External memory effectively grounds large language models (LLMs) and vision-language models (VLMs)-based question answering (QA) in relevant multimodal evidence. However, existing memory paradigms represent each memory item in raw text and image forms, so retrieval-based systems must pass the retrieved text or images to the generation LLMs/VLMs, resulting in high token consumption and storage pressure, making it unaffordable for resource-constrained applications.
Most recent 3D reconstruction and editing systems operate on implicit and explicit representations such as NeRF, point clouds, or meshes. While these representations enable high-fidelity rendering, they are fundamentally low-level and hard to control programmatically.
Multi-task vehicle routing problems play a critical role in enhancing efficiency across various industries and service sectors. These problems consist of multiple variants that optimize routing costs while meeting diverse customer constraints.
arXiv:2606. 08893v1 Announce Type: cross Abstract: A small transformer encoder is trained to map Terminal-Wrench trajectories onto a unit sphere where embedding distance approximates the $L_1$ distance between reward and metadata signals.
By Iv\'an Belenky, Joaqu\'in Itria, Steven Johns
arXiv:2606. 08067v1 Announce Type: new Abstract: Graph neural networks (GNNs) are widely deployed on relational data, yet they can leak sensitive or proprietary information about the training graph adjacency, e.
By Zhanke Zhou, Bo Han, Xuan Li, Jiangchao Yao, Sanmi Koyejo, Michael K. Ng
arXiv:2606. 07523v1 Announce Type: cross Abstract: Legal domains in high-resource languages like English have widely adopted artificial intelligence for legal question answering.
By Samir Wagle, Abiral Adhikari, Reewaj Khanal, Batsal Bhandari, Prashant Manandhar, Praveen Acharya, Bal Krishna Bal
arXiv:2604. 08304v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) extends large language models (LLMs) with external knowledge, but this access path also introduces security risks that existing work often conflates with inherent LLM flaws.
By Yuming Xu, Mingtao Zhang, Zhuohan Ge, Haoyang Li, Nicole Hu, Yongqi Zhang, Zhiyuan Wen, Jason Chen Zhang, Qing Li, Lei Chen
arXiv:2601. 16510v3 Announce Type: replace-cross Abstract: Solving massive-scale optimization problems requires scalable first-order methods with low per-iteration cost.
By Liping Tao, Xindi Tong, Chee Wei Tan
arXiv:2606. 09311v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) have shown promising world modeling capabilities, enabling planning in latent space by optimizing action trajectories using methods like the Cross-Entropy Method (CEM).
By Sergi Masip, Jonathan Swinnen, Yutong Hu, Renaud Detry, Tinne Tuytelaars
arXiv:2509. 10534v3 Announce Type: replace-cross Abstract: The attention mechanism in a Transformer architecture matches key to query based on both content -- the what -- and position in a sequence -- the where.
By Anand Gopalakrishnan, Robert Csord\'as, J\"urgen Schmidhuber, Michael C. Mozer
arXiv:2606. 08993v1 Announce Type: new Abstract: We propose LEAF, a learning-enabled ADMM framework for accelerated convex optimization.
By Binh Nguyen, Trinh Tran, Truong X. Nghiem
arXiv:2606. 07843v1 Announce Type: cross Abstract: Schema matching, a critical task for integrating data from diverse sources, seeks to identify correspondences between columns across different schemas.
By Leonard Traeger, Enas Khwaileh, Andreas Behrend, George Karabatis
arXiv:2511. 11041v2 Announce Type: replace-cross Abstract: We find that current sentence-embedding models produce outputs with a consistent bias: every embedding $e$ decomposes as $\tilde e + \mu$, where the mean $\mu$ is near-identical across all sentences.
By Xingyu Ren, Youran Sun, Haoyu Liang
arXiv:2606. 07766v1 Announce Type: cross Abstract: We present a quantum--classical hybrid pipeline for polarimetric material classification that casts this as a point-matching problem.
By Sara Shojaei, Seyed Mohamad Ali Tousi, Emma Bennett, Param Sangani, Ali Shiri Sichani, Ilker Ersoy, Hadi Ali-Akbarpour, Filiz Bunyak, G. N. DeSouza
arXiv:2606. 09800v1 Announce Type: cross Abstract: Multi-agent code generation offers a promising paradigm for autonomous software development by simulating the human software engineering lifecycle.
By Shizhe Lin, Ladan Tahvildari
arXiv:2606. 09520v1 Announce Type: cross Abstract: Can a general-purpose large language model design molecules with the precision of a seasoned chemist?
By Junyi Gong, Zijie Qiu, Ben Zhong Tang
arXiv:2606. 09672v1 Announce Type: new Abstract: Ask a pretrained biomedical language model whether "cortisol 28 ug/dL" and "stock-market volatility" are related, and it returns a cosine similarity of 0.
By Suraj Biswas, Saurabh Gupta, Pritam Mukherjee
arXiv:2605. 17301v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in practice.
By Chenyu Wang, Yueyuan Li, Yingmin Liu, Yang Shu
arXiv:2602. 04402v3 Announce Type: replace-cross Abstract: Performative predictions influence the very outcomes they aim to forecast.
By Julian Rodemann, Unai Fischer-Abaigar, James Bailie, Krikamol Muandet
arXiv:2606. 09327v1 Announce Type: cross Abstract: Football event data constitute a rich spatiotemporal source for quantitative analysis of player actions in team sports.
By Weiran Yang, Daniel Memmert, Maximilian Klemp-Weins