Tootfinder

Koalas vs. Crows: An Evolutionary Theory of #Software
https://ajmoon.com/posts/koalas-vs-crows-an-evolutionary-theory-of-software

FHIR-RAG-MEDS: Integrating HL7 FHIR with Retrieval-Augmented Large Language Models for Enhanced Medical Decision Support
Yildiray Kabak, Gokce B. Laleci Erturkmen, Mert Gencturk, Tuncay Namli, A. Anil Sinaci, Ruben Alcantud Corcoles, Cristina Gomez Ballesteros, Pedro Abizanda, Asuman Dogac
https://arxiv.org/abs/2509.07706

FHIR-RAG-MEDS: Integrating HL7 FHIR with Retrieval-Augmented Large Language Models for Enhanced Medical Decision Support
In this study, we propose FHIR-RAG-MEDS system that aims to integrate Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) with a Retrieval-Augmented Generation (RAG)-based system to improve personalized medical decision support on evidence-based clinical guidelines, emphasizing the need for research in practical applications. In the evolving landscape of medical decision support systems, integrating advanced technologies such as RAG and HL7 FHIR can significantly enhance clinic…

From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translation
Mardiyyah Oduwole, Oluwatosin Olajide, Jamiu Suleiman, Faith Hunja, Busayo Awobade, Fatimo Adebanjo, Comfort Akanni, Chinonyelum Igwe, Peace Ododo, Promise Omoigui, Steven Kolawole, Abraham Owodunni
https://arxiv.org/abs/2509.07471

From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translation
The linguistic diversity across the African continent presents different challenges and opportunities for machine translation. This study explores the effects of data augmentation techniques in improving translation systems in low-resource African languages. We focus on two data augmentation techniques: sentence concatenation with back translation and switch-out, applying them across six African languages. Our experiments show significant improvements in machine translation performance, with a …

🇺🇦 #NowPlaying on KEXP's #VarietyMix
OutKast:
🎵 Aquemini
#OutKast
https://areyoulost.bandcamp.com/album/outkast-aquemini-screwed
https://open.spotify.com/track/4V3N5LvUOh3yDPP16cwAhE

"It is clear to us that the assessed approaches are not feasible, and that further research into these techniques would not be an effective use of limited time and resources. It is vital that these ideas do not distract from the priority to reduce greenhouse gas (GHG) emissions or from the critical need to conduct fundamental research in the polar regions."
New paper out today led by Martin Siegert and ft. (on here) me and @… and about 40 others where we argue that some of the proposed #Polar #Geoengineering schemes are dangerous, misleading and unethical...
#AskMeAnything if you'd like to know more.
https://www.frontiersin.org/journals/science/articles/10.3389/fsci.2025.1527393/full
.

Hyperspectral data augmentation with transformer-based diffusion models
Mattia Ferrari, Lorenzo Bruzzone
https://arxiv.org/abs/2510.08363 https://arxiv.org…

Hyperspectral data augmentation with transformer-based diffusion models
The introduction of new generation hyperspectral satellite sensors, combined with advancements in deep learning methodologies, has significantly enhanced the ability to discriminate detailed land-cover classes at medium-large scales. However, a significant challenge in deep learning methods is the risk of overfitting when training networks with small labeled datasets. In this work, we propose a data augmentation technique that leverages a guided diffusion model. To effectively train the model w…

Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG)
Junki Mori, Kazuya Kakizaki, Taiki Miyagawa, Jun Sakuma
https://arxiv.org/abs/2510.06719

Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding them in external knowledge. However, its application in sensitive domains is limited by privacy risks. Existing private RAG methods typically rely on query-time differential privacy (DP), which requires repeated noise injection and leads to accumulated privacy loss. To address this issue, we propose DP-SynRAG, a framework that uses LLMs to generate differentially private synthetic RAG databases. Unlike prior…

Guided Decoding and Its Critical Role in Retrieval-Augmented Generation
\"Ozg\"ur U\u{g}ur, Musa Y{\i}lmaz, Esra \c{S}avirdi, \"Ozay Ezerceli, Mahmut El Huseyni, Selva Ta\c{s}, Reyhan Bayraktar
https://arxiv.org/abs/2509.06631

Guided Decoding and Its Critical Role in Retrieval-Augmented Generation
The integration of Large Language Models (LLMs) into various applications has driven the need for structured and reliable responses. A key challenge in Retrieval-Augmented Generation (RAG) systems is ensuring that outputs align with expected formats while minimizing hallucinations. This study examines the role of guided decoding in RAG systems, comparing three methods, Outlines, XGrammar, and LM Format Enforcer, across different multi-turn prompting setups (0-turn, 1-turn, and 2-turn). By evalu…

DCMI: A Differential Calibration Membership Inference Attack Against Retrieval-Augmented Generation
Xinyu Gao, Xiangtao Meng, Yingkai Dong, Zheng Li, Shanqing Guo
https://arxiv.org/abs/2509.06026

DCMI: A Differential Calibration Membership Inference Attack Against Retrieval-Augmented Generation
While Retrieval-Augmented Generation (RAG) effectively reduces hallucinations by integrating external knowledge bases, it introduces vulnerabilities to membership inference attacks (MIAs), particularly in systems handling sensitive data. Existing MIAs targeting RAG's external databases often rely on model responses but ignore the interference of non-member-retrieved documents on RAG outputs, limiting their effectiveness. To address this, we propose DCMI, a differential calibration MIA that miti…

ImportSnare: Directed "Code Manual" Hijacking in Retrieval-Augmented Code Generation
Kai Ye, Liangcai Su, Chenxiong Qian
https://arxiv.org/abs/2509.07941 https://

ImportSnare: Directed "Code Manual" Hijacking in Retrieval-Augmented Code Generation
Code generation has emerged as a pivotal capability of Large Language Models(LLMs), revolutionizing development efficiency for programmers of all skill levels. However, the complexity of data structures and algorithmic logic often results in functional deficiencies and security vulnerabilities in generated code, reducing it to a prototype requiring extensive manual debugging. While Retrieval-Augmented Generation (RAG) can enhance correctness and security by leveraging external code manuals, it …