Tootfinder

Teilchen hinter Dunkler Materie? Mit einem Trick wohl Spur von Axionen gefunden
Axionen sind bisher nur hypothetische Elementarteilchen, die die Dunkle Materie bilden könnten. Mit einem Trick wurde nun womöglich erstmals eine Spur gefunden.

Micah Parsons contract: Troy Aikman would be 'shocked' if Cowboys don't re-sign All-Pro linebacker

https://www.cbssports.com/nfl/news/micah-p

Transplant Then Regenerate: A New Paradigm for Text Data Augmentation
Guangzhan Wang, Hongyu Zhang, Beijun Shen, Xiaodong Gu
https://arxiv.org/abs/2508.14723 https://

Transplant Then Regenerate: A New Paradigm for Text Data Augmentation
Data augmentation is a critical technique in deep learning. Traditional methods like Back-translation typically focus on lexical-level rephrasing, which primarily produces variations with the same semantics. While large language models (LLMs) have enhanced text augmentation by their "knowledge emergence" capability, controlling the style and structure of these outputs remains challenging and requires meticulous prompt engineering. In this paper, we propose LMTransplant, a novel text augmentatio…

Controllable Latent Space Augmentation for Digital Pathology
Sofi\`ene Boutaj, Marin Scalbert, Pierre Marza, Florent Couzinie-Devy, Maria Vakalopoulou, Stergios Christodoulidis
https://arxiv.org/abs/2508.14588

Controllable Latent Space Augmentation for Digital Pathology
Whole slide image (WSI) analysis in digital pathology presents unique challenges due to the gigapixel resolution of WSIs and the scarcity of dense supervision signals. While Multiple Instance Learning (MIL) is a natural fit for slide-level tasks, training robust models requires large and diverse datasets. Even though image augmentation techniques could be utilized to increase data variability and reduce overfitting, implementing them effectively is not a trivial task. Traditional patch-level au…

DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning
Qingyun Sun, Jiaqi Yuan, Shan He, Xiao Guan, Haonan Yuan, Xingcheng Fu, Jianxin Li, Philip S. Yu
https://arxiv.org/abs/2507.13396

DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning
Graph Retrieval-Augmented Generation has emerged as a powerful paradigm for grounding large language models with external structured knowledge. However, existing Graph RAG methods struggle with temporal reasoning, due to their inability to model the evolving structure and order of real-world events. In this work, we introduce DyG-RAG, a novel event-centric dynamic graph retrieval-augmented generation framework designed to capture and reason over temporal knowledge embedded in unstructured text.…

Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs
Skatje Myers, Dmitriy Dligach, Timothy A. Miller, Samantha Barr, Yanjun Gao, Matthew Churpek, Anoop Mayampurath, Majid Afshar
https://arxiv.org/abs/2508.14817

Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs
Electronic health records (EHRs) are long, noisy, and often redundant, posing a major challenge for the clinicians who must navigate them. Large language models (LLMs) offer a promising solution for extracting and reasoning over this unstructured text, but the length of clinical notes often exceeds even state-of-the-art models' extended context windows. Retrieval-augmented generation (RAG) offers an alternative by retrieving task-relevant passages from across the entire EHR, potentially reducin…

Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
Lorenz Brehme, Benedikt Dornauer, Thomas Str\"ohle, Maximilian Ehrhart, Ruth Breu
https://arxiv.org/abs/2508.14066

Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
Retrieval-Augmented Generation (RAG) is a well-established and rapidly evolving field within AI that enhances the outputs of large language models by integrating relevant information retrieved from external knowledge sources. While industry adoption of RAG is now beginning, there is a significant lack of research on its practical application in industrial contexts. To address this gap, we conducted a semistructured interview study with 13 industry practitioners to explore the current state of R…

HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks
Zhe Chen, Yusheng Liao, Shuyang Jiang, Zhiyuan Zhu, Haolin Li, Yanfeng Wang, Yu Wang
https://arxiv.org/abs/2508.12778

HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks
Medical large vision-language Models (Med-LVLMs) have shown promise in clinical applications but suffer from factual inaccuracies and unreliable outputs, posing risks in real-world diagnostics. While retrieval-augmented generation has emerged as a potential solution, current medical multimodal RAG systems are unable to perform effective retrieval across heterogeneous sources. The irrelevance of retrieved reports affects the factuality of analysis, while insufficient knowledge affects the credib…

EEG-MedRAG: Enhancing EEG-based Clinical Decision-Making via Hierarchical Hypergraph Retrieval-Augmented Generation
Yi Wang, Haoran Luo, Lu Meng
https://arxiv.org/abs/2508.13735

EEG-MedRAG: Enhancing EEG-based Clinical Decision-Making via Hierarchical Hypergraph Retrieval-Augmented Generation
With the widespread application of electroencephalography (EEG) in neuroscience and clinical practice, efficiently retrieving and semantically interpreting large-scale, multi-source, heterogeneous EEG data has become a pressing challenge. We propose EEG-MedRAG, a three-layer hypergraph-based retrieval-augmented generation framework that unifies EEG domain knowledge, individual patient cases, and a large-scale repository into a traversable n-ary relational hypergraph, enabling joint semantic-tem…

AutoBnB-RAG: Enhancing Multi-Agent Incident Response with Retrieval-Augmented Generation
Zefang Liu, Arman Anwar
https://arxiv.org/abs/2508.13118 https://a…

AutoBnB-RAG: Enhancing Multi-Agent Incident Response with Retrieval-Augmented Generation
Incident response (IR) requires fast, coordinated, and well-informed decision-making to contain and mitigate cyber threats. While large language models (LLMs) have shown promise as autonomous agents in simulated IR settings, their reasoning is often limited by a lack of access to external knowledge. In this work, we present AutoBnB-RAG, an extension of the AutoBnB framework that incorporates retrieval-augmented generation (RAG) into multi-agent incident response simulations. Built on the Backdo…