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
Doc2LoRA introduces a method to represent scientific papers as LoRA adapters generated by a hypernetwork, enabling each point in the resulting vector space—whether a real paper or a mixture—to be interpreted by a large language model (LLM). The approach allows both search and generation: it achieves competitive search performance with models like SPECTER2 and EmbeddingGemma, while also enabling the LLM to label subfields and generate abstracts that smoothly transition between papers. The invertible transform ensures every point maps back to an LLM, making the embeddings usable for both querying and creative idea generation.
By Chand Sahil Mansuri, Joel Zachariah, Sadamori Kojaku
arXiv:2606. 15656v1 Announce Type: new Abstract: Modern artificial intelligence remains fundamentally divided between the continuous, probabilistic spaces of Foundation Models and the discrete, deterministic structures of Knowledge Graphs.
By Sahil Rajesh Dhayalkar
arXiv:2606. 13566v1 Announce Type: new Abstract: Current discussions of AI in scientific discovery are often dominated by two visible capabilities: search over existing knowledge and execution through optimization, simulation, and automation.
By Guojun Liao
arXiv:2511. 04873v2 Announce Type: replace-cross Abstract: Prototype selection methods compress a training set, but the existing taxonomy of condensation, edition, hybrid, competence-based, optimization-based, and clustering-based families does not include methods that operate on the multi-scale topological structure of the data.
By Jordan Eckert, Elvan Ceyhan, Henry Schenck
arXiv:2512. 07355v2 Announce Type: replace Abstract: Two traditions of interpretability have evolved side by side but seldom spoken to each other: Concept Bottleneck Models (CBMs), which prescribe what a concept should be, and Sparse Autoencoders (SAEs), which discover what concepts emerge.
By Alexandre Rocchi, Thomas Fel, Gianni Franchi