Tootfinder

heise | Knicken und tippen: Logitech Flip Folio fürs iPad im Test
Der Zubehörspezialist bringt eine herausnehmbare Tastatur in einer iPad-Hülle unter. Taugt sie etwas im Vergleich zum Magic Keyboard?
https://www…

Samsung hofft mit Galaxy S26 auf "zweite Blütezeit" des Smartphone-Geschäfts
Samsung plant für 2026 eine "zweite Blütezeit" seines Smartphone-Geschäfts und will mit der Galaxy-S26-Serie und Foldables an alte Absatzerfolge anschließen.

Looking out for text salting - abuse and scamming via hidden CSS and HTML
https://blog.talosintelligence.com/too-salty-to-handle-exposing-cases-of-css-abuse-for-hidden-text-salting

Too salty to handle: Exposing cases of CSS abuse for hidden text salting
A simple yet effective tactic, known as hidden text salting, is increasingly used by cybercriminals over the past few months to evade even the most advanced email security solutions, including those powered by machine learning and large language models.

TIT-Score: Evaluating Long-Prompt Based Text-to-Image Alignment via Text-to-Image-to-Text Consistency
Juntong Wang, Huiyu Duan, Jiarui Wang, Ziheng Jia, Guangtao Zhai, Xiongkuo Min
https://arxiv.org/abs/2510.02987

TIT-Score: Evaluating Long-Prompt Based Text-to-Image Alignment via Text-to-Image-to-Text Consistency
With the rapid advancement of large multimodal models (LMMs), recent text-to-image (T2I) models can generate high-quality images and demonstrate great alignment to short prompts. However, they still struggle to effectively understand and follow long and detailed prompts, displaying inconsistent generation. To address this challenge, we introduce LPG-Bench, a comprehensive benchmark for evaluating long-prompt-based text-to-image generation. LPG-Bench features 200 meticulously crafted prompts wit…

EditLens: Quantifying the Extent of AI Editing in Text
Katherine Thai, Bradley Emi, Elyas Masrour, Mohit Iyyer
https://arxiv.org/abs/2510.03154 https://arx…

EditLens: Quantifying the Extent of AI Editing in Text
A significant proportion of queries to large language models ask them to edit user-provided text, rather than generate new text from scratch. While previous work focuses on detecting fully AI-generated text, we demonstrate that AI-edited text is distinguishable from human-written and AI-generated text. First, we propose using lightweight similarity metrics to quantify the magnitude of AI editing present in a text given the original human-written text and validate these metrics with human annota…

Inception, which is building diffusion-based AI models for code and text, raised a $50M seed led by Menlo and releases an update to its Mercury coding model (Russell Brandom/TechCrunch)
https://techcrunch.com/2025/11/06/inception-rai…

Inception raises $50 million to build diffusion models for code and text | TechCrunch
Diffusion models already power AI image generators, but Inception thinks they can be even more powerful applied in software development.

heise | GitHub Spark im Test: Apps programmieren, ohne eine Zeile Code zu schreiben
Prompt eingeben, Enter drücken und fertig ist die App – ohne eine Zeile Code zu tippen. Wir haben getestet, was GitHubs KI-Programmierer Spark wirklich kann.

Taming Text-to-Sounding Video Generation via Advanced Modality Condition and Interaction
Kaisi Guan, Xihua Wang, Zhengfeng Lai, Xin Cheng, Peng Zhang, XiaoJiang Liu, Ruihua Song, Meng Cao
https://arxiv.org/abs/2510.03117

Taming Text-to-Sounding Video Generation via Advanced Modality Condition and Interaction
This study focuses on a challenging yet promising task, Text-to-Sounding-Video (T2SV) generation, which aims to generate a video with synchronized audio from text conditions, meanwhile ensuring both modalities are aligned with text. Despite progress in joint audio-video training, two critical challenges still remain unaddressed: (1) a single, shared text caption where the text for video is equal to the text for audio often creates modal interference, confusing the pretrained backbones, and (2) …

Listening or Reading? Evaluating Speech Awareness in Chain-of-Thought Speech-to-Text Translation
Jacobo Romero-D\'iaz, Gerard I. G\'allego, Oriol Pareras, Federico Costa, Javier Hernando, Cristina Espa\~na-Bonet
https://arxiv.org/abs/2510.03115

Listening or Reading? Evaluating Speech Awareness in Chain-of-Thought Speech-to-Text Translation
Speech-to-Text Translation (S2TT) systems built from Automatic Speech Recognition (ASR) and Text-to-Text Translation (T2TT) modules face two major limitations: error propagation and the inability to exploit prosodic or other acoustic cues. Chain-of-Thought (CoT) prompting has recently been introduced, with the expectation that jointly accessing speech and transcription will overcome these issues. Analyzing CoT through attribution methods, robustness evaluations with corrupted transcripts, and p…

OTR: Synthesizing Overlay Text Dataset for Text Removal
Jan Zdenek, Wataru Shimoda, Kota Yamaguchi
https://arxiv.org/abs/2510.02787 https://arxiv.org/pdf/2…

OTR: Synthesizing Overlay Text Dataset for Text Removal
Text removal is a crucial task in computer vision with applications such as privacy preservation, image editing, and media reuse. While existing research has primarily focused on scene text removal in natural images, limitations in current datasets hinder out-of-domain generalization or accurate evaluation. In particular, widely used benchmarks such as SCUT-EnsText suffer from ground truth artifacts due to manual editing, overly simplistic text backgrounds, and evaluation metrics that do not ca…