arXiv:2608. 11810v1 Announce Type: cross Abstract: Air traffic controllers perceive traffic complexity through the radar display, suggesting that a computer vision model operating on the same imagery may provide a natural architecture for modeling controller-perceived complexity; however, whether radar imagery is a viable input format for deep learning vision models remains unclear.
By Hyewook Kim, Byul Kang, Seokbin Yoon, Keumjin Lee
arXiv:2608. 11759v1 Announce Type: cross Abstract: Non-destructive X-ray imaging can reveal internal hazelnut defects that are difficult to detect by external inspection alone; however, automated interpretation remains challenging because of subtle radiographic differences among classes, marked class imbalance, and limited annotated data.
By Giancarlo Sportelli, Nicola Belcari, Roberta Pace, Umberto Bernardo, Sharmin Sultana, Alessandra Toncelli, Matteo Giaccone
arXiv:2608. 11822v1 Announce Type: cross Abstract: A growing body of work reports that language models represent task-relevant latent structure that they fail to use.
By Xining Xun
arXiv:2409. 03682v2 Announce Type: replace Abstract: Learning new tasks by leveraging prior experience is a fundamental trait of intelligent systems.
By El Mahdi Chayti, Martin Jaggi
arXiv:2505. 23696v2 Announce Type: replace Abstract: Solving systems of polynomial equations, particularly those with finitely many solutions, is a crucial challenge across many scientific fields.
By Hiroshi Kera, Nico Pelleriti, Yuki Ishihara, Max Zimmer, Sebastian Pokutta
arXiv:2510. 26690v3 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has become a popular technique for parameter-efficient fine-tuning of large language models (LLMs).
By Amir Reza Mirzaei, Yuqiao Wen, Yanshuai Cao, Lili Mou
arXiv:2511. 18191v2 Announce Type: replace Abstract: Time series forecasting drives operational decisions under tight latency budgets, and autoregressive time series foundation models (TSFMs) increasingly deliver the most accurate forecasts.
By Pranav Subbaraman, Fang Sun, Jinxi Yu, Yue Yao, Huacong Tang, Xiao Luo, Yizhou Sun
arXiv:2604. 27723v2 Announce Type: replace Abstract: Learning algorithms can be significantly improved by routing complex or uncertain inputs to specialized experts, balancing accuracy with computational cost.
By Corinna Cortes, Anqi Mao, Mehryar Mohri, Yutao Zhong
arXiv:2603. 20895v3 Announce Type: replace-cross Abstract: Existing routers rely on semantic query features or handcrafted features, which often fail to capture model-specific failures or intrinsic task difficulty.
By Tanay Varshney, Annie Surla, Michelle Xu, Gomathy Venkata Krishnan, Maximilian Jeblick, David Austin, Neal Vaidya, Davide Onofrio
arXiv:2608. 12150v1 Announce Type: new Abstract: Standard evaluation of large language models assumes stable model rankings across inference conditions.
By Rodrigo Guedes de Souza, Alison R. Panisson
arXiv:2608. 11493v1 Announce Type: new Abstract: Traditional offline recommendation evaluation relies heavily on complex, manually maintained feature pipelines that are difficult to scale.
By Alireza S. Ziabari, Kat Ellis, Colleen Chan, Ding Tong
arXiv:2608. 11244v1 Announce Type: new Abstract: Construction standards are critical for building safety and sustainability.
By Jia-Rui Lin, Junxi Guo, Keyin Chen, Peng Pan
arXiv:2608. 11317v1 Announce Type: cross Abstract: High-resolution images of unprocessed surgical breast tissue can be obtained using microscopy with ultraviolet surface excitation (MUSE).
By Pouya Afshin, Tianling Niu, Tongtong Lu, David Helminiak, Julie Jorns, Mollie Patton, Tina Yen, Donghye Ye, Bing Yu
arXiv:2608. 11280v1 Announce Type: cross Abstract: Skin cancer diagnosis from dermoscopic images remains challenging due to high intra-class variability, inter-class similarity, class imbalance, and the limited interpretability of deep learning models.
By Rofiqul Islam, Lilatul Ferdouse
arXiv:2604. 07801v2 Announce Type: replace-cross Abstract: Large language models are trained and evaluated on quantitative reasoning tasks written in clean, emotionally neutral language.
By Atahan Dokme, Benjamin Reichman, Larry Heck
arXiv:2608. 11239v1 Announce Type: cross Abstract: Administering Database Management Systems (DBMS) instances requires Database Administrators (DBA) to balance performance in terms of Service Level Agreement (SLA) against resource usage, often prompting RAM over-allocation that wastes memory.
By Yifan Wang, Patrick Royer, Rapha\"el F\'eraud, David Delande
arXiv:2608. 11343v1 Announce Type: new Abstract: Multimodal retrieval and classification across different types of media, spanning text, images,video and audio, has traditionally relied on dual-encoder models that align visual and textual representations through contrastive learning.
By Archan Dutta, Vyanktesh Kanungo
arXiv:2608. 11238v1 Announce Type: new Abstract: Retrieval-augmented generation improves the factuality of large language models by grounding responses in retrieved evidence, yet existing evaluation frameworks struggle to provide consistent, fine-grained diagnostics across the diverse spectrum of user queries, ranging from close-ended fact-seeking to open-ended explanatory requests.
By Jeonghwan Choi, Taewon Yun, Minjeong Ban, Gyeonghun Sun, Jae-Gil Lee, Hwanjun Song
arXiv:2608. 11573v1 Announce Type: cross Abstract: Achieving effective self-correction, where models verify and correct their own mistakes, remains a fundamental challenge for large language models (LLMs).
By Vu Duc Anh, Nhat M. Hoang, Do Xuan Long, Cong-Duy Nguyen, Ponhvoan Srey, Luu Anh Tuan
arXiv:2608. 11656v1 Announce Type: new Abstract: Recent advances in EEG foundation models have demonstrated the potential of large-scale pretraining to enable generalizable neural decoding across subjects, recording environments, and datasets.
By Myeong-Ju Cho, Hye-Bin Shin, Seo-Hyun Lee, Seong-Whan Lee