The paper reports the first in‑space demonstration of using machine learning‑based warm starts to accelerate trajectory optimization for the Astrobee free‑flying robot on the International Space Station. A neural network is trained to learn the structure of the trajectory generation problem solved by sequential convex programming, and onboard predictions reduce solver iterations by 60% for rotational dynamics and 50% for obstacle‑laden scenarios. This approach enables real‑time trajectory planning while respecting safety constraints on limited flight hardware.
By Somrita Banerjee, Abhishek Cauligi, Marco Pavone
arXiv:2509.12196v3 Announce Type: replace-cross
Abstract: Standard attention mechanisms in transformers employ static token representations that remain unchanged across all pair-wise computations in...
By Hunjae Lee, Corey Clark
arXiv:2603.07973v2 Announce Type: replace-cross
Abstract: Hierarchical multi-robot exploration decouples frontier allocation from local navigation, making systems brittle in dynamic environments. Wit...
By Ning Liu, Sen Shen, Zheng Li, Sheng Liu, Dongkun Han, Shangke Lyu, Thomas Br\"aunl
arXiv:2605.31196v2 Announce Type: replace-cross
Abstract: Safe human--robot collaboration requires more than visual description: a monitor must determine whether the robot body is safely separated, a...
By Jun Wang, Xiaohao Xu, Xiaonan Huang
arXiv:2608.25577v2 Announce Type: replace-cross
Abstract: With the widespread adoption of personal intelligent agents, users generate massive, heterogeneous data during long-term interactions. Levera...
By Yu Wang, Jiaheng Lu
arXiv:2610.31894v1 Announce Type: new
Abstract: Recent Sparse Autoencoders (SAEs) decompose the representations of large language models (LLMs) into interpretable features, enabling precise readout a...
By Xinyu Guo, Minglai Yang, Clayton T. Morrison, Eduardo Blanco, Mihai Surdeanu
arXiv:2610.32055v1 Announce Type: new
Abstract: Large language models (LLMs) often exhibit sycophancy, altering their answers to align with user suggestions even on objective factual and reasoning ta...
By Anxin Guo, Chirag Pabbaraju, Sara Ahmadian, Kamesh Munagala, Aravindan Vijayaraghavan
arXiv:2610.32333v1 Announce Type: new
Abstract: When LLM-powered agents interact, information is repeatedly shared, expanded, and forgotten, yet multi-agent evaluation typically focuses on final task...
By Haocheng Jiang, Herun Wan, Yongyuan Peng, Minnan Luo
arXiv:2610.32463v1 Announce Type: new
Abstract: While large vision-language models excel at optical character recognition, it remains unclear how their underlying vision encoders process text and its...
By Sugam Karki, Ra\'ul V\'azquez, Yova Kementchedjhieva
arXiv:2610.32691v1 Announce Type: new
Abstract: Natural Language Autoencoders (NLAs) express a model's internal activations in the form of readable text. The verbalizer turns an activation into an En...
By Kristina \v{S}ekrst, Barbara Geld
arXiv:2610.31713v1 Announce Type: cross
Abstract: Advances in Omni-modal Large Language Models (OLLMs) have enabled increasingly seamless human-AI interaction, but their unified processing of heterog...
By Junhao Wang, Yidou Chen, Li Lu, Hongwei Yao, Xiaodan Li, Mengjie Wu, Yuefeng Chen, Zhan Qin, Kui Ren
arXiv:2610.31862v1 Announce Type: cross
Abstract: This article provides an introduction to the mathematical foundations and algorithmic frameworks used to align Large Language Models (LLMs) with huma...
By Siliang Zeng, Luca Viano, Chenliang Li, Jiaxiang Li, Volkan Cevher, Markus Wulfmeier, Stefano Ermon, Alfredo Garcia, Mingyi Hong
arXiv:2610.32513v1 Announce Type: new
Abstract: Resources for toxicity detection in Punjabi remain limited, particularly for musical content that combines linguistic and multimodal information. We pr...
By Lakhjeet Kaur, Jaspreet Kaur, Komal Rani, Yougal Attri, Ramakant KumarJaydeep Kishore, Pravin Kumar
arXiv:2606.01697v3 Announce Type: replace
Abstract: We propose RCEM, a Robust Conversational search EMbedder that is additionally equipped with LLM's query reformulation capability without losing bas...
By Kilho Son, Paul Hsu, Cha Zhang, Dinei Florencio
arXiv:2608.02555v2 Announce Type: replace
Abstract: Arabizi refers to Arabic written in Latin script. Although previous studies have shown that the prevalence and usage of Arabizi vary by factors suc...
By Amr Keleg, Ahmed Amine Ben Abdallah, Taha Yassine, Chadi Helwe, Imane Guellil, Nedjma Ousidhoum
arXiv:2610.26474v2 Announce Type: replace
Abstract: Large language models (LLMs) designed to interact with users are typically trained to respond appropriately to harmful or toxic content. However, e...
By Israel Abebe Azime, Jesujoba O. Alabi, Iffat Maab, Alexander D. Cai, Tadesse Destaw Belay, Atnafu Lambebo Tonja, Jonas Kgomo, Folasade Alabi, Salomey Osei, Saminu Muhammad Aliyu, Nkechinyere Faith Aguobi, Bontu Fufa Balcha, Blessing Sibanda, Davis David, Mouhamadane Mboup, Daud Abolade, Neo Putini, Louis Powell, Kiant'e Brantley, Philipp Slusallek, Dietrich Klakow, Julia Kreutzer
arXiv:2610.31835v1 Announce Type: new
Abstract: Temporary traffic control redirects vehicles through work zones, requiring automated vehicles to connect an interpretation of the visible devices to a...
By Jiaxi Liu, Keshu Wu, Weizhe Tang, Bofeng Cao, Sikai Chen, Xiaopeng Li, Steven T. Parker, Bin Ran
arXiv:2610.31983v1 Announce Type: new
Abstract: Video encoder models are increasingly used as world models for physical AI, where a policy plans by rolling out the latent, so what physics the latent...
By Ayushi Mehrotra, Sai Vidyaranya Nuthalapati
arXiv:2610.32311v1 Announce Type: new
Abstract: Geographic regions are multimodal: satellite imagery captures visual morphology, text conveys human-interpretable semantics, and map structures describ...
By Radoslaw Malek, Szymon Soltysiak, Aleksandra Kawala-Sterniuk, Piotr Szymanski
arXiv:2610.32321v1 Announce Type: new
Abstract: Point-supervised Temporal Action Localization (PTAL) aims to localize action instances using only a single annotated timestamp for each instance. Such...
By Mengnan Liu, Sanping Zhou, Kun Xia, Yonghao Dong, Jie Wang, Gang Hua