Tootfinder

Urban Life 101 🏙️
城市生活 101 🏙️
📷 Nikon FE
🎞️Ilford FP4 Plus, expired 1995
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#filmphotography

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The Emergence of Abstract Thought in Large Language Models Beyond Any Language
Yuxin Chen, Yiran Zhao, Yang Zhang, An Zhang, Kenji Kawaguchi, Shafiq Joty, Junnan Li, Tat-Seng Chua, Michael Qizhe Shieh, Wenxuan Zhang
https://arxiv.org/abs/2506.09890

The Emergence of Abstract Thought in Large Language Models Beyond Any Language
As large language models (LLMs) continue to advance, their capacity to function effectively across a diverse range of languages has shown marked improvement. Preliminary studies observe that the hidden activations of LLMs often resemble English, even when responding to non-English prompts. This has led to the widespread assumption that LLMs may "think" in English. However, more recent results showing strong multilingual performance, even surpassing English performance on specific tasks in other…

I finally got rid of the old bed I’d kept since moving in, so the room feels pretty empty right now.
I’m in the process of reorganizing everything and plan to turn this space into my own anarchist sanctuary.
Soon, I’ll add a bookshelf and more anarchist art to the wall, right next to the communist hammer and sickle symbol.
#Declutter

Clustering of large deviations events in heavy-tailed moving average processes: the catastrophe principle in the short-memory case
Jiaqi Wang, Gennady Samorodnitsky
https://arxiv.org/abs/2506.10256

Clustering of large deviations events in heavy-tailed moving average processes: the catastrophe principle in the short-memory case
How do large deviation events in a stationary process cluster? The answer depends not only on the type of large deviations, but also on the length of memory in the process. Somewhat unexpectedly, it may also depend on the tails of the process. In this paper we work in the context of large deviations for partial sums in moving average processes with short memory and regularly varying tails. We show that the structure of the large deviation cluster in this case markedly differs from the correspon…

Understanding the Performance and Power of LLM Inferencing on Edge Accelerators
Mayank Arya, Yogesh Simmhan
https://arxiv.org/abs/2506.09554 https://

Understanding the Performance and Power of LLM Inferencing on Edge Accelerators
Large Language Models (LLMs) have demonstrated exceptional benefits to a wide range of domains, for tasks as diverse as code generation and robot navigation. While LLMs are usually served from cloud data centers, mission-critical and privacy-sensitive applications may require local hosting of open LLM models. Given the large GPU memory footprint needed for LLMs, edge accelerators such as Nvidia Jetson Orin AGX with 64GB of shared GPU-CPU RAM are a compelling choice. However, the feasibility and…

ArcNeural: A Multi-Modal Database for the Gen-AI Era
Wu Min, Qiao Yuncong, Yu Tan, Chenghu Yang
https://arxiv.org/abs/2506.09467 https://

ArcNeural: A Multi-Modal Database for the Gen-AI Era
ArcNeural introduces a novel multimodal database tailored for the demands of Generative AI and Large Language Models, enabling efficient management of diverse data types such as graphs, vectors, and documents. Its storage-compute separated architecture integrates graph technology, advanced vector indexing, and transaction processing to support real-time analytics and AI-driven applications. Key features include a unified storage layer, adaptive edge collection in MemEngine, and seamless integra…

BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis
Surya Jasper, Minh Luu, Evan Pan, Aakash Tyagi, Michael Quinn, Jiang Hu, David Kebo Houngninou
https://arxiv.org/abs/2506.10501

BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis
Hardware complexity continues to strain verification resources, motivating the adoption of machine learning (ML) methods to improve debug efficiency. However, ML-assisted debugging critically depends on diverse and scalable bug datasets, which existing manual or automated bug insertion methods fail to reliably produce. We introduce BugGen, a first of its kind, fully autonomous, multi-agent pipeline leveraging Large Language Models (LLMs) to systematically generate, insert, and validate realisti…

NDCG-Consistent Softmax Approximation with Accelerated Convergence
Yuanhao Pu, Defu Lian, Xiaolong Chen, Xu Huang, Jin Chen, Enhong Chen
https://arxiv.org/abs/2506.09454

NDCG-Consistent Softmax Approximation with Accelerated Convergence
Ranking tasks constitute fundamental components of extreme similarity learning frameworks, where extremely large corpora of objects are modeled through relative similarity relationships adhering to predefined ordinal structures. Among various ranking surrogates, Softmax (SM) Loss has been widely adopted due to its natural capability to handle listwise ranking via global negative comparisons, along with its flexibility across diverse application scenarios. However, despite its effectiveness, SM …

This https://arxiv.org/abs/2505.16784 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Four Eyes Are Better Than Two: Harnessing the Collaborative Potential of Large Models via Differentiated Thinking and Complementary Ensembles
In this paper, we present the runner-up solution for the Ego4D EgoSchema Challenge at CVPR 2025 (Confirmed on May 20, 2025). Inspired by the success of large models, we evaluate and leverage leading accessible multimodal large models and adapt them to video understanding tasks via few-shot learning and model ensemble strategies. Specifically, diversified prompt styles and process paradigms are systematically explored and evaluated to effectively guide the attention of large models, fully unleas…

Rock-salt ScN(113) layers grown on AlN$(11\bar{2}2)$ by plasma-assisted molecular beam epitaxy
Duc V. Dinh, Esperanza Luna, Oliver Brandt
https://arxiv.org/abs/2506.09715

Rock-salt ScN(113) layers grown on AlN$(11\bar{2}2)$ by plasma-assisted molecular beam epitaxy
Transition-metal nitrides constitute a versatile class of materials with diverse properties and wide-ranging applications. Exploring new surface orientations and uncovering novel properties can enable innovative material configurations with tailored functionalities for device integration. Here, we report the growth and characterization of (85-210)-nm-thick undoped ScN layers on AlN$(11\bar{2}2)$/Al$_{2}$O$_{3}$$(10\bar{1}0)$ templates via plasma-assisted molecular beam epitaxy. X-ray diffractom…