Tootfinder

GMT: General Motion Tracking for Humanoid Whole-Body Control
Zixuan Chen, Mazeyu Ji, Xuxin Cheng, Xuanbin Peng, Xue Bin Peng, Xiaolong Wang
https://arxiv.org/abs/2506.14770

GMT: General Motion Tracking for Humanoid Whole-Body Control
The ability to track general whole-body motions in the real world is a useful way to build general-purpose humanoid robots. However, achieving this can be challenging due to the temporal and kinematic diversity of the motions, the policy's capability, and the difficulty of coordination of the upper and lower bodies. To address these issues, we propose GMT, a general and scalable motion-tracking framework that trains a single unified policy to enable humanoid robots to track diverse motions in t…

Google makes Gemini 2.5 Flash and Pro generally available and introduces 2.5 Flash-Lite, which it says is its most cost-efficient and fastest 2.5 model yet (Tulsee Doshi/The Keyword)
https://blog.google/products/gemini/gemini-2-5-model-family-expands/…

We’re expanding our Gemini 2.5 family of models
Gemini 2.5 Flash and Pro are now generally available, and we’re introducing 2.5 Flash-Lite, our most cost-efficient and fastest 2.5 model yet.

Single Index Bandits: Generalized Linear Contextual Bandits with Unknown Reward Functions
Yue Kang, Mingshuo Liu, Bongsoo Yi, Jing Lyu, Zhi Zhang, Doudou Zhou, Yao Li
https://arxiv.org/abs/2506.12751

Single Index Bandits: Generalized Linear Contextual Bandits with Unknown Reward Functions
Generalized linear bandits have been extensively studied due to their broad applicability in real-world online decision-making problems. However, these methods typically assume that the expected reward function is known to the users, an assumption that is often unrealistic in practice. Misspecification of this link function can lead to the failure of all existing algorithms. In this work, we address this critical limitation by introducing a new problem of generalized linear bandits with unknown…

Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
Hyeonseo Lee, Juhyun Park, Jihyong Oh, Chanho Eom
https://arxiv.org/abs/2506.12413

Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
Person Re-identification (ReID) aims to retrieve images of the same individual captured across non-overlapping camera views, making it a critical component of intelligent surveillance systems. Traditional ReID methods assume that the training and test domains share similar characteristics and primarily focus on learning discriminative features within a given domain. However, they often fail to generalize to unseen domains due to domain shifts caused by variations in viewpoint, background, and l…

Generalized False Vacuum Skyrme model
L. A. Ferreira, L. R. Livramento
https://arxiv.org/abs/2506.12235 https://arxiv.org/pdf/2506.12…

Generalized False Vacuum Skyrme model
We propose a generalization of the False Vacuum Skyrme model for any simple compact Lie groups $G$ that leads to Hermitian symmetric spaces. The Skyrme field that in the original theory takes its values in $SU(2)$ Lie group, now takes its values in $G$, while the remaining fields correspond to the entries of a symmetric, positive, and invertible $\dim G \times \dim G$-dimensional matrix $h$. This model is also an extension of the generalized BPS Skyrme model. We prove that the global minima cor…

Fronthaul-Aware User-Centric Generalized Cell-Free Massive MIMO Systems
Zahra Mobini, Ahmet Hasim Gokceoglu, Li Wang, Gunnar Peters, Hien Quoc Ngo
https://arxiv.org/abs/2506.14494

Fronthaul-Aware User-Centric Generalized Cell-Free Massive MIMO Systems
We consider fronthaul-limited generalized zeroforcing-based cell-free massive multiple-input multiple-output (CF-mMIMO) systems with multiple-antenna users and multipleantenna access points (APs) relying on both cooperative beamforming (CB) and user-centric (UC) clustering. The proposed framework is very general and can be degenerated into different special cases, such as pure CB/pure UC clustering, or fully centralized CB/fully distributed beamforming. We comprehensively analyze the spectral e…

Michigan Got Rid of Most Abortion Restrictions. Now AG Dana Nessel is Challenging the Final One. - Ms. Magazine
https://msmagazine.com/2025/06/17/michigan-abortion-restrictions-attorney-general-dana-nessel/

Quantum algorithm for solving generalized eigenvalue problems with application to the Schr\"odinger equation
Grzegorz Rajchel-Mieldzio\'c, Szymon Pli\'s, Emil Zak
https://arxiv.org/abs/2506.13534

Quantum algorithm for solving generalized eigenvalue problems with application to the Schrödinger equation
Accurate computation of multiple eigenvalues of quantum Hamiltonians is essential in quantum chemistry, materials science, and molecular spectroscopy. Estimating excited-state energies is challenging for classical algorithms due to exponential scaling with system size, posing an even harder problem than ground-state calculations. We present a quantum algorithm for estimating eigenvalues and singular values of parameterized matrix families, including solving generalized eigenvalue problems that …

NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving
Ren Xin, Hongji Liu, Xiaodong Mei, Wenru Liu, Maosheng Ye, Zhili Chen, Jun Ma
https://arxiv.org/abs/2506.14589

NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving
Integrating General Models (GMs) such as Large Language Models (LLMs), with Specialized Models (SMs) in autonomous driving tasks presents a promising approach to mitigating challenges in data diversity and model capacity of existing specialized driving models. However, this integration leads to problems of asynchronous systems, which arise from the distinct characteristics inherent in GMs and SMs. To tackle this challenge, we propose NetRoller, an adapter that incorporates a set of novel mechan…

General and Estimable Learning Bound Unifying Covariate and Concept Shifts
Hongbo Chen, Li Charlie Xia
https://arxiv.org/abs/2506.12829 https://

General and Estimable Learning Bound Unifying Covariate and Concept Shifts
Generalization under distribution shift remains a core challenge in modern machine learning, yet existing learning bound theory is limited to narrow, idealized settings and is non-estimable from samples. In this paper, we bridge the gap between theory and practical applications. We first show that existing bounds become loose and non-estimable because their concept shift definition breaks when the source and target supports mismatch. Leveraging entropic optimal transport, we propose new support…