arXiv:2610.18572v1 Announce Type: new
Abstract: Automated post-training transforms pre-trained large language models (LLMs) into models capable of handling diverse domains by automatically constructi...
By Ruohong Liu, Bohan Tang, Lekang Jiang, Jack Umenberger, Yiwen Guo
arXiv:2609.00059v1 Announce Type: cross
Abstract: Materials property prediction remains difficult in low-data settings, where many target properties are supported by only a limited number of labeled...
By Weiran Wang, Xintong Huo, Yueying Wang, Yusi Fan, Wenyan Wang, Xin Feng, Ruihao Xin, Lan Huang, Kewei Li, Fengfeng Zhou
CORE improves compositional reasoning in multimodal language models by distilling a cross‑attentive reranker’s fine‑grained judgments into the embedding model. It generates candidate lists across five compositional matching levels and trains with a Rank‑KL objective to replicate the reranker’s ranking. Experiments on COLA, SUGARCREPE++, and NEGBENCH show CORE‑RERANKER‑8B outperforms Jina‑Reranker by 10.7 points, while CORE‑EMBED‑8B achieves the best overall average among evaluated embeddings, with gains also transferring to the MCMR benchmark without harming COCO or Flickr30K retrieval.
By Tingyu Song, Mingxin Li, Yanzhao Zhang, Dingkun Long, Chu Liu, Pengjun Xie, Yilun Zhao, Shu Wu
Whole slide image (WSI) classification is an evidence-driven task, where diagnostic cues are often sparse, spatially organized, and class-dependent. Existing MIL and vision-language methods aggregate a large pool of patch features into a single global slide representation.
arXiv:2505. 20161v2 Announce Type: replace-cross Abstract: Effective generalization in language models depends critically on the diversity of their training data.
By Jaehun Jung, Seungju Han, Ximing Lu, Skyler Hallinan, David Acuna, Shrimai Prabhumoye, Mostafa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Yejin Choi
TCSP3.0 is an improved template‑based crystal structure prediction method that enforces coefficient‑preserving element mappings, substitutes species simultaneously on parsed structures, and validates each candidate’s composition and cell. It adds periodic‑number retrieval, soft oxidation‑state evidence, radius‑compatibility scoring, structural deduplication, and ORB‑v3 machine‑learning‑potential relaxation and energy ranking, achieving Top‑1 and Top‑20 match rates of 72.2 % and 90.6 % on the T180 benchmark and outperforming previous TCSP2.0 and several deep generative models. The method runs in a median of 28 s per target on two CPU cores, roughly ten times faster than CrystalFlow, with most gains attributed to the correctness fixes and structural deduplication.
By Ying Feng, Jeffrey Hu, Lai Wei, Liang Tao, Madeline Miklos, Dipannoy Das Gupta, Hossein Erfanshekooh, Jianjun Hu