Tootfinder

SwizzlePerf: Hardware-Aware LLMs for GPU Kernel Performance Optimization
Arya Tschand, Muhammad Awad, Ryan Swann, Kesavan Ramakrishnan, Jeffrey Ma, Keith Lowery, Ganesh Dasika, Vijay Janapa Reddi
https://arxiv.org/abs/2508.20258

SwizzlePerf: Hardware-Aware LLMs for GPU Kernel Performance Optimization
Large language models (LLMs) have shown progress in GPU kernel performance engineering using inefficient search-based methods that optimize around runtime. Any existing approach lacks a key characteristic that human performance engineers rely on for near-optimal utilization -- hardware-awareness. By leveraging the workload's specific memory access patterns, architecture specifications, filtered profiling logs, and reflections on historical performance, we can make software-level optimizations t…

Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks
Ryan Ghamandi, Yahya Hmaiti, Mykola Maslych, Ravi Kiran Kattoju, Joseph J. LaViola Jr
https://arxiv.org/abs/2508.17124

Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks
We explore natural user interactions using a virtual reality simulation of a robot arm for assembly tasks. Using a Wizard-of-Oz study, participants completed collaborative LEGO and instructive PCB assembly tasks, with the robot responding under experimenter control. We collected voice, hand tracking, and gaze data from users. Statistical analyses revealed that instructive and collaborative scenarios elicit distinct behaviors and adopted strategies, particularly as tasks progress. Users tended t…

Toward Generalized Autonomous Agents: A Neuro-Symbolic AI Framework for Integrating Social and Technical Support in Education
Ryan Hare, Ying Tang
https://arxiv.org/abs/2508.18406

Toward Generalized Autonomous Agents: A Neuro-Symbolic AI Framework for Integrating Social and Technical Support in Education
One of the enduring challenges in education is how to empower students to take ownership of their learning by setting meaningful goals, tracking their progress, and adapting their strategies when faced with setbacks. Research has shown that this form of leaner-centered learning is best cultivated through structured, supportive environments that promote guided practice, scaffolded inquiry, and collaborative dialogue. In response, educational efforts have increasingly embraced artificial-intellig…

Igniting VLMs toward the Embodied Space
Andy Zhai, Brae Liu, Bruno Fang, Chalse Cai, Ellie Ma, Ethan Yin, Hao Wang, Hugo Zhou, James Wang, Lights Shi, Lucy Liang, Make Wang, Qian Wang, Roy Gan, Ryan Yu, Shalfun Li, Starrick Liu, Sylas Chen, Vincent Chen, Zach Xu
https://arxiv.org/abs/2509.11766

Igniting VLMs toward the Embodied Space
While foundation models show remarkable progress in language and vision, existing vision-language models (VLMs) still have limited spatial and embodiment understanding. Transferring VLMs to embodied domains reveals fundamental mismatches between modalities, pretraining distributions, and training objectives, leaving action comprehension and generation as a central bottleneck on the path to AGI. We introduce WALL-OSS, an end-to-end embodied foundation model that leverages large-scale multimoda…

Automated Cervical Os Segmentation for Camera-Guided, Speculum-Free Screening
Aoife McDonald-Bowyer, Anjana Wijekoon, Ryan Laurance Love, Katie Allan, Scott Colvin, Aleksandra Gentry-Maharaj, Adeola Olaitan, Danail Stoyanov, Agostino Stilli, Sophia Bano
https://arxiv.org/abs/2509.10593

Automated Cervical Os Segmentation for Camera-Guided, Speculum-Free Screening
Cervical cancer is highly preventable, yet persistent barriers to screening limit progress toward elimination goals. Speculum-free devices that integrate imaging and sampling could improve access, particularly in low-resource settings, but require reliable visual guidance. This study evaluates deep learning methods for real-time segmentation of the cervical os in transvaginal endoscopic images. Five encoder-decoder architectures were compared using 913 frames from 200 cases in the IARC Cervical…

A path towards AI-scale, interoperable biological data
Brian Aevermann, Andrea Califano, Chi-Li Chiu, Nathan Clack, William M. Clemons Jr., Jonah Cool Florence D. D'Orazi, Joseph L. DeRisi, Joshua E. Elias, Elizabeth Fahsbender, Scott E. Fraser, Carlos G. Gonzalez, Matthias Haury, Theofanis Karaletsos, Shana O. Kelley, Aly A. Khan, Alan R. Lowe, Emma Lundberg, Ryan A. McClure, Stephani Otte, Evan O. Paull, Lo\"ic A. Royer, Dana Sadgat, Sandra L. Schmid, Samantha Scovanner, Cat…

A path towards AI-scale, interoperable biological data
Biology is at the precipice of a new era where AI accelerates and amplifies the ability to study how cells operate, organize, and work as systems, revealing why disease happens and how to correct it. Organizations globally are prioritizing AI to accelerate basic research, drug discovery, personalized medicine, and synthetic biology. However, despite these opportunities, scientific data have proven a bottleneck, and progress has been slow and fragmented. Unless the scientific community takes a t…