Tootfinder

On the $4$-clique cover number of graphs
Yihan Chen, Jialin He, Tianying Xie
https://arxiv.org/abs/2506.10478 https://arxiv.org/pdf/2…

On the $4$-clique cover number of graphs
In 1966, Erdős, Goodman, and Pósa proved that $\lfloor n^2/4 \rfloor$ cliques are sufficient to cover all edges in any $n$-vertex graph, with tightness achieved by the balanced complete bipartite graph. This result was generalized by Dau, Milenkovic, and Puleo, who showed that at most $\lfloor \frac n 3 \rfloor \lfloor \frac {n+1} 3 \rfloor \lfloor \frac {n+2} 3 \rfloor$ cliques are needed to cover all triangles in any $n$-vertex graph $G$, and the bound is best possible as witnessed by the b…

MLLM-Based UI2Code Automation Guided by UI Layout Information
Fan Wu, Cuiyun Gao, Shuqing Li, Xin-Cheng Wen, Qing Liao
https://arxiv.org/abs/2506.10376 htt…

MLLM-Based UI2Code Automation Guided by UI Layout Information
Converting user interfaces into code (UI2Code) is a crucial step in website development, which is time-consuming and labor-intensive. The automation of UI2Code is essential to streamline this task, beneficial for improving the development efficiency. There exist deep learning-based methods for the task; however, they heavily rely on a large amount of labeled training data and struggle with generalizing to real-world, unseen web page designs. The advent of Multimodal Large Language Models (MLLMs…

Multimodal Large Language Models: A Survey
Longzhen Han, Awes Mubarak, Almas Baimagambetov, Nikolaos Polatidis, Thar Baker
https://arxiv.org/abs/2506.10016

Multimodal Large Language Models: A Survey
Multimodal Large Language Models (MLLMs) have rapidly evolved beyond text generation, now spanning diverse output modalities including images, music, video, human motion, and 3D objects, by integrating language with other sensory modalities under unified architectures. This survey categorises six primary generative modalities and examines how foundational techniques, namely Self-Supervised Learning (SSL), Mixture of Experts (MoE), Reinforcement Learning from Human Feedback (RLHF), and Chain-of-…

Nowcasting the euro area with social media data
Konstantin Boss, Luigi Longo, Luca Onorante
https://arxiv.org/abs/2506.10546 https://…

Nowcasting the euro area with social media data
Using a state-of-the-art large language model, we extract forward-looking and context-sensitive signals related to inflation and unemployment in the euro area from millions of Reddit submissions and comments. We develop daily indicators that incorporate, in addition to posts, the social interaction among users. Our empirical results show consistent gains in out-of-sample nowcasting accuracy relative to daily newspaper sentiment and financial variables, especially in unusual times such as the (p…

Broadband Tunable Deep-UV Emission from AI-Optimized Nonlinear Metasurface Architectures
Omar A. M. Abdelraouf
https://arxiv.org/abs/2506.10442 https://

Broadband Tunable Deep-UV Emission from AI-Optimized Nonlinear Metasurface Architectures
Metasurfaces represent a pivotal advancement in nonlinear optics, leveraging high-Q resonant cavities to enhance harmonic generation. Multi-layer metasurfaces (MLMs) further amplify this potential by intensifying light-matter interactions within individual meta-atoms at the nanoscale. However, maximizing nonlinear efficiency demands extreme field confinement through optimized designs of large geometric and material parameters, which exceed traditional simulation's computational ability. To over…

Learning Obfuscations Of LLM Embedding Sequences: Stained Glass Transform
Jay Roberts, Kyle Mylonakis, Sidhartha Roy, Kaan Kale
https://arxiv.org/abs/2506.09452

Learning Obfuscations Of LLM Embedding Sequences: Stained Glass Transform
The high cost of ownership of AI compute infrastructure and challenges of robust serving of large language models (LLMs) has led to a surge in managed Model-as-a-service deployments. Even when enterprises choose on-premises deployments, the compute infrastructure is typically shared across many teams in order to maximize the return on investment. In both scenarios the deployed models operate only on plaintext data, and so enterprise data owners must allow their data to appear in plaintext on a …

This https://arxiv.org/abs/2505.18675 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Can MLLMs Guide Me Home? A Benchmark Study on Fine-Grained Visual Reasoning from Transit Maps
Multimodal large language models (MLLMs) have recently achieved significant progress in visual tasks, including semantic scene understanding and text-image alignment, with reasoning variants enhancing performance on complex tasks involving mathematics and logic. However, their capacity for reasoning tasks involving fine-grained visual understanding remains insufficiently evaluated. To address this gap, we introduce ReasonMap, a benchmark designed to assess the fine-grained visual understanding …

The geometric bookkeeping guide to Feynman integral reduction and $\varepsilon$-factorised differential equations
Iris Bree, Federico Gasparotto, Antonela Matija\v{s}i\'c, Pouria Mazloumi, Dmytro Melnichenko, Sebastian P\"ogel, Toni Teschke, Xing Wang, Stefan Weinzierl, Konglong Wu, Xiaofeng Xu
https://arxiv.org/abs/250…

The geometric bookkeeping guide to Feynman integral reduction and $\varepsilon$-factorised differential equations
We report on three improvements in the context of Feynman integral reduction and $\varepsilon$-factorised differential equations: Firstly, we show that with a specific choice of prefactors, we trivialise the $\varepsilon$-dependence of the integration-by-parts identities. Secondly, we observe that with a specific choice of order relation in the Laporta algorithm, we directly obtain a basis of master integrals, whose differential equation on the maximal cut is in Laurent polynomial form with res…

Can Sound Replace Vision in LLaVA With Token Substitution?
Ali Vosoughi, Jing Bi, Pinxin Liu, Yunlong Tang, Chenliang Xu
https://arxiv.org/abs/2506.10416 h…

Can Sound Replace Vision in LLaVA With Token Substitution?
While multimodal systems have achieved impressive advances, they typically rely on text-aligned representations rather than directly integrating audio and visual inputs. This reliance can limit the use of acoustic information in tasks requiring nuanced audio understanding. In response, SoundCLIP explores direct audio-visual integration within multimodal large language models (MLLMs) by substituting CLIP's visual tokens with audio representations and selecting sound-relevant patch tokens in mode…

This https://arxiv.org/abs/2505.16652 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding
Recent advancements in multimodal large language models (MLLMs) have significantly improved performance in visual question answering. However, they often suffer from hallucinations. In this work, hallucinations are categorized into two main types: initial hallucinations and snowball hallucinations. We argue that adequate contextual information can be extracted directly from the token interaction process. Inspired by causal inference in the decoding strategy, we propose to leverage causal masks …