Tootfinder

Got a new op-ed in LNC/LancasterOnline this morning, on the importance of the curiosity-driven science the National Science Foundation is supposed to support
"For every Thermus aquaticus there are hundreds of scientific projects that end in nothing more than a peer-reviewed research article and some fond memories of fieldwork. But we need those hundreds of curiosity-driven studies to find that one lucky, world-changing discovery."

An author from deepintodev.com reviews how modern database storage engines store, retrieve, and update database table rows on memory and disk. This serves as a foundation to understanding how indexes and clustered indexes work, the concerned data structures, and their use cases; these are reviewed as well.
"How Databases Store Your Tables on Disk"

Text-to-LoRA: Instant Transformer Adaption
Rujikorn Charakorn, Edoardo Cetin, Yujin Tang, Robert Tjarko Lange
https://arxiv.org/abs/2506.06105 https://

Text-to-LoRA: Instant Transformer Adaption
While Foundation Models provide a general tool for rapid content creation, they regularly require task-specific adaptation. Traditionally, this exercise involves careful curation of datasets and repeated fine-tuning of the underlying model. Fine-tuning techniques enable practitioners to adapt foundation models for many new applications but require expensive and lengthy training while being notably sensitive to hyper-parameter choices. To overcome these limitations, we introduce Text-to-LoRA (T2…

This https://arxiv.org/abs/2503.05231 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interaction
Cutting-edge robot learning techniques including foundation models and imitation learning from humans all pose huge demands on large-scale and high-quality datasets which constitute one of the bottleneck in the general intelligent robot fields. This paper presents the Kaiwu multimodal dataset to address the missing real-world synchronized multimodal data problems in the sophisticated assembling scenario,especially with dynamics information and its fine-grained labelling. The dataset first provi…

A "Wenlu" Brain System for Multimodal Cognition and Embodied Decision-Making: A Secure New Architecture for Deep Integration of Foundation Models and Domain Knowledge
Liang Geng
https://arxiv.org/abs/2506.00570

A "Wenlu" Brain System for Multimodal Cognition and Embodied Decision-Making: A Secure New Architecture for Deep Integration of Foundation Models and Domain Knowledge
With the rapid penetration of artificial intelligence across industries and scenarios, a key challenge in building the next-generation intelligent core lies in effectively integrating the language understanding capabilities of foundation models with domain-specific knowledge bases in complex real-world applications. This paper proposes a multimodal cognition and embodied decision-making brain system, ``Wenlu", designed to enable secure fusion of private knowledge and public models, unified proc…

Human-Centric Evaluation for Foundation Models
Yijin Guo, Kaiyuan Ji, Xiaorong Zhu, Junying Wang, Farong Wen, Chunyi Li, Zicheng Zhang, Guangtao Zhai
https://arxiv.org/abs/2506.01793

Human-Centric Evaluation for Foundation Models
Currently, nearly all evaluations of foundation models focus on objective metrics, emphasizing quiz performance to define model capabilities. While this model-centric approach enables rapid performance assessment, it fails to reflect authentic human experiences. To address this gap, we propose a Human-Centric subjective Evaluation (HCE) framework, focusing on three core dimensions: problem-solving ability, information quality, and interaction experience. Through experiments involving Deepseek R…

This https://arxiv.org/abs/2502.05206 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Safety at Scale: A Comprehensive Survey of Large Model Safety
The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artificial Intelligence (AI). These models are now foundational to a wide range of applications, including conversational AI, recommendation systems, autonomous driving, content generation, medical diagnostics, and scientific discovery. However, their widespread deployment also exposes them to significant safety risks, raisin…

An Overview of GPU-based First-Order Methods for Linear Programming and Extensions
Haihao Lu, Jinwen Yang
https://arxiv.org/abs/2506.02174 https://

An Overview of GPU-based First-Order Methods for Linear Programming and Extensions
The rapid progress in GPU computing has revolutionized many fields, yet its potential in mathematical programming, such as linear programming (LP), has only recently begun to be realized. This survey aims to provide a comprehensive overview of recent advancements in GPU-based first-order methods for LP, with a particular focus on the design and development of cuPDLP. We begin by presenting the design principles and algorithmic foundation of the primal-dual hybrid gradient (PDHG) method, which f…

This https://arxiv.org/abs/2412.03293 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Diffusion-VLA: Generalizable and Interpretable Robot Foundation Model via Self-Generated Reasoning
In this paper, we present DiffusionVLA, a novel framework that seamlessly combines the autoregression model with the diffusion model for learning visuomotor policy. Central to our approach is a next-token prediction objective, enabling the model to reason effectively over the user's query in the context of current observations. Subsequently, a diffusion model is attached to generate robust action outputs. To enhance policy learning through self-reasoning, we introduce a novel reasoning injectio…

This https://arxiv.org/abs/2411.03328 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Foundation Models for Rapid Autonomy Validation
We are motivated by the problem of autonomous vehicle performance validation. A key challenge is that an autonomous vehicle requires testing in every kind of driving scenario it could encounter, including rare events, to provide a strong case for safety and show there is no edge-case pathological behavior. Autonomous vehicle companies rely on potentially millions of miles driven in realistic simulation to expose the driving stack to enough miles to estimate rates and severity of collisions. To …