Tootfinder

On Monday I started a new GitHub repo, to implement a distributed knowledge graph that knows about stuff in houses and how they are related. I used the Claude AI tools and Claude-flow agent framework to write all the code. Got a simplified thing running and cleaned up on Tuesday, built the full functionality on Wednesday and after a bit more testing and documentation wrapped up work before lunch on Thursday. It’s a lot of well tested and documented functionality.

A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation
Renjie Liang, Zhengkang Fan, Jinqian Pan, Chenkun Sun, Russell Terry, Jie Xu
https://arxiv.org/abs/2506.23584

A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation
Generating radiology reports from CT scans remains a complex task due to the nuanced nature of medical imaging and the variability in clinical documentation. In this study, we propose a two-stage framework for generating renal radiology reports from 2D CT slices. First, we extract structured abnormality features using a multi-task learning model trained to identify lesion attributes such as location, size, enhancement, and attenuation. These extracted features are subsequently combined with the…

Standard Applicability Judgment and Cross-jurisdictional Reasoning: A RAG-based Framework for Medical Device Compliance
Yu Han, Aaron Ceross, Jeroen H. M. Bergmann
https://arxiv.org/abs/2506.18511

Standard Applicability Judgment and Cross-jurisdictional Reasoning: A RAG-based Framework for Medical Device Compliance
Identifying the appropriate regulatory standard applicability remains a critical yet understudied challenge in medical device compliance, frequently necessitating expert interpretation of fragmented and heterogeneous documentation across different jurisdictions. To address this challenge, we introduce a modular AI system that leverages a retrieval-augmented generation (RAG) pipeline to automate standard applicability determination. Given a free-text device description, our system retrieves cand…

ReCode: Updating Code API Knowledge with Reinforcement Learning
Haoze Wu, Yunzhi Yao, Wenhao Yu, Huajun Chen, Ningyu Zhang
https://arxiv.org/abs/2506.20495

ReCode: Updating Code API Knowledge with Reinforcement Learning
Large Language Models (LLMs) exhibit remarkable code generation capabilities but falter when adapting to frequent updates in external library APIs. This critical limitation, stemming from reliance on outdated API knowledge from their training data, even with access to current documentation, impedes reliable code generation in dynamic environments. To tackle this issue, we propose ReCode (rule-based Reinforcement learning for Code Update), a novel framework that mimics human programmer adaptatio…

Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
Tetiana Gladkykh, Kyrylo Kirykov
https://arxiv.org/abs/2506.12234 https://

Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
Text-to-SQL systems enable users to query databases using natural language, democratizing access to data analytics. However, they face challenges in understanding ambiguous phrasing, domain-specific vocabulary, and complex schema relationships. This paper introduces Datrics Text2SQL, a Retrieval-Augmented Generation (RAG)-based framework designed to generate accurate SQL queries by leveraging structured documentation, example-based learning, and domain-specific rules. The system builds a rich K…

FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations
Shaun Shuster, Eyal Zaloof, Asaf Shabtai, Rami Puzis
https://arxiv.org/abs/2506.11635

FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations
The continuous growth of the e-commerce industry attracts fraudsters who exploit stolen credit card details. Companies often investigate suspicious transactions in order to retain customer trust and address gaps in their fraud detection systems. However, analysts are overwhelmed with an enormous number of alerts from credit card transaction monitoring systems. Each alert investigation requires from the fraud analysts careful attention, specialized knowledge, and precise documentation of the out…

Developing Shared Vocabulary System For Collaborative Software Engineering
Carey Lai Zheng Hui, Johnson Britto Jessia Esther Leena, Kumuthini Subramanian, Zhao Chenyu, Shubham Rajeshkumar Jariwala
https://arxiv.org/abs/2507.14396

Developing Shared Vocabulary System For Collaborative Software Engineering
Effective communication is a critical factor in successful software engineering collaboration. However, communication gaps remain a persistent challenge, often leading to misunderstandings, inefficiencies, and defects. This research investigates the technical factors contributing to such misunderstandings and explores the measurable benefits of establishing shared vocabulary systems within software documentation and codebases. Using a Design Science Research (DSR) framework, the study was struc…

Bookmark this for when you ask yourself why skepticism turns to hatred on the use of unconstrained LLMs.
https://infosec.exchange/@dvandal/114638324336575392

KnowIt: Deep Time Series Modeling and Interpretation
M. W. Theunissen, R. Rabe, M. H. Davel
https://arxiv.org/abs/2507.06009 https://…

KnowIt: Deep Time Series Modeling and Interpretation
KnowIt (Knowledge discovery in time series data) is a flexible framework for building deep time series models and interpreting them. It is implemented as a Python toolkit, with source code and documentation available from https://must-deep-learning.github.io/KnowIt. It imposes minimal assumptions about task specifications and decouples the definition of dataset, deep neural network architecture, and interpretability technique through well defined interfaces. This ensures the ease of importing n…

OASBuilder: Generating OpenAPI Specifications from Online API Documentation with Large Language Models
Koren Lazar, Matan Vetzler, Kiran Kate, Jason Tsay, David Boaz Himanshu Gupta, Avraham Shinnar, Rohith D Vallam, David Amid Esther Goldbraich, Guy Uziel, Jim Laredo, Ateret Anaby Tavor
https://arxiv.org/abs/2507.05316

OASBuilder: Generating OpenAPI Specifications from Online API Documentation with Large Language Models
AI agents and business automation tools interacting with external web services require standardized, machine-readable information about their APIs in the form of API specifications. However, the information about APIs available online is often presented as unstructured, free-form HTML documentation, requiring external users to spend significant time manually converting it into a structured format. To address this, we introduce OASBuilder, a novel framework that transforms long and diverse API d…

Model Cards Revisited: Bridging the Gap Between Theory and Practice for Ethical AI Requirements
Tim Puhlf\"ur{\ss}, Julia Butzke, Walid Maalej
https://arxiv.org/abs/2507.06014

Model Cards Revisited: Bridging the Gap Between Theory and Practice for Ethical AI Requirements
Model cards are the primary documentation framework for developers of artificial intelligence (AI) models to communicate critical information to their users. Those users are often developers themselves looking for relevant documentation to ensure that their AI systems comply with the ethical requirements of existing laws, guidelines, and standards. Recent studies indicate inadequate model documentation practices, suggesting a gap between AI requirements and current practices in model documentat…

Day 3 2/2
2. Enabling API documentation with Swagger.
I integrated Swagger into the backend (NestJS v10, ESM, Turbo monorepo), using `@nestjs/swagger@7` to match our current framework version.
The docs are now live at `/api/docs`, fully aligned with our global `ValidationPipe` and `AllExceptionsFilter` — ensuring consistent validation and error responses.
Next: annotate DTOs, describe endpoints properly, and restrict access.

This https://arxiv.org/abs/2411.10918 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection
Cyber-Physical Systems (CPS) are vulnerable to cyber-physical attacks that violate physical laws. While invariant-based anomaly detection is effective, existing methods are limited: data-driven approaches lack semantic context, and physics-based models require extensive manual work. We propose INVARLLM, a hybrid framework that uses large language models (LLMs) to extract semantic information from CPS documentation and generate physical invariants, then validates these against real system data u…