arXiv:2608. 02641v1 Announce Type: cross Abstract: Large language models (LLMs) can translate natural-language optimization problems into solver-ready formulations, but direct code generation is brittle: schema, indexing, and semantic errors can cause compilation failures, infeasible models, or incorrect objectives, while iterative repair, search, and multi-agent workflows increase inference cost.
By Penglin Zhu, Linhai Zhang, Jungang Xu, Xinchi Wei, Xiuqi Wu
arXiv:2608. 02663v1 Announce Type: cross Abstract: Accurate ICU mortality prediction requires modeling irregular clinical observations across heterogeneous entity types.
By Mohammad Nasir Uddin, Rahnuma Tabassum Orpita, Asaduzzaman Anik, Eklachur Rahman Bhuiyan, Marjahan Risalat, SM Wali Ullah, Asif Ahamed
arXiv:2608. 02665v1 Announce Type: cross Abstract: A benchmark score is a measurement instrument, yet most benchmarks read each item at a single canonical surface form.
By Yongxi Zhou, Junwei Yao, Yuanzhe Liu, Zihan Dong, Wenbo Ye, Jiaxi Wen, Lai Yun Choi
arXiv:2608. 02669v1 Announce Type: cross Abstract: Docker Hub is the registry underneath most container deployments, and a flaw in a widely reused base image is inherited by every image built on it.
By Cristhian Kapelinski, Beatriz Machado, Diego Kreutz
arXiv:2608. 02680v1 Announce Type: cross Abstract: Tool-using language-model agents repeatedly rediscover procedures they have already executed, producing traces that mix reusable structure with retries, exploration, accidental ordering, and repeated lookups.
By Salma El Yadouni (EPFL), Guanyi Li (Binome Technologies)
arXiv:2608. 02685v1 Announce Type: cross Abstract: Coding-agent benchmarks increasingly cover long-horizon, end-to-end, and interactive development, but typically retain one requested outcome or a fixed change sequence.
By Zetong Xiong, Qiao Zhao, Jun Zhang, Xueying Lyu, Zhi Li, Yixiang Tu, Xiaowen Yang, Yunjie Zhang, Yufeng Wang, Zhe Zhang, Kaize Yu, Hanwen Du, Zhongkai Sun, Zhuoxin Liu, Zekun Lin, Jianwen Yang, Ruining Chen, Ying Zhang, Tingxuan Pan, Ke Chen, Shubin Han, Chuanhao Sun, Yehua Yang
arXiv:2608. 02712v1 Announce Type: cross Abstract: Kernel generation for hardware accelerators such as GPUs and NPUs has become a proving ground for large language models (LLMs), and state-of-the-art systems raise correctness through pipelines that couple LLMs with agentic reinforcement learning and evolutionary search.
By Yansong Sun, Shenxiu Wu, Siyuan Chen, Runlin Hou, Junhao Qiu, Junming Cao, Shudi Shao, Zhichao Lu, Qingfu Zhang
arXiv:2608. 02830v1 Announce Type: cross Abstract: Many-shot in-context learning (ICL) lets vision-language models (VLMs) adapt from image--label demonstrations without weight updates, and is widely assumed to improve as more demonstrations are supplied.
By Mohammad Rostami
arXiv:2608. 02843v1 Announce Type: cross Abstract: Persistent agent memory must adapt as later outcomes change earlier evidence, yet mutable retrieval weights create an attribution problem: reviewers must distinguish authorized adaptation from database tampering.
By Walid Saidi
arXiv:2608. 03041v1 Announce Type: cross Abstract: Deep reinforcement learning (DRL) approaches for flexible job shop scheduling (FJSP) heavily rely on attention-centric architectures to achieve state-of-the-art performance.
By Dhivya Dharshini Kannan, Wei Zhang, Jieyi Bi, Yingpeng Du, Tianjun Wei, Jie Zhang, Zuming Liu, Anupam Trivedi
arXiv:2608. 03070v1 Announce Type: cross Abstract: Frontier AI model developers increasingly rely on layered safeguards to prevent catastrophic misuse, but little public evidence exists on how much protection these safeguards provide, or how consistently across developers.
By Jasper Timm, Lukas Struppek, Ziwei Xu, Grace Cheong, Oscar Mata, Dan Zhao, Mick Yang, Isadora De Andrade, Xiaojun Jia, Yiming Li, Samuel Bauer, Heather McIntyre, Adam Gleave, Edward Yee, Kellin Pelrine
arXiv:2608. 03079v1 Announce Type: cross Abstract: Breast core needle biopsy (CNB) is central to breast cancer diagnosis yet remains challenging because limited tissue sampling, lesion heterogeneity, and subtle morphologic overlap can obscure subtype distinctions.
By Ting Yin, Danning Li, Chen Shu, Xiaoxia Yao, Boyu Fu, Yujing Chang, Tianyu Shi, Mengna Feng, Jie Chen, Jing Fu, Xiuli Xiao, Tianlin Li, Mumin Shao, Jiaxin Bi, Wenchuan Zhang, Xiaoyan Wu, Xiao Han, Zhang Zhang, Yuhao Yi, Hong Bu
arXiv:2608. 03103v1 Announce Type: cross Abstract: Diffusion policies have shown strong performance in learning complex, multi-modal behaviors for robotic manipulation.
By Rishabh Shukla, Adithya Santhosh, Shaili Gandhi, Samrudh Moode, Satyandra K. Gupta
arXiv:2608. 03112v1 Announce Type: cross Abstract: Vision-language models excel at image and video understanding but suffer from high inference latency due to the need to process thousands of tokens per image, limiting their deployment on resource-constrained edge devices and in real-time surveillance applications.
By Paribesh Regmi, Qingshuang Chen, Chi Zhang, Heba Aly, Yelin Kim, Hongda Mao
arXiv:2608. 03135v1 Announce Type: cross Abstract: Text-to-image diffusion models can generate individual concepts well, but they often omit or merge concepts incorrectly with multiple concepts.
By Ning Zhu, An Chen, Mengfei Zhao, Juntao Xu, Jingze Liang, Boyuan Gu, Liang-Jian Deng
arXiv:2608. 03270v1 Announce Type: cross Abstract: GUI grounding maps natural-language instructions to click locations and is essential for reliable GUI agents.
By Zichuan Fu, Shirong Wang, Wenlin Zhang, Guojing Li, Yimin Deng, Jingtong Gao, Junjia Qi, Hanyu Yan, Yefeng Zheng, Xiaopeng Li, Wanyu Wang, Xian Wu, Xiangyu Zhao
arXiv:2608. 03341v1 Announce Type: cross Abstract: Large language models (LLMs) have substantially improved code generation, yet achieving strong functional correctness remains difficult, especially for heterogeneous programming tasks where a single prompting strategy and a single directly generated output are often insufficient.
By Erxue Zhou, Jingxiang Meng, Aofan Liu
arXiv:2608. 03407v1 Announce Type: cross Abstract: Road network segmentation from satellite imagery remains challenging due to large geographic variation in road appearance, occlusions, and domain shifts introduced by differing resolutions and sensors.
By Sanayya, Rakshith Sathish, Ashwathi Nambiar
arXiv:2608. 03591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents offer a promising approach to attack chain reconstruction by retrieving and interpreting heterogeneous telemetry to infer ordered attacker actions.
By Xuyang Liu, Yibin Han, Zhenwei Zhang, Kai Chang, Zhiwei Xu, Tian Qiu, Weixian Deng, Jiabao Gao, Xiaolin Peng, Hai Wan, Xibin Zhao
arXiv:2608. 03742v1 Announce Type: cross Abstract: Sound effects play a crucial role in conveying actions, events, and environmental cues across digital applications, often requiring a high degree of variation and contextual adaptability.
By Sandy Abdo, Bill Kapralos, Priyamvada Tripathi, KC Collins, Adam Dubrowski