The paper introduces TriageRA-CCF, a method for adaptively allocating low‑rank LoRA channels in medical large language models based on source‑side signals: answer confidence, clinical coverage, and a counterfactual close‑miss proxy. By supervising a budget router that selects among 2, 4, or 8 active ranks, the approach improves average accuracy over existing LoRA variants on Qwen3‑8B and Llama3.1‑8B, though gains vary across benchmarks. Ablation studies confirm that each signal contributes to better budget decisions, though their combined effect is not uniformly superior across all backbones.
By Shucan Ji, Yining Huang, Hongliang Guo
The paper introduces Knowledge-Aware Semantic Bridging (KASB), a framework designed to improve Retrieval-Augmented Generation (RAG) by aligning the semantic spaces of queries and retrieved contexts. KASB achieves this through intelligent fusion of generative and retrieval-based knowledge in a multistage process, aiming to enhance passage selection quality, relevance, and accuracy. The authors evaluate the method on three popular open-domain Question Answering datasets, demonstrating its effectiveness.
By Xinkai Du, Chao Lv, Yalin Sun, Quanjie Han, Lei Yao, Maosong Sun
arXiv:2609.32352v2 Announce Type: replace-cross
Abstract: Vision-language models (VLMs) have shown increasing potential for medical image understanding, yet their capabilities in ophthalmic imaging r...
By Gujie Shao, Zixun Xie, Xuechun Xing, Ruixiang Wang, Ziyun Lan, Yanlin Qi, Gangyi Zhang, Yuxin Yang, Dawei Li, Haiming Tang
The paper introduces RS-OPSD, a reliable privileged on-policy self-distillation framework designed for ultra‑high‑resolution remote sensing visual question answering. It leverages a new dataset, GeoEvidence‑6K, and a human‑feedback guided skill refinement process to provide explicit question‑relevant evidence. By incorporating context‑preserving visual privilege and correctness‑aligned distillation, RS‑OPSD achieves state‑of‑the‑art performance on several benchmarks without requiring additional visual search or tool calls at inference time.
By Chengjie Jiang, Yunqi Zhou, Jiafeng Yan, Sihang Zhao, Chun Yuan, Jing Li
arXiv:2601.03418v3 Announce Type: replace
Abstract: Trustworthy clinical summarization requires every claim to be traceable to its evidence, yet existing attribution often resolves only to the senten...
By Bohao Chu, Hendrik Damm, Tabea M. G. Pakull, Sameh Frihat, Georg Lodde, Elisabeth Livingstone, Christoph M. Friedrich, Norbert Fuhr
arXiv:2609.36064v1 Announce Type: cross
Abstract: Foundation models are powerful generators, but many engineering domains require structured representations that general-purpose systems handle poorly...
By Sahand Rezaei-Shoshtari, Patryk Wozniczka, Shu Ishida, Gregg Streuber, Farnoosh Javadi, Jeffrey Landes, Angela Ju, Muhammad Azam, Bryan Lim, Johan Luttun, Indrajeet Haldar, Jonathan Shaw, Beatriz Guerra, Ivan Sosnovik, James Stoddart, Robert Giaquinto, Adam Gaier
The paper introduces GeoPatch, a fixed‑scaffold patch encoder that decouples token support from signal geometry for time‑warp robust sequence retrieval. By using geometry‑derived descriptors (slope, curvature, acceleration, affine‑residual, confidence) as continuous conditioning variables, GeoPatch prevents patch boundary drift while still allowing geometry to modulate embeddings. Experiments on ECG, speech, and multivariate time‑series data show that GeoPatch improves early‑rank retrieval under timing variation and highlights a trade‑off between local surface matching and strict non‑overlap retrieval.
By Cassandra Yang, Yufan Tang
SkillCome introduces a skill-evolution framework that uses group contrast optimization and a dual memory system to refine large language model skills. By generating multiple trajectories per question and contrasting successful versus failed ones, it identifies key behavioral divergences to guide skill edits. The dual memory accumulates historical evidence across groups, enabling more generalized and reliable optimization signals, leading to consistent performance gains on diverse benchmarks.
By Haolin Li, Feng Hong, Ang Li, Chilin Fu, Weichang Wu, Ya Zhang, Yanfeng Wang, Xiaolu Zhang, Jiangchao Yao
arXiv:2609.36544v1 Announce Type: cross
Abstract: Generative AI has changed how students produce writing assignments. The final artifact is no longer sufficient to understand the process through whic...
By Divyansh Chandarana, Sandipan De, Vivek Gupta
arXiv:2609.36756v1 Announce Type: cross
Abstract: One-dimensional (1D) variable-length visual tokenizers enable adaptive compression by varying the number of tokens, allowing downstream autoregressiv...
By Jiawei Zhang, Shuhao Liu, Rong Huang, Yuancheng Li, Zhihui Li, Xiaojun Chang, Changlin Li
arXiv:2609.36700v1 Announce Type: cross
Abstract: When conversing with large language models (LLMs), users often begin with a simple question and build towards a multi-hop question through follow-up...
By Pranav Handa, Ariful Azad
arXiv:2609.38155v1 Announce Type: cross
Abstract: Answering questions about long videos often requires connecting events involving the same objects across hours or days. Chronological descriptions an...
By Hui Ren, Lei Fan, Henry Pao, Han Guo, Zeeshan Zia, Ying Chen, Alexander Schwing, Gang Hua
arXiv:2609.31857v2 Announce Type: replace
Abstract: Validating an LLM-as-a-judge requires estimating its agreement with humans, yet annotation budgets rarely allow every item to be multiply labeled....
By Junxuan Li, Arko Mukherjee, Soumyabrata Pal
arXiv:2602.03915v2 Announce Type: replace-cross
Abstract: Tokens are discrete representations that allow modern deep learning to scale by transforming high-dimensional data into sequences that can be...
By Levi Lingsch, Georgios Kissas, Johannes Jakubik, Siddhartha Mishra
arXiv:2609.36488v1 Announce Type: new
Abstract: Large language model (LLM) agents have demonstrated strong performance on complex web navigation tasks, yet they remain brittle in real-world settings...
By Dongchan Shin, Xing Han L\`u, Jiaqi Deng, Jay Gala, Tom\'as Vergara Browne, Jaewon Moon, Fengyuan Liu, Alexandre Drouin, Siva Reddy, Alexandre Lacoste
arXiv:2309.15670v3 Announce Type: replace
Abstract: In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have been increasingly popular in the Bangla language, which is the seventh m...
By Sumit Kumar Banshal, Sajal Das, Shumaiya Akter Shammi, Narayan Ranjan Chakraborty, Vedika Gupta, Mousumi Karmakar
arXiv:2609.36131v1 Announce Type: new
Abstract: Sinhala Sandhi splitting recovers the constituent words or morphemes hidden inside a phonologically merged surface form. The task is important for Sinh...
By Yasas Ekanayaka, Deshan Sumanathilaka
arXiv:2609.36931v1 Announce Type: new
Abstract: Reproducibility is essential for scientific research, yet prior work shows that LLM outputs vary with hardware and batching. We identify an overlooked...
By Mario Sanz-Guerrero, Minh Duc Bui, Manuel Mager, Katharina von der Wense
arXiv:2602.05385v2 Announce Type: replace
Abstract: Text-to-SQL is a key natural language processing task that maps natural language questions to SQL queries, enabling intuitive interaction with web-...
By Tao Liu, Jiafan Lu, Bohan Yu, Pengcheng Wu, Liu Haixin, Guoyu Xu, Li Xiangheng, Lixiao Li, Jiaming Hou, Zhao Shijun, Xinglin Lyu, Kunli Zhang, Yuxiang Jia, Hongyin Zan
arXiv:2609.35823v1 Announce Type: new
Abstract: Vision-language models (VLMs) have made substantial progress in autonomous driving, but their success has primarily been studied in ego-centric scenes....
By Kang Yang, Shuai Liu, Hang Li, Yance Fang, Deying Li, Yongcai Wang