Tootfinder

Representation Potentials of Foundation Models for Multimodal Alignment: A Survey
Jianglin Lu, Hailing Wang, Yi Xu, Yizhou Wang, Kuo Yang, Yun Fu
https://arxiv.org/abs/2510.05184

Representation Potentials of Foundation Models for Multimodal Alignment: A Survey
Foundation models learn highly transferable representations through large-scale pretraining on diverse data. An increasing body of research indicates that these representations exhibit a remarkable degree of similarity across architectures and modalities. In this survey, we investigate the representation potentials of foundation models, defined as the latent capacity of their learned representations to capture task-specific information within a single modality while also providing a transferabl…

ODKE : Ontology-Guided Open-Domain Knowledge Extraction with LLMs
Samira Khorshidi, Azadeh Nikfarjam, Suprita Shankar, Yisi Sang, Yash Govind, Hyun Jang, Ali Kasgari, Alexis McClimans, Mohamed Soliman, Vishnu Konda, Ahmed Fakhry, Xiaoguang Qi
https://arxiv.org/abs/2509.04696

ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs
Knowledge graphs (KGs) are foundational to many AI applications, but maintaining their freshness and completeness remains costly. We present ODKE+, a production-grade system that automatically extracts and ingests millions of open-domain facts from web sources with high precision. ODKE+ combines modular components into a scalable pipeline: (1) the Extraction Initiator detects missing or stale facts, (2) the Evidence Retriever collects supporting documents, (3) hybrid Knowledge Extractors apply …

Multimodal Foundation Model-Driven User Interest Modeling and Behavior Analysis on Short Video Platforms
Yushang Zhao, Yike Peng, Li Zhang, Qianyi Sun, Zhihui Zhang, Yingying Zhuang
https://arxiv.org/abs/2509.04751

Multimodal Foundation Model-Driven User Interest Modeling and Behavior Analysis on Short Video Platforms
With the rapid expansion of user bases on short video platforms, personalized recommendation systems are playing an increasingly critical role in enhancing user experience and optimizing content distribution. Traditional interest modeling methods often rely on unimodal data, such as click logs or text labels, which limits their ability to fully capture user preferences in a complex multimodal content environment. To address this challenge, this paper proposes a multimodal foundation model-based…

Ah yes, MIPS Technologies.
https://mips.com/products/hardware/
The famous designer of MIPS (microprocessor without interlocked pipeline stages) architecture processors.
Such a well named company /s

IP Processors - MIPS
Products Forging a New Path for the AI Compute Era Billions of Units Shipped MIPS Sub-Systems – More than a Core A foundation of compute DNA built over decades with billions of units shipped Bring Your Own Accelerators Flexible Integration Coherent Through Coherence Manager Fabric or Connect Direct to CPU RISC-V Core RISC-V Multi-threading Custom […]

Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers
Yuji Takubo, Daniele Gammelli, Marco Pavone, Simone D'Amico
https://arxiv.org/abs/2510.03544

Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers
Spacecraft rendezvous enables on-orbit servicing, debris removal, and crewed docking, forming the foundation for a scalable space economy. Designing such missions requires rapid exploration of the tradespace between control cost and flight time across multiple candidate targets. However, multi-objective optimization in this setting is challenging, as the underlying constraints are often highly nonconvex, and mission designers must balance accuracy (e.g., solving the full problem) with efficienc…

UItron: Foundational GUI Agent with Advanced Perception and Planning
Zhixiong Zeng, Jing Huang, Liming Zheng, Wenkang Han, Yufeng Zhong, Lei Chen, Longrong Yang, Yingjie Chu, Yuzhi He, Lin Ma
https://arxiv.org/abs/2508.21767

UItron: Foundational GUI Agent with Advanced Perception and Planning
GUI agent aims to enable automated operations on Mobile/PC devices, which is an important task toward achieving artificial general intelligence. The rapid advancement of VLMs accelerates the development of GUI agents, owing to their powerful capabilities in visual understanding and task planning. However, building a GUI agent remains a challenging task due to the scarcity of operation trajectories, the availability of interactive infrastructure, and the limitation of initial capabilities in fou…

Foundation Model-Driven Grasping of Unknown Objects via Center of Gravity Estimation
Kang Xiangli, Yage He, Xianwu Gong, Zehan Liu, Yuru Bai
https://arxiv.org/abs/2507.19242 htt…

Foundation Model-Driven Grasping of Unknown Objects via Center of Gravity Estimation
This study presents a grasping method for objects with uneven mass distribution by leveraging diffusion models to localize the center of gravity (CoG) on unknown objects. In robotic grasping, CoG deviation often leads to postural instability, where existing keypoint-based or affordance-driven methods exhibit limitations. We constructed a dataset of 790 images featuring unevenly distributed objects with keypoint annotations for CoG localization. A vision-driven framework based on foundation mode…

A First Look at Privacy Risks of Android Task-executable Voice Assistant Applications
Shidong Pan, Yikai Ge, Xiaoyu Sun
https://arxiv.org/abs/2509.23680 https://

A First Look at Privacy Risks of Android Task-executable Voice Assistant Applications
With the development of foundation AI technologies, task-executable voice assistants (VAs) have become more popular, enhancing user convenience and expanding device functionality. Android task-executable VAs are applications that are capable of understanding complex tasks and performing corresponding operations. Given their prevalence and great autonomy, there is no existing work examine the privacy risks within the voice assistants from the task-execution pattern in a holistic manner. To fill …

MedDINOv3: How to adapt vision foundation models for medical image segmentation?
Yuheng Li, Yizhou Wu, Yuxiang Lai, Mingzhe Hu, Xiaofeng Yang
https://arxiv.org/abs/2509.02379 ht…

MedDINOv3: How to adapt vision foundation models for medical image segmentation?
Accurate segmentation of organs and tumors in CT and MRI scans is essential for diagnosis, treatment planning, and disease monitoring. While deep learning has advanced automated segmentation, most models remain task-specific, lacking generalizability across modalities and institutions. Vision foundation models (FMs) pretrained on billion-scale natural images offer powerful and transferable representations. However, adapting them to medical imaging faces two key challenges: (1) the ViT backbone …

From Seeing to Experiencing: Scaling Navigation Foundation Models with Reinforcement Learning
Honglin He, Yukai Ma, Wayne Wu, Bolei Zhou
https://arxiv.org/abs/2507.22028 https:/…

From Seeing to Experiencing: Scaling Navigation Foundation Models with Reinforcement Learning
Navigation foundation models trained on massive webscale data enable agents to generalize across diverse environments and embodiments. However, these models trained solely on offline data, often lack the capacity to reason about the consequences of their actions or adapt through counterfactual understanding. They thus face significant limitations in the real-world urban navigation where interactive and safe behaviors, such as avoiding obstacles and moving pedestrians, are critical. To tackle th…