By Joy Bose
arXiv:2604. 19047v2 Announce Type: replace-cross Abstract: Existing QA benchmarks typically assume distinct documents with minimal overlap, yet real-world retrieval-augmented generation (RAG) systems operate on corpora such as financial reports, legal codes, and patents, where information is highly redundant and documents exhibit strong inter-document similarity.
By Hanjun Cho, Jay-Yoon Lee
SciTBERT is a family of BERT-derived language models trained on chronologically consistent corpora of scientific papers, patents, and educational web text, with training cutoffs spanning 2013 to 2025. The models are further post‑trained using paper and patent citations to create the SciTBERT‑CI family, which better captures temporal dynamics. Experiments show that these chronologically consistent encoders outperform earlier domain‑specific models, especially when training data is limited by early year restrictions, and they perform well on the new PatRepEval benchmark of patent‑related tasks at the science‑technology interface.
By Thomas Gebhart, Russell J. Funk
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
Sparse Coverage is an unsupervised semantic retrieval framework designed for patent prior‑art search. It maps local span embeddings to a sparse vocabulary of embedding‑space centers chosen via a coverage‑oriented k‑center objective, allowing spans to activate nearby centers and produce sparse representations that work with inverted‑index retrieval. Experiments on CLEF‑IP 2013 demonstrate that Sparse Coverage matches or surpasses dense patent encoders in document‑level recall while remaining competitive at the passage level, making it an effective first‑stage retrieval approach for patent search.
By You Zuo (ALMAnaCH), Kim Gerdes (LISN, Qatent, STL), \'Eric de la Clergerie (ALMAnaCH), Beno\^it Sagot (ALMAnaCH)
arXiv:2607. 13044v1 Announce Type: cross Abstract: The European Patent Office (EPO) reported record filings in 2025, and the 2026 EPO Guidelines hold applicants strictly responsible for LLM-assisted content under Article 83 and Rule 42, creating pressure to triage suspected AI-generated patent text.
By Anubhab Banerjee
The paper introduces concept provenance, a framework that categorizes query concepts into backstory-supported, human-central, human-tail, and candidate answer-side zones to detect knowledge intrusion in LLM-generated search queries. Applying this framework to 77,004 queries across 100 topics, 8 LLMs, and 5 prompt conditions, the authors find that 7.40% of non-generic concepts are candidate answer-side and appear in 97 of 100 topics, with human validation revealing 68.2% relaxed precision and equal contributions from knowledge and deployment intrusion. The study shows that while prompt conditions do not eliminate intrusion, post-generation concept-provenance selection can achieve 99% elimination, positioning concept provenance as a diagnostic tool rather than an evaluation predictor.
By Chenglong Ma, Xinye Wanyan, Danula Hettiachchi, Ziqi Xu, Jeffrey Chan
arXiv:2608. 16824v1 Announce Type: new Abstract: Generative Engine Optimization (GEO) modifies web content to increase its likelihood of being selected and cited by generative search engines.
By Junjie Chu, Ye Leng, Mingjie Li, Yun Shen, Xinyue Shen, Yang Zhang