Tootfinder

Switzerland-based Mimic Robotics, which is building AI models to enable human-like robotic hands to adapt to complex, high-precision tasks, raised a $16M seed (Kyt Dotson/SiliconANGLE)
https://siliconangle.com/2025/11/04/mimic-raises-16m-build-a…

Mimic raises $16M to build AI models for human-like robotic hands - SiliconANGLE
Mimic raises $16M to build AI models for human-like robotic hands - SiliconANGLE

Exploring Diffusion Models for Generative Forecasting of Financial Charts
Taegyeong Lee, Jiwon Park, Kyunga Bang, Seunghyun Hwang, Ung-Jin Jang
https://arxiv.org/abs/2509.02308 …

Exploring Diffusion Models for Generative Forecasting of Financial Charts
Recent advances in generative models have enabled significant progress in tasks such as generating and editing images from text, as well as creating videos from text prompts, and these methods are being applied across various fields. However, in the financial domain, there may still be a reliance on time-series data and a continued focus on transformer models, rather than on diverse applications of generative models. In this paper, we propose a novel approach that leverages text-to-image model …

Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices
Evan King, Adam Sabra, Manjunath Kudlur, James Wang, Pete Warden
https://arxiv.org/abs/2509.02523 https://

Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices
We present the Flavors of Moonshine, a suite of tiny automatic speech recognition (ASR) models specialized for a range of underrepresented languages. Prevailing wisdom suggests that multilingual ASR models outperform monolingual counterparts by exploiting cross-lingual phonetic similarities. We challenge this assumption, showing that for sufficiently small models (27M parameters), training monolingual systems on a carefully balanced mix of high-quality human-labeled, pseudo-labeled, and synthet…

A profile of Anthropic's Frontier Red Team, which is unique among AI companies in having a mandate to both evaluate its AI models and publicize findings widely (Sharon Goldman/Fortune)
https://fortune.com/2025/09/04/anthrop

Inside the Anthropic ‘red team’ tasked with breaking its AI models—and burnishing the company’s reputation for safety
Anthropic's 'Frontier Red Team' works to ensure the company's AI models are safe—and that its policy solutions get heard in Washington and beyond

Meta Says Porn Stash was for 'Personal Use,' Not Training AI Models
https://gizmodo.com/meta-says-porn-stash-was-for-personal-use-not-training-ai-models-2000679672

DeepL stellt eigenen KI-Agenten für Unternehmen vor
Finanzen, Vertrieb, Kundenservice – all das sollen Unternehmen künftig mittels eines KI-Agenten von DeepL erledigen lassen.
https://www.…

Researchers find OpenAI's o1 can analyze languages like a human expert, including inferring the phonological rules of made-up languages without prior knowledge (Steve Nadis/Quanta Magazine)
https://www.quantamagazine.org/in-a-first-

In a First, AI Models Analyze Language As Well As a Human Expert | Quanta Magazine
If language is what makes us human, what does it mean now that large language models have gained “metalinguistic” abilities?

Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition
Mayur Shirke, Amey Shembade, Pavan Thorat, Madhushri Wagh, Raviraj Joshi
https://arxiv.org/abs/2509.02514

Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition
Named Entity Recognition (NER) in code-mixed text, particularly Hindi-English (Hinglish), presents unique challenges due to informal structure, transliteration, and frequent language switching. This study conducts a comparative evaluation of code-mixed fine-tuned models and non-code-mixed multilingual models, along with zero-shot generative large language models (LLMs). Specifically, we evaluate HingBERT, HingMBERT, and HingRoBERTa (trained on code-mixed data), and BERT Base Cased, IndicBERT, R…

Model Merging to Maintain Language-Only Performance in Developmentally Plausible Multimodal Models
Ece Takmaz, Lisa Bylinina, Jakub Dotlacil
https://arxiv.org/abs/2510.01845 htt…

Model Merging to Maintain Language-Only Performance in Developmentally Plausible Multimodal Models
State-of-the-art vision-and-language models consist of many parameters and learn from enormous datasets, surpassing the amounts of linguistic data that children are exposed to as they acquire a language. This paper presents our approach to the multimodal track of the BabyLM challenge addressing this discrepancy. We develop language-only and multimodal models in low-resource settings using developmentally plausible datasets, with our multimodal models outperforming previous BabyLM baselines. One…

GRAM-R$^2$: Self-Training Generative Foundation Reward Models for Reward Reasoning
Chenglong Wang, Yongyu Mu, Hang Zhou, Yifu Huo, Ziming Zhu, Jiali Zeng, Murun Yang, Bei Li, Tong Xiao, Xiaoyang Hao, Chunliang Zhang, Fandong Meng, Jingbo Zhu
https://arxiv.org/abs/2509.02492

GRAM-R$^2$: Self-Training Generative Foundation Reward Models for Reward Reasoning
Significant progress in reward modeling over recent years has been driven by a paradigm shift from task-specific designs towards generalist reward models. Despite this trend, developing effective reward models remains a fundamental challenge: the heavy reliance on large-scale labeled preference data. Pre-training on abundant unlabeled data offers a promising direction, but existing approaches fall short of instilling explicit reasoning into reward models. To bridge this gap, we propose a self-t…