Tootfinder

This https://arxiv.org/abs/2505.16196 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SEM: Enhancing Spatial Understanding for Robust Robot Manipulation
A key challenge in robot manipulation lies in developing policy models with strong spatial understanding, the ability to reason about 3D geometry, object relations, and robot embodiment. Existing methods often fall short: 3D point cloud models lack semantic abstraction, while 2D image encoders struggle with spatial reasoning. To address this, we propose SEM (Spatial Enhanced Manipulation model), a novel diffusion-based policy framework that explicitly enhances spatial understanding from two com…

Asterinas: A Linux ABI-Compatible, Rust-Based Framekernel OS with a Small and Sound TCB
Yuke Peng, Hongliang Tian, Zhang Junyang, Ruihan Li, Chengjun Chen, Jianfeng Jiang, Jinyi Xian, Xiaolin Wang, Chenren Xu, Diyu Zhou, Yingwei Luo, Shoumeng Yan, Yinqian Zhang
https://arxiv.org/abs/2506.03876

Asterinas: A Linux ABI-Compatible, Rust-Based Framekernel OS with a Small and Sound TCB
How can one build a feature-rich, general-purpose, Rust-based operating system (OS) with a minimal and sound Trusted Computing Base (TCB) for memory safety? Existing Rust-based OSes fall short due to their improper use of unsafe Rust in kernel development. To address this challenge, we propose a novel OS architecture called framekernel that realizes Rust's full potential to achieve intra-kernel privilege separation, ensuring TCB minimality and soundness. We present OSTD, a streamlined framework…

Beyond C/C : Probabilistic and LLM Methods for Next-Generation Software Reverse Engineering
Zhuo Zhuo, Xiangyu Zhang
https://arxiv.org/abs/2506.03504 http…

Beyond C/C++: Probabilistic and LLM Methods for Next-Generation Software Reverse Engineering
This proposal discusses the growing challenges in reverse engineering modern software binaries, particularly those compiled from newer system programming languages such as Rust, Go, and Mojo. Traditional reverse engineering techniques, developed with a focus on C and C++, fall short when applied to these newer languages due to their reliance on outdated heuristics and failure to fully utilize the rich semantic information embedded in binary programs. These challenges are exacerbated by the limi…

Kwai Keye-VL Technical Report
Kwai Keye Team, Biao Yang, Bin Wen, Changyi Liu, Chenglong Chu, Chengru Song, Chongling Rao, Chuan Yi, Da Li, Dunju Zang, Fan Yang, Guorui Zhou, Hao Peng, Haojie Ding, Jiaming Huang, Jiangxia Cao, Jiankang Chen, Jingyun Hua, Jin Ouyang, Kaibing Chen, Kaiyu Jiang, Kaiyu Tang, Kun Gai, Shengnan Zhang, Siyang Mao, Sui Huang, Tianke Zhang, Tingting Gao, Wei Chen, Wei Yuan, Xiangyu Wu, Xiao Hu, Xingyu Lu, Yang Zhou, Yi-Fan Zhang, Yiping Yang, Yulong Chen, Zhenh…

Kwai Keye-VL Technical Report
While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities on static images, they often fall short in comprehending dynamic, information-dense short-form videos, a dominant medium in today's digital landscape. To bridge this gap, we introduce \textbf{Kwai Keye-VL}, an 8-billion-parameter multimodal foundation model engineered for leading-edge performance in short-video understanding while maintaining robust general-purpose vision-language abilities. The development of Ke…

EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaboration
Beichen Huang, Ran Cheng, Kay Chen Tan
https://arxiv.org/abs/2506.02049 https:…

EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaboration
We introduce EvoGit, a decentralized multi-agent framework for collaborative software development driven by autonomous code evolution. EvoGit deploys a population of independent coding agents, each proposing edits to a shared codebase without centralized coordination, explicit message passing, or shared memory. Instead, all coordination emerges through a Git-based phylogenetic graph that tracks the full version lineage and enables agents to asynchronously read from and write to the evolving cod…

This https://arxiv.org/abs/2504.01285 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Parameterized Attenuated Exchange for Generalized TDHF@$v_W$ Applications
Building upon our previously developed time-dependent Hartree-Fock (TDHF)@$v_W$ method, based on many-body perturbation theory and specifically the Bethe-Salpeter Equation (BSE), we introduce a parameterization scheme for the attenuated exchange kernel, $v_W(|r - r'|)$. In the original method, $v_W$ was determined individually for each system via an efficient stochastic short-time TD Hartree propagation for the screened Coulomb interaction, $W(r,r')$. The new parameterization leverage…

ClustOpt: A Clustering-based Approach for Representing and Visualizing the Search Dynamics of Numerical Metaheuristic Optimization Algorithms
Gjorgjina Cenikj, Ga\v{s}per Petelin, Tome Eftimov
https://arxiv.org/abs/2507.02337

ClustOpt: A Clustering-based Approach for Representing and Visualizing the Search Dynamics of Numerical Metaheuristic Optimization Algorithms
Understanding the behavior of numerical metaheuristic optimization algorithms is critical for advancing their development and application. Traditional visualization techniques, such as convergence plots, trajectory mapping, and fitness landscape analysis, often fall short in illustrating the structural dynamics of the search process, especially in high-dimensional or complex solution spaces. To address this, we propose a novel representation and visualization methodology that clusters solution …

Enhancing Live Broadcast Engagement: A Multi-modal Approach to Short Video Recommendations Using MMGCN and User Preferences
Saeid Aghasoleymani Najafabadi
https://arxiv.org/abs/2506.23085

Enhancing Live Broadcast Engagement: A Multi-modal Approach to Short Video Recommendations Using MMGCN and User Preferences
The purpose of this paper is to explore a multi-modal approach to enhancing live broadcast engagement by developing a short video recommendation system that incorporates Multi-modal Graph Convolutional Networks (MMGCN) with user preferences. In order to provide personalized recommendations tailored to individual interests, the proposed system takes into account user interaction data, video content features, and contextual information. With the aid of a hybrid approach combining collaborative fi…

SSCard: Substring Cardinality Estimation using Suffix Tree-Guided Learned FM-Index
Yirui Zhan, Wen Nie, Jun Gao
https://arxiv.org/abs/2505.24312 https://…

SSCard: Substring Cardinality Estimation using Suffix Tree-Guided Learned FM-Index
Accurate cardinality estimation of substring queries, which are commonly expressed using the SQL LIKE predicate, is crucial for query optimization in database systems. While both rule-based methods and machine learning-based methods have been developed to optimize various aspects of cardinality estimation, their absence of error bounds may result in substantial estimation errors, leading to suboptimal execution plans. In this paper, we propose SSCard, a novel SubString Cardinality estimator tha…