Tootfinder

lkml_reply: Linux kernel reply network
A network of replies among email address found on the Linux kernel mailing list. Nodes are email addresses and each directed edge represents a reply from email address i to email address j. The date of this snapshot is uncertain.
This network has 63399 nodes and 1096440 edges.
Tags: Social, Communication, Unweighted, Multigraph, Timestamps

"Come to mind the names of violent battles: DR-DOS, Taligent, AmigaOS, Newton OS, Windows for Pen Computing, Copland, Vino, NeXTSTEP, Novell NetWare, JavaOS, BeOS, DoJ vs Microsoft, Rhapsody, POSIX, Linux is communism, Linux is a cancer, Samizdat, Windows Vista, SCO, Symbian, Solaris, OpenSolaris, systemd, Windows Phone, MeeGo, Tizen, Firefox OS, Sailfish OS. Countless mythical man-month hours were lost. Millions of lines of code were fired."

GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary Lines
Yumeng Fu, Jiayin Zhu, Lingling Zhang, Bo Zhao, Shaoxuan Ma, Yushun Zhang, Yanrui Wu, Wenjun Wu
https://arxiv.org/abs/2508.06226

GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary Lines
Geometry problem solving (GPS) requires models to master diagram comprehension, logical reasoning, knowledge application, numerical computation, and auxiliary line construction. This presents a significant challenge for Multimodal Large Language Models (MLLMs). However, existing benchmarks for evaluating MLLM geometry skills overlook auxiliary line construction and lack fine-grained process evaluation, making them insufficient for assessing MLLMs' long-step reasoning abilities. To bridge these …

lkml_thread: Linux kernel mailing list
A bipartite network of contributions by users to threads on the Linux kernel mailing list. A left node is a person, and a right node is a thread, and each timestamped edge (i,j,t) denotes that user i contributed to thread j at time t. The date of the snapshot is not given.
This network has 379554 nodes and 1565683 edges.
Tags: Social, Communication, Unweighted, Timestamps

LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering
Jielin Qiu, Zuxin Liu, Zhiwei Liu, Rithesh Murthy, Jianguo Zhang, Haolin Chen, Shiyu Wang, Ming Zhu, Liangwei Yang, Juntao Tan, Zhepeng Cen, Cheng Qian, Shelby Heinecke, Weiran Yao, Silvio Savarese, Caiming Xiong, Huan Wang
https://arxiv.org/abs/2…

LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering
The emergence of long-context language models with context windows extending to millions of tokens has created new opportunities for sophisticated code understanding and software development evaluation. We propose LoCoBench, a comprehensive benchmark specifically designed to evaluate long-context LLMs in realistic, complex software development scenarios. Unlike existing code evaluation benchmarks that focus on single-function completion or short-context tasks, LoCoBench addresses the critical e…

VisCodex: Unified Multimodal Code Generation via Merging Vision and Coding Models
Lingjie Jiang, Shaohan Huang, Xun Wu, Yixia Li, Dongdong Zhang, Furu Wei
https://arxiv.org/abs/2508.09945

VisCodex: Unified Multimodal Code Generation via Merging Vision and Coding Models
Multimodal large language models (MLLMs) have significantly advanced the integration of visual and textual understanding. However, their ability to generate code from multimodal inputs remains limited. In this work, we introduce VisCodex, a unified framework that seamlessly merges vision and coding language models to empower MLLMs with strong multimodal code generation abilities. Leveraging a task vector-based model merging technique, we integrate a state-of-the-art coding LLM into a strong vis…

biblically accurate angels blew up cuz they made neet husks in youtube comments sections glimpse the marvelous for the 1st time since they were 7, then the internet spent the last decade working tirelessly, out of love, to petrify and print millions of their now-predictable listless forms out for window dressing as if they're any other coming and going meme, unable to see any other way to love anything in the 21st century.

🍳 Scientists discover how heat 'awakens' carbon food source for deep Earth biosphere
#geology

Scientists discover how heat 'awakens' carbon food source for deep Earth biosphere
Deep beneath the ocean's surface lies Earth's largest carbon reservoir: marine sediments that have accumulated organic matter over millions of years. Long assumed to be permanently "locked away," this vast carbon pool is far more dynamic than scientists previously believed, according to a new international study.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation
Liu He, Xiao Zeng, Yizhi Song, Albert Y. C. Chen, Lu Xia, Shashwat Verma, Sankalp Dayal, Min Sun, Cheng-Hao Kuo, Daniel Aliaga
https://arxiv.org/abs/2507.08513

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation
Multimodal Large Language Models (MLLMs) struggle with accurately capturing camera-object relations, especially for object orientation, camera viewpoint, and camera shots. This stems from the fact that existing MLLMs are trained on images with limited diverse camera-object relations and corresponding textual descriptions. To address this, we propose a synthetic generation pipeline to create large-scale 3D visual instruction datasets. Our framework takes 3D assets as input and uses rendering and…

Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization
Zhengzhao Lai, Youbin Zheng, Zhenyang Cai, Haonan Lyu, Jinpu Yang, Hongqing Liang, Yan Hu, Benyou Wang
https://arxiv.org/abs/2509.09307

Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization
Materials characterization is fundamental to acquiring materials information, revealing the processing-microstructure-property relationships that guide material design and optimization. While multimodal large language models (MLLMs) have recently shown promise in generative and predictive tasks within materials science, their capacity to understand real-world characterization imaging data remains underexplored. To bridge this gap, we present MatCha, the first benchmark for materials characteriz…