Tootfinder

InstructX: Towards Unified Visual Editing with MLLM Guidance
Chong Mou, Qichao Sun, Yanze Wu, Pengze Zhang, Xinghui Li, Fulong Ye, Songtao Zhao, Qian He
https://arxiv.org/abs/2510.08485

InstructX: Towards Unified Visual Editing with MLLM Guidance
With recent advances in Multimodal Large Language Models (MLLMs) showing strong visual understanding and reasoning, interest is growing in using them to improve the editing performance of diffusion models. Despite rapid progress, most studies lack an in-depth analysis of MLLM design choices. Moreover, the integration of MLLMs and diffusion models remains an open challenge in some difficult tasks, such as video editing. In this paper, we present InstructX, a unified framework for image and video…

Dual Knowledge-Enhanced Two-Stage Reasoner for Multimodal Dialog Systems
Xiaolin Chen, Xuemeng Song, Haokun Wen, Weili Guan, Xiangyu Zhao, Liqiang Nie
https://arxiv.org/abs/2509.07817

Dual Knowledge-Enhanced Two-Stage Reasoner for Multimodal Dialog Systems
Textual response generation is pivotal for multimodal \mbox{task-oriented} dialog systems, which aims to generate proper textual responses based on the multimodal context. While existing efforts have demonstrated remarkable progress, there still exist the following limitations: 1) \textit{neglect of unstructured review knowledge} and 2) \textit{underutilization of large language models (LLMs)}. Inspired by this, we aim to fully utilize dual knowledge (\textit{i.e., } structured attribute and un…

SparkUI-Parser: Enhancing GUI Perception with Robust Grounding and Parsing
Hongyi Jing, Jiafu Chen, Chen Rao, Ziqiang Dang, Jiajie Teng, Tianyi Chu, Juncheng Mo, Shuo Fang, Huaizhong Lin, Rui Lv, Chenguang Ma, Lei Zhao
https://arxiv.org/abs/2509.04908

SparkUI-Parser: Enhancing GUI Perception with Robust Grounding and Parsing
The existing Multimodal Large Language Models (MLLMs) for GUI perception have made great progress. However, the following challenges still exist in prior methods: 1) They model discrete coordinates based on text autoregressive mechanism, which results in lower grounding accuracy and slower inference speed. 2) They can only locate predefined sets of elements and are not capable of parsing the entire interface, which hampers the broad application and support for downstream tasks. To address the a…

A Privacy-Preserving Recommender for Filling Web Forms Using a Local Large Language Model
Amirreza Nayyeri, Abbas Rasoolzadegan
https://arxiv.org/abs/2509.01527 https://

A Privacy-Preserving Recommender for Filling Web Forms Using a Local Large Language Model
Web applications are increasingly used in critical domains such as education, finance, and e-commerce. This highlights the need to ensure their failure-free performance. One effective method for evaluating failure-free performance is web form testing, where defining effective test scenarios is key to a complete and accurate evaluation. A core aspect of this process involves filling form fields with suitable values to create effective test cases. However, manually generating these values is time…

FYI, here’s the exact language of 50 U.S.C. § 1702(b) which spells out how media mail is exempt:
https://kind.social/@wbwolf/115103183358864928

A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Ming Hu, Chenglong Ma, Wei Li, Wanghan Xu, Jiamin Wu, Jucheng Hu, Tianbin Li, Guohang Zhuang, Jiaqi Liu, Yingzhou Lu, Ying Chen, Chaoyang Zhang, Cheng Tan, Jie Ying, Guocheng Wu, Shujian Gao, Pengcheng Chen, Jiashi Lin, Haitao Wu, Lulu Chen, Fengxiang Wang, Yuanyuan Zhang, Xiangyu Zhao, Feilong Tang, Encheng Su, Junzhi Ning, Xinyao Liu, Ye Du, Changkai Ji, Cheng Tang, Huihui Xu, Ziyang Chen, Ziyan Hu…

A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, yet their progress is shaped by the complex nature of scientific data. This survey presents a comprehensive, data-centric synthesis that reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate. We formulate a unified taxonomy of scientific data and a hierarchical model of scientific knowledge, emphasizing the…

Sen. Josh Hawley launches a probe of Meta following a report on its AI policy allowing sensual chats with kids, tells Zuckerberg to produce docs by September 19 (Jonathan Vanian/CNBC)
https://www.cnbc.com/2025/08/15/meta-ai-bot-child-investigation.html

Sen. Hawley to probe Meta AI bot policies for children following damning report
Sen. Josh Hawley called on Meta CEO Mark Zuckerberg to preserve relevant materials, including emails.

Here are the email addresses to make your voice heard at Disney/ABC:
👉General Issues: DisneyABCtv_Support@disney.com
👉Publicity / Corporate Comms: Ashley.r.kline@disney.com
👉Audience Information: cheryl.pratesa@abc.com
👉Investor Relations: alexia.quadrani@disney.com
👉Disney Advertising: Disney.Advertising@disney.com
If anyone needs sample language, here’s a starting point you can copy and paste:
Dear Disney/ABC Leadership,
Your decision to silence a comedian for a harmless joke while protecting and platforming voices that openly promote cruelty is indefensible. It reeks of hypocrisy, cowardice, and corporate complicity.
Suppressing humor while elevating propaganda is how fascism gains ground. You are helping it along.
This is a warning: every dollar I once spent on Disney subscriptions, merchandise, films, and parks will now be withheld. I will also be encouraging others to cancel, divest, and pressure your advertisers and shareholders until Disney/ABC adopts clear, viewpoint-neutral standards that respect all voices equally.
History will not remember kindly the corporations that bent to fear and power, silencing dissent while protecting division. Right now, Disney/ABC is on the wrong side of that history. Shame on you. We will remember!
Sincerely,
- From Rob Brezsny on a different Social Media platform
#BoycottDisney #JimmyKImmel #FreedomOfSpeech

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs
Ruyi Ding, Tianhong Xu, Xinyi Shen, Aidong Adam Ding, Yunsi Fei
https://arxiv.org/abs/2508.15036

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs
The transformer architecture has become a cornerstone of modern AI, fueling remarkable progress across applications in natural language processing, computer vision, and multimodal learning. As these models continue to scale explosively for performance, implementation efficiency remains a critical challenge. Mixture of Experts (MoE) architectures, selectively activating specialized subnetworks (experts), offer a unique balance between model accuracy and computational cost. However, the adaptive …

When Instructions Multiply: Measuring and Estimating LLM Capabilities of Multiple Instructions Following
Keno Harada, Yudai Yamazaki, Masachika Taniguchi, Edison Marrese-Taylor, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo
https://arxiv.org/abs/2509.21051

When Instructions Multiply: Measuring and Estimating LLM Capabilities of Multiple Instructions Following
As large language models (LLMs) are increasingly applied to real-world scenarios, it becomes crucial to understand their ability to follow multiple instructions simultaneously. To systematically evaluate these capabilities, we introduce two specialized benchmarks for fundamental domains where multiple instructions following is important: Many Instruction-Following Eval (ManyIFEval) for text generation with up to ten instructions, and Style-aware Mostly Basic Programming Problems (StyleMBPP) for…