Tootfinder

VR- und KI-Brillen sind nur Brücken: Der heimliche Star heißt Augmented Reality
Während VR lange Zeit lautstark um Aufmerksamkeit buhlte, schleicht sich AR in unser Leben, um zu bleiben – auf dem Rücken von Mixed Reality und Smart Glasses.

A Project Moohan benchmark gets spotted, and may have revealed the Android XR headset's key spec https://www.techradar.com/computing/virtual-reality-augmented-reality…

MADrive: Memory-Augmented Driving Scene Modeling
Polina Karpikova, Daniil Selikhanovych, Kirill Struminsky, Ruslan Musaev, Maria Golitsyna, Dmitry Baranchuk
https://arxiv.org/abs/2506.21520

MADrive: Memory-Augmented Driving Scene Modeling
Recent advances in scene reconstruction have pushed toward highly realistic modeling of autonomous driving (AD) environments using 3D Gaussian splatting. However, the resulting reconstructions remain closely tied to the original observations and struggle to support photorealistic synthesis of significantly altered or novel driving scenarios. This work introduces MADrive, a memory-augmented reconstruction framework designed to extend the capabilities of existing scene reconstruction methods by r…

TAPS: Tool-Augmented Personalisation via Structured Tagging
Ekaterina Taktasheva, Jeff Dalton
https://arxiv.org/abs/2506.20409 https://

TAPS: Tool-Augmented Personalisation via Structured Tagging
Recent advancements in tool-augmented large language models have enabled them to interact with external tools, enhancing their ability to perform complex user tasks. However, existing approaches overlook the role of personalisation in guiding tool use. This work investigates how user preferences can be effectively integrated into goal-oriented dialogue agents. Through extensive analysis, we identify key weaknesses in the ability of LLMs to personalise tool use. To this end, we introduce \name, …

Controlled Retrieval-augmented Context Evaluation for Long-form RAG
Jia-Huei Ju, Suzan Verberne, Maarten de Rijke, Andrew Yates
https://arxiv.org/abs/2506.20051

Controlled Retrieval-augmented Context Evaluation for Long-form RAG
Retrieval-augmented generation (RAG) enhances large language models by incorporating context retrieved from external knowledge sources. While the effectiveness of the retrieval module is typically evaluated with relevance-based ranking metrics, such metrics may be insufficient to reflect the retrieval's impact on the final RAG result, especially in long-form generation scenarios. We argue that providing a comprehensive retrieval-augmented context is important for long-form RAG tasks like report…

#FotoDerWoche mit einem Stück Berliner Mauer in künstlerischer Auseinandersetzung.
https://blog.till-westermayer.de/index.php/2025/07/27/photo-of-the-we…

Photo of the week: Berlin wall
Anfang Juli war ich in Berlin und hatte ein Hotelzimmer in der Nähe der Mauergedenkstätte. Da ist dieses Foto entstanden - eine von meheren künstlerischen Installationen, die sich mit der ehemaligen Berliner Mauer, den Mauertoten und der Teilung der Stadt auseinandersetzen.

User Understanding of Privacy Permissions in Mobile Augmented Reality: Perceptions and Misconceptions
Viktorija Paneva, Verena Winterhalter, Franziska Augustinowski, Florian Alt
https://arxiv.org/abs/2506.20207

User Understanding of Privacy Permissions in Mobile Augmented Reality: Perceptions and Misconceptions
Mobile Augmented Reality (AR) applications leverage various sensors to provide immersive user experiences. However, their reliance on diverse data sources introduces significant privacy challenges. This paper investigates user perceptions and understanding of privacy permissions in mobile AR apps through an analysis of existing applications and an online survey of 120 participants. Findings reveal common misconceptions, including confusion about how permissions relate to specific AR functionali…

Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation
Guanting Dong, Xiaoxi Li, Yuyao Zhang, Mengjie Deng
https://arxiv.org/abs/2506.21384

Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation
Real-world live retrieval-augmented generation (RAG) systems face significant challenges when processing user queries that are often noisy, ambiguous, and contain multiple intents. While RAG enhances large language models (LLMs) with external knowledge, current systems typically struggle with such complex inputs, as they are often trained or evaluated on cleaner data. This paper introduces Omni-RAG, a novel framework designed to improve the robustness and effectiveness of RAG systems in live, o…

RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation
Ali Tourani, Fatemeh Nazary, Yashar Deldjoo
https://arxiv.org/abs/2506.20817

RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation
This paper addresses the challenge of developing multimodal recommender systems for the movie domain, where limited metadata (e.g., title, genre) often hinders the generation of robust recommendations. We introduce a resource that combines LLM-generated plot descriptions with trailer-derived visual embeddings in a unified pipeline supporting both Retrieval-Augmented Generation (RAG) and collaborative filtering. Central to our approach is a data augmentation step that transforms sparse metadata …

SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
Dhruv Gupta, Gayathri Ganesh Lakshmy, Yiqing Xie
https://arxiv.org/abs/2506.20081

SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
Retrieval-Augmented Code Generation (RACG) is a critical technique for enhancing code generation by retrieving relevant information. In this work, we conduct an in-depth analysis of code retrieval by systematically masking specific features while preserving code functionality. Our discoveries include: (1) although trained on code, current retrievers heavily rely on surface-level textual features (e.g., docstrings, identifier names), and (2) they exhibit a strong bias towards well-documented cod…