Tootfinder

Automated Seam Folding and Sewing Machine on Pleated Pants for Apparel Manufacturing
Ray Wai Man Kong
https://arxiv.org/abs/2508.06518 https://arxiv.org/pd…

Automated Seam Folding and Sewing Machine on Pleated Pants for Apparel Manufacturing
The applied research is the design and development of an automated folding and sewing machine for pleated pants. It represents a significant advancement in addressing the challenges associated with manual sewing processes. Traditional methods for creating pleats are labour-intensive, prone to inconsistencies, and require high levels of skill, making automation a critical need in the apparel industry. This research explores the technical feasibility and operational benefits of integrating advanc…

Harmonic balance-automatic differentiation method: an out-of-the-box and efficient solver for general nonlinear dynamics simulation
Yi Chen, Yuhong Jin, Rongzhou Lin, Yifan Jiang, Xutao Mei, Lei Houb, Yilong Wang, Ng Teng Yong, Anxin Guo
https://arxiv.org/abs/2508.07309

Harmonic balance-automatic differentiation method: an out-of-the-box and efficient solver for general nonlinear dynamics simulation
The Harmonic Balance-Alternating Frequency-Time domain (HB-AFT) method is extensively employed for dynamic response analysis of nonlinear systems. However, its application to high-dimensional complex systems is constrained by the manual derivation of Jacobian matrices during Newton-Raphson iterations, which become computationally intractable or error-prone for intricate nonlinearities. The Harmonic Balance-Automatic Differentiation (HB-AD) method is proposed to address this limitation, in which…

Integrating Rules and Semantics for LLM-Based C-to-Rust Translation
Feng Luo, Kexing Ji, Cuiyun Gao, Shuzheng Gao, Jia Feng, Kui Liu, Xin Xia, Michael R. Lyu
https://arxiv.org/abs/2508.06926

Integrating Rules and Semantics for LLM-Based C-to-Rust Translation
Automated translation of legacy C code into Rust aims to ensure memory safety while reducing the burden of manual migration. Early approaches in code translation rely on static rule-based methods, but they suffer from limited coverage due to dependence on predefined rule patterns. Recent works regard the task as a sequence-to-sequence problem by leveraging large language models (LLMs). Although these LLM-based methods are capable of reducing unsafe code blocks, the translated code often exhibit…

ARISE: Automating RISC-V Instruction Set Extension
Andreas Hager-Clukas, Philipp van Kempen, Stefan Wallentowitz
https://arxiv.org/abs/2508.07725 https://a…

ARISE: Automating RISC-V Instruction Set Extension
RISC-V is an extendable Instruction Set Architecture, growing in popularity for embedded systems. However, optimizing it to specific requirements, imposes a great deal of manual effort. To bridge the gap between software and ISA, the tool ARISE is presented. It automates the generation of RISC-V instructions based on assembly patterns, which are selected by an extendable set of metrics. These metrics implement the optimization goals of code size and instruction count reduction, both statically …

LLM Analysis of 150 years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade
Aida Kostikova, Ole P\"utz, Steffen Eger, Olga Sabelfeld, Benjamin Paassen
https://arxiv.org/abs/2509.07274

LLM Analysis of 150+ years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade
Migration has been a core topic in German political debate, from millions of expellees post World War II over labor migration to refugee movements in the recent past. Studying political speech regarding such wide-ranging phenomena in depth traditionally required extensive manual annotations, limiting the scope of analysis to small subsets of the data. Large language models (LLMs) have the potential to partially automate even complex annotation tasks. We provide an extensive evaluation of a mult…

Implicit Shape-Prior for Few-Shot Assisted 3D Segmentation
Mathilde Monvoisin, Louise Piecuch, Blanche Texier, C\'edric H\'emon, Ana\"is Barateau, J\'er\'emie Huet, Antoine Nordez, Anne-Sophie Boureau, Jean-Claude Nunes, Diana Mateus
https://arxiv.org/abs/2509.08580

Implicit Shape-Prior for Few-Shot Assisted 3D Segmentation
The objective of this paper is to significantly reduce the manual workload required from medical professionals in complex 3D segmentation tasks that cannot be yet fully automated. For instance, in radiotherapy planning, organs at risk must be accurately identified in computed tomography (CT) or magnetic resonance imaging (MRI) scans to ensure they are spared from harmful radiation. Similarly, diagnosing age-related degenerative diseases such as sarcopenia, which involve progressive muscle volum…

AEGIS: An Agent for Extraction and Geographic Identification in Scholarly Proceedings
Om Vishesh, Harshad Khadilkar, Deepak Akkil
https://arxiv.org/abs/2509.09470 https://

AEGIS: An Agent for Extraction and Geographic Identification in Scholarly Proceedings
Keeping pace with the rapid growth of academia literature presents a significant challenge for researchers, funding bodies, and academic societies. To address the time-consuming manual effort required for scholarly discovery, we present a novel, fully automated system that transitions from data discovery to direct action. Our pipeline demonstrates how a specialized AI agent, 'Agent-E', can be tasked with identifying papers from specific geographic regions within conference proceedings and then …

Improved Riemannian potato field: an Automatic Artifact Rejection Method for EEG
Davoud Hajhassani (GIPSA-VIBS), Quentin Barth\'elemy (CRNL, CRNL-COPHY), J\'er\'emie Mattout (CRNL, CRNL-COPHY), Marco Congedo (GIPSA-VIBS)
https://arxiv.org/abs/2509.09264

Improved Riemannian potato field: an Automatic Artifact Rejection Method for EEG
Electroencephalography (EEG) signal cleaning has long been a critical challenge in the research community. The presence of artifacts can significantly degrade EEG data quality, complicating analysis and potentially leading to erroneous interpretations. While various artifact rejection methods have been proposed, the gold standard remains manual visual inspection by human experts-a process that is time-consuming, subjective, and impractical for large-scale EEG studies. Existing techniques are of…

In a stinging dissent joined by her two liberal colleagues,
Justice Sonia Sotomayor wrote:
“Countless people in the Los Angeles area have been grabbed, thrown to the ground, and handcuffed
simply because of their looks, their accents, and the fact they make a living by doing manual labor.
Today, the Court needlessly subjects countless more to these exact same indignities.”
Department of Homeland Security attorneys have said immigration officers target people bas…

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection
Biwei Yan, Yue Zhang, Minghui Xu, Runyu Pan, Jinku Li, Xiuzhen Cheng
https://arxiv.org/abs/2509.09291

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection
The application layer of Bluetooth Low Energy (BLE) is a growing source of security vulnerabilities, as developers often neglect to implement critical protections such as encryption, authentication, and freshness. While formal verification offers a principled way to check these properties, the manual effort of constructing formal models makes it impractical for large-scale analysis. This paper introduces a key insight: BLE application security analysis can be reframed as a semantic translation …

ArchiveGPT: A human-centered evaluation of using a vision language model for image cataloguing
Line Abele, Gerrit Anders, Tolgahan Ayd{\i}n, J\"urgen Buder, Helen Fischer, Dominik Kimmel, Markus Huff
https://arxiv.org/abs/2507.07551

ArchiveGPT: A human-centered evaluation of using a vision language model for image cataloguing
The accelerating growth of photographic collections has outpaced manual cataloguing, motivating the use of vision language models (VLMs) to automate metadata generation. This study examines whether Al-generated catalogue descriptions can approximate human-written quality and how generative Al might integrate into cataloguing workflows in archival and museum collections. A VLM (InternVL2) generated catalogue descriptions for photographic prints on labelled cardboard mounts with archaeological co…

It's just an ordinary #caturday, I think I'll spare my other socials which got a stick of photos for International Cat Day yesterday.
Kim is sitting in her usual spot, I took this with the 7Artisan 50mm/0.95 manual focus lens which I reach for whenever I get bored
#cat

EnvX: Agentize Everything with Agentic AI
Linyao Chen, Zimian Peng, Yingxuan Yang, Yikun Wang, Wenzheng Tom Tang, Hiroki H. Kobayashi, Weinan Zhang
https://arxiv.org/abs/2509.08088

EnvX: Agentize Everything with Agentic AI
The widespread availability of open-source repositories has led to a vast collection of reusable software components, yet their utilization remains manual, error-prone, and disconnected. Developers must navigate documentation, understand APIs, and write integration code, creating significant barriers to efficient software reuse. To address this, we present EnvX, a framework that leverages Agentic AI to agentize GitHub repositories, transforming them into intelligent, autonomous agents capable o…

VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification.
This paper presents VLAI, a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated…

VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification
This paper presents VLAI, a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service.

Treemble: A Graphical Tool to Generate Newick Strings from Phylogenetic Tree Images
John B. Allard, Sudhir Kumar
https://arxiv.org/abs/2508.07081 https://a…

Treemble: A Graphical Tool to Generate Newick Strings from Phylogenetic Tree Images
Phylogenetic trees are ubiquitous and central to biology, but most published trees are available only as visual diagrams and not in the machine-readable newick format. There are thus thousands of published trees in the scientific literature that are unavailable for follow-up analyses, comparisons, supertree construction, etc. Experts can easily read such diagrams, but the manual construction of a newick string is prohibitively laborious. Previous attempts to semi-automate the reading of tree im…

All parts should go together without forcing. You must remember that the parts you are reassembling were disassembled by you. Therefore, if you can’t get them together again, there must be a reason. By all means, do not use a hammer.
-- IBM maintenance manual, 1925

MESH -- Understanding Videos Like Human: Measuring Hallucinations in Large Video Models
Garry Yang, Zizhe Chen, Man Hon Wong, Haoyu Lei, Yongqiang Chen, Zhenguo Li, Kaiwen Zhou, James Cheng
https://arxiv.org/abs/2509.08538

MESH -- Understanding Videos Like Human: Measuring Hallucinations in Large Video Models
Large Video Models (LVMs) build on the semantic capabilities of Large Language Models (LLMs) and vision modules by integrating temporal information to better understand dynamic video content. Despite their progress, LVMs are prone to hallucinations-producing inaccurate or irrelevant descriptions. Current benchmarks for video hallucination depend heavily on manual categorization of video content, neglecting the perception-based processes through which humans naturally interpret videos. We introd…

Identifying Clean and Contaminated Atomic-Sized Gold Contacts under Ambient Conditions Using a Clustering Algorithm
Guillem Pellicer, Carlos Sabater
https://arxiv.org/abs/2506.09608

Identifying Clean and Contaminated Atomic-Sized Gold Contacts under Ambient Conditions Using a Clustering Algorithm
Molecular electronics studies have advanced from early, simple single-molecule experiments at cryogenic temperatures to complex and multifunctional molecules under ambient conditions. However, room-temperature environments increase the risk of contamination, making it essential to identify and quantify clean and contaminated rupture traces (i.e., conductance versus relative electrode displacement) within large datasets. Given the high throughput of measurements, manual analysis becomes unfeasib…

STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation
Wenxiang Guo, Yu Zhang, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Zhetao Chen, Wenhao Xu, Fei Wu, Zhou Zhao
https://arxiv.org/abs/2507.06670

STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation
Recent breakthroughs in singing voice synthesis (SVS) have heightened the demand for high-quality annotated datasets, yet manual annotation remains prohibitively labor-intensive and resource-intensive. Existing automatic singing annotation (ASA) methods, however, primarily tackle isolated aspects of the annotation pipeline. To address this fundamental challenge, we present STARS, which is, to our knowledge, the first unified framework that simultaneously addresses singing transcription, alignme…

AgriVLN: Vision-and-Language Navigation for Agricultural Robots
Xiaobei Zhao, Xingqi Lyu, Xiang Li
https://arxiv.org/abs/2508.07406 https://arxiv.org/pdf/2…

AgriVLN: Vision-and-Language Navigation for Agricultural Robots
Agricultural robots have emerged as powerful members in agricultural tasks, nevertheless, still heavily rely on manual operation or untransportable railway for movement, resulting in limited mobility and poor adaptability. Vision-and-Language Navigation (VLN) enables robots to navigate to the target destinations following natural language instructions, demonstrating strong performance on several domains. However, none of the existing benchmarks or methods is specifically designed for agricultur…

Astra: A Multi-Agent System for GPU Kernel Performance Optimization
Anjiang Wei, Tianran Sun, Yogesh Seenichamy, Hang Song, Anne Ouyang, Azalia Mirhoseini, Ke Wang, Alex Aiken
https://arxiv.org/abs/2509.07506

Astra: A Multi-Agent System for GPU Kernel Performance Optimization
GPU kernel optimization has long been a central challenge at the intersection of high-performance computing and machine learning. Efficient kernels are crucial for accelerating large language model (LLM) training and serving, yet attaining high performance typically requires extensive manual tuning. Compiler-based systems reduce some of this burden, but still demand substantial manual design and engineering effort. Recently, researchers have explored using LLMs for GPU kernel generation, though…

Airway Segmentation Network for Enhanced Tubular Feature Extraction
Qibiao Wu, Yagang Wang, Qian Zhang
https://arxiv.org/abs/2507.06581 https://

Airway Segmentation Network for Enhanced Tubular Feature Extraction
Manual annotation of airway regions in computed tomography images is a time-consuming and expertise-dependent task. Automatic airway segmentation is therefore a prerequisite for enabling rapid bronchoscopic navigation and the clinical deployment of bronchoscopic robotic systems. Although convolutional neural network methods have gained considerable attention in airway segmentation, the unique tree-like structure of airways poses challenges for conventional and deformable convolutions, which oft…

Fine-Tuning Large Audio-Language Models with LoRA for Precise Temporal Localization of Prolonged Exposure Therapy Elements
Suhas BN, Andrew M. Sherrill, Jyoti Alaparthi, Dominik Mattioli, Rosa I. Arriaga, Chris W. Wiese, Saeed Abdullah
https://arxiv.org/abs/2506.09707

Fine-Tuning Large Audio-Language Models with LoRA for Precise Temporal Localization of Prolonged Exposure Therapy Elements
Prolonged Exposure (PE) therapy is an effective treatment for post-traumatic stress disorder (PTSD), but evaluating therapist fidelity remains labor-intensive due to the need for manual review of session recordings. We present a method for the automatic temporal localization of key PE fidelity elements -- identifying their start and stop times -- directly from session audio and transcripts. Our approach fine-tunes a large pre-trained audio-language model, Qwen2-Audio, using Low-Rank Adaptation …

from my link log —
The SLAX scripting language: an alternative syntax for XSLT.
http://juniper.github.io/libslax/slax-manual.html
saved 2024-11-25

Chain-of-Alpha: Unleashing the Power of Large Language Models for Alpha Mining in Quantitative Trading
Lang Cao, Zekun Xi, Long Liao, Ziwei Yang, Zheng Cao
https://arxiv.org/abs/2508.06312

Chain-of-Alpha: Unleashing the Power of Large Language Models for Alpha Mining in Quantitative Trading
Alpha factor mining is a fundamental task in quantitative trading, aimed at discovering interpretable signals that can predict asset returns beyond systematic market risk. While traditional methods rely on manual formula design or heuristic search with machine learning, recent advances have leveraged Large Language Models (LLMs) for automated factor discovery. However, existing LLM-based alpha mining approaches remain limited in terms of automation, generality, and efficiency. In this paper, we…

This factory tour making LED strips is kind of weird; mix of high automation and odd human manual stuff; I don't envy the guy having to fix up uncoated strips!
The QA stuff is kind of neat though; the big test probes lighting up big rolls and doing erm percussive testing.
https://www.youtube.com/watch?v=Kc…

Breaking Android with AI: A Deep Dive into LLM-Powered Exploitation
Wanni Vidulige Ishan Perera, Xing Liu, Fan liang, Junyi Zhang
https://arxiv.org/abs/2509.07933 https://

Breaking Android with AI: A Deep Dive into LLM-Powered Exploitation
The rapid evolution of Artificial Intelligence (AI) and Large Language Models (LLMs) has opened up new opportunities in the area of cybersecurity, especially in the exploitation automation landscape and penetration testing. This study explores Android penetration testing automation using LLM-based tools, especially PentestGPT, to identify and execute rooting techniques. Through a comparison of the traditional manual rooting process and exploitation methods produced using AI, this study evaluate…

Spectral Methods in Complex Systems
Francesco Caravelli
https://arxiv.org/abs/2509.05793 https://arxiv.org/pdf/2509.05793

Spectral Methods in Complex Systems
These notes offer a unified introduction to spectral methods for the study of complex systems. They are intended as an operative manual rather than a theorem-proof textbook: the emphasis is on tools, identities, and perspectives that can be readily applied across disciplines. Beginning with a compendium of matrix identities and inversion techniques, the text develops the connections between spectra, dynamics, and structure in finite-dimensional systems. Applications range from dynamical stabili…

M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models
Zexuan Li, Hongliang Dai, Piji Li
https://arxiv.org/abs/2509.07730 ht…

M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models
For Relation Extraction (RE), the manual annotation of training data may be prohibitively expensive, since the sentences that contain the target relations in texts can be very scarce and difficult to find. It is therefore beneficial to develop an efficient method that can automatically extract training instances from unlabeled texts for training RE models. Recently, large language models (LLMs) have been adopted in various natural language processing tasks, with RE also benefiting from their ad…

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 …

Series D, Episode 11 - Orbit
VILA: This had better work.
AVON: Have you cleared the governors?
VILA: I think so. Try it now.
AVON: Switching to manual. Maximum power on all drives.
[Biodome]
PINDER: Egrorian.
https://blake.torpidity.net/m/411/397 B7B3

Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest
Xiao Yang, Mehdi Ben Ayed, Longyu Zhao, Fan Zhou, Yuchen Shen, Abe Engle, Jinfeng Zhuang, Ling Leng, Jiajing Xu, Charles Rosenberg, Prathibha Deshikachar
https://arxiv.org/abs/2509.05292

Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest
The ranking utility function in an ad recommender system, which linearly combines predictions of various business goals, plays a central role in balancing values across the platform, advertisers, and users. Traditional manual tuning, while offering simplicity and interpretability, often yields suboptimal results due to its unprincipled tuning objectives, the vast amount of parameter combinations, and its lack of personalization and adaptability to seasonality. In this work, we propose a general…

Fixed a recently reported, asan-caught UaF in ngscopeclient earlier today when loading session files containing a protocol analyzer.
Apparently the analyzer never called AddRef() / Release() on the filter and could thus outlive it.
Yet another reason why the planned refactoring of filter ownership to use std::shared_ptr's rather than manual hand refcounting can't come soon enough. Just need to ship v0.1 first...

"The elephant will gradually disappear on its own"
WAT

SheetDesigner: MLLM-Powered Spreadsheet Layout Generation with Rule-Based and Vision-Based Reflection
Qin Chen, Yuanyi Ren, Xiaojun Ma, Mugeng Liu, Han Shi, Dongmei Zhang
https://arxiv.org/abs/2509.07473

SheetDesigner: MLLM-Powered Spreadsheet Layout Generation with Rule-Based and Vision-Based Reflection
Spreadsheets are critical to data-centric tasks, with rich, structured layouts that enable efficient information transmission. Given the time and expertise required for manual spreadsheet layout design, there is an urgent need for automated solutions. However, existing automated layout models are ill-suited to spreadsheets, as they often (1) treat components as axis-aligned rectangles with continuous coordinates, overlooking the inherently discrete, grid-based structure of spreadsheets; and (2)…

KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Guanzhi Deng, Yi Xie, Yu-Keung Ng, Mingyang Liu, Peijun Zheng, Jie Liu, Dapeng Wu, Yinqiao Li, Linqi Song
https://arxiv.org/abs/2509.07860

KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Effectively managing intellectual property is a significant challenge. Traditional methods for patent analysis depend on labor-intensive manual searches and rigid keyword matching. These approaches are often inefficient and struggle to reveal the complex relationships hidden within large patent datasets, hindering strategic decision-making. To overcome these limitations, we introduce KLIPA, a novel framework that leverages a knowledge graph and a large language model (LLM) to significantly adva…

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging
Zhihao Zhao, Yinzheng Zhao, Junjie Yang, Xiangtong Yao, Quanmin Liang, Shahrooz Faghihroohi, Kai Huang, Nassir Navab, M. Ali Nasseri
https://arxiv.org/abs/2509.08618

CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging
Recent advancements in foundation models, such as the Segment Anything Model (SAM), have significantly impacted medical image segmentation, especially in retinal imaging, where precise segmentation is vital for diagnosis. Despite this progress, current methods face critical challenges: 1) modality ambiguity in textual disease descriptions, 2) a continued reliance on manual prompting for SAM-based workflows, and 3) a lack of a unified framework, with most methods being modality- and task-specifi…

T-araVLN: Translator for Agricultural Robotic Agents on Vision-and-Language Navigation
Xiaobei Zhao, Xingqi Lyu, Xiang Li
https://arxiv.org/abs/2509.06644 https://

T-araVLN: Translator for Agricultural Robotic Agents on Vision-and-Language Navigation
Agricultural robotic agents have been becoming powerful helpers in a wide range of agricultural tasks, nevertheless, still heavily rely on manual operation or untransportable railway for movement. The AgriVLN method and the A2A benchmark pioneeringly extend Vision-and-Language Navigation (VLN) to the agricultural domain, enabling agents navigate to the target position following the natural language instructions. AgriVLN effectively understands the simple instructions, however, often misundersta…

Code2MCP: A Multi-Agent Framework for Automated Transformation of Code Repositories into Model Context Protocol Services
Chaoqian Ouyang, Ling Yue, Shimin Di, Libin Zheng, Shaowu Pan, Min-Ling Zhang
https://arxiv.org/abs/2509.05941

Code2MCP: A Multi-Agent Framework for Automated Transformation of Code Repositories into Model Context Protocol Services
The proliferation of Large Language Models (LLMs) has created a significant integration challenge in the AI agent ecosystem, often called the "$N \times M$ problem," where N models require custom integrations for M tools. This fragmentation stifles innovation and creates substantial development overhead. While the Model Context Protocol (MCP) has emerged as a standard to resolve this, its adoption is hindered by the manual effort required to convert the vast universe of existing software into M…

Machine Learning based Enterprise Financial Audit Framework and High Risk Identification
Tingyu Yuan, Xi Zhang, Xuanjing Chen
https://arxiv.org/abs/2507.06266 https://

Machine Learning based Enterprise Financial Audit Framework and High Risk Identification
In the face of global economic uncertainty, financial auditing has become essential for regulatory compliance and risk mitigation. Traditional manual auditing methods are increasingly limited by large data volumes, complex business structures, and evolving fraud tactics. This study proposes an AI-driven framework for enterprise financial audits and high-risk identification, leveraging machine learning to improve efficiency and accuracy. Using a dataset from the Big Four accounting firms (EY, Pw…

Data-Semantics-Aware Recommendation of Diverse Pivot Tables
Whanhee Cho, Anna Fariha
https://arxiv.org/abs/2507.06171 https://arxiv.o…

Data-Semantics-Aware Recommendation of Diverse Pivot Tables
Data summarization is essential to discover insights from large datasets. In a spreadsheets, pivot tables offer a convenient way to summarize tabular data by computing aggregates over some attributes, grouped by others. However, identifying attribute combinations that will result in useful pivot tables remains a challenge, especially for high-dimensional datasets. We formalize the problem of automatically recommending insightful and interpretable pivot tables, eliminating the tedious manual pro…

The Real Cost of Poor #Documentation for Developers
https://andiku.com/blog/the-real-cost-of-poor-documentation-for-developers

Salient, which uses AI to automate the post-loan origination process for lenders, raised a $60M Series A led by a16z, a source says at a $350M valuation (Lucinda Shen/Axios)
https://www.axios.com/pro/fintech-deals/2025/07/28/loan-servicing-ai-salient-6…

Exclusive: Loan servicing startup Salient raises $60M Series A
The process is still mostly manual but banks are investing tech in the area.

3. Sa11y is a really great tool: https://sa11y.netlify.app It’s a live, in-page accessibility audit whose workflow is far, far more useful for actually improving the page than the server-based “enter your URL” online services. Killer features:
- It live-highlights potential issues on the page.
- It lets you dismiss warnings and •remembers that you dismissed them•, so it can offer subjective concerns that require manual review (e.g. “this image is marked as decorative; is that correct?”).
- Per the previous post, it actually respects the contrast setting, because it reports on what’s on the screen.

Score-Informed BiLSTM Correction for Refining MIDI Velocity in Automatic Piano Transcription
Zhanhong He (David), Roberto Togneri (David), Defeng (David), Huang
https://arxiv.org/abs/2508.07757

Score-Informed BiLSTM Correction for Refining MIDI Velocity in Automatic Piano Transcription
MIDI is a modern standard for storing music, recording how musical notes are played. Many piano performances have corresponding MIDI scores available online. Some of these are created by the original performer, recording on an electric piano alongside the audio, while others are through manual transcription. In recent years, automatic music transcription (AMT) has rapidly advanced, enabling machines to transcribe MIDI from audio. However, these transcriptions often require further correction. A…

I still don’t understand how an institution can announce a new #CorporateDesign without having a CD manual ready, but still immediately start using the new logo. The manual has now been announced for September 22, 4 months later.
Apparently, somebody noticed that courses already start on September 15, so they scrambled to put together PowerPoint and Keynote templates, while at the…

I still don’t understand how an institution can announce a new #CorporateDesign without having a CD manual ready, but still immediately start using the new logo. The manual has now been announced for September 22, 4 months later.
Apparently, somebody noticed that courses already start on September 15, so they scrambled to put together PowerPoint and Keynote templates, while at the…

I still don’t understand how an institution can announce a new #CorporateDesign without having a CD manual ready, but still immediately start using the new logo. The manual has now been announced for September 22, 4 months later.
Apparently, somebody noticed that courses already start on September 15, so they scrambled to put together PowerPoint and Keynote templates, while at the…

Automated Workflow for the Detection of Vugs
M. Quamer Nasim, T. Maiti, N. Mosavat, P. V. Grech, T. Singh, P. Nath Singha Roy
https://arxiv.org/abs/2507.02988

Automated Workflow for the Detection of Vugs
Image logs are crucial in capturing high-quality geological information about subsurface formations. Among the various geological features that can be gleaned from Formation Micro Imager log, vugs are essential for reservoir evaluation. This paper introduces an automated Vug Detection Model, leveraging advanced computer vision techniques to streamline the vug identification process. Manual and semiautomated methods are limited by individual bias, labour-intensity and inflexibility in parameter …

It's maybe a bit odd, but I seriously love minor bike maintenance. With my trusty manual, a handful of basic tools, and a bit of oil, I can get the whole family's bikes purring in only a few minutes. A perfect sunny Saturday afternoon activity.

This week's ISE 2025 lecture was focussed on artificial neural networks. In particular, we were discussing how to get rid of manual feature engineering and doing representation learning from raw data with convolutional neural networks.
#AI #ArtificialNeuralNetworks

Charting the Future of Scholarly Knowledge with AI: A Community Perspective
Azanzi Jiomekong, Hande K\"u\c{c}\"uk McGinty, Keith G. Mills, Allard Oelen, Enayat Rajabi, Harry McElroy, Antrea Christou, Anmol Saini, Janice Anta Zebaze, Hannah Kim, Anna M. Jacyszyn, S\"oren Auer
https://arxiv.org/abs/2509.02581

Charting the Future of Scholarly Knowledge with AI: A Community Perspective
Despite the growing availability of tools designed to support scholarly knowledge extraction and organization, many researchers still rely on manual methods, sometimes due to unfamiliarity with existing technologies or limited access to domain-adapted solutions. Meanwhile, the rapid increase in scholarly publications across disciplines has made it increasingly difficult to stay current, further underscoring the need for scalable, AI-enabled approaches to structuring and synthesizing scholarly k…

RCA Copilot: Transforming Network Data into Actionable Insights via Large Language Models
Alexander Shan, Jasleen Kaur, Rahul Singh, Tarun Banka, Raj Yavatkar, T. Sridhar
https://arxiv.org/abs/2507.03224

RCA Copilot: Transforming Network Data into Actionable Insights via Large Language Models
Ensuring the reliability and availability of complex networked services demands effective root cause analysis (RCA) across cloud environments, data centers, and on-premises networks. Traditional RCA methods, which involve manual inspection of data sources such as logs and telemetry data, are often time-consuming and challenging for on-call engineers. While statistical inference methods have been employed to estimate the causality of network events, these approaches alone are similarly challengi…

The exit manual: How to leave the free energy cult and still get tenure https://www.researchgate.net/publication/391807467_The_Exit_Manual_How_to_Leave_the_Free_Energy_Cult_and_Still_Get_Tenure "When a theory…

User Manual for Model-based Imaging Inverse Problem
Xiaodong Wang
https://arxiv.org/abs/2509.01572 https://arxiv.org/pdf/2509.01572

User Manual for Model-based Imaging Inverse Problem
This user manual is intended to provide a detailed description on model-based optimization for imaging inverse problem. Theseproblems can be particularly complex and challenging, especially for individuals without prior exposure to convex optimization orinverse problem theory, like myself. In light of this, I am writing this manual to clarify and systematically organize the mathematicalrationale underlying imaging inverse problems. This manual might not be accurate in mathmatical notion but mor…

Design of an innovative robotic surgical instrument for circular stapling
Paul Tucan (CESTER), Nadim Al Hajjar (UMF), Calin Vaida (CESTER), Alexandru Pusca (CESTER), Tiberiu Antal (CESTER), Corina Radu (UMF), Daniel Jucan (CESTER), Adrian Pisla (CESTER), Damien Chablat (LS2N, LS2N - \'equipe RoMas), Doina Pisla (CESTER)
https://

Design of an innovative robotic surgical instrument for circular stapling
Esophageal cancer remains a highly aggressive malignancy with low survival rates, requiring advanced surgical interventions like esophagectomy. Traditional manual techniques, including circular staplers, face challenges such as limited precision, prolonged recovery times, and complications like leaks and tissue misalignment. This paper presents a novel robotic circular stapler designed to enhance the dexterity in confined spaces, improve tissue alignment, and reduce post-operative risks. Integr…

A matrix criterion and algorithmic approach for the Peterson hit problem: Part I
Dang Vo Phuc
https://arxiv.org/abs/2506.18392 https://

A matrix criterion and algorithmic approach for the Peterson hit problem: Part I
The Peterson hit problem in algebraic topology is to explicitly determine the dimension of the quotient space $Q\mathcal P_k = \mathbb F_2\otimes_{\mathcal A}\mathcal P_k$ in positive degrees, where $\mathcal{P}_k$ denotes the polynomial algebra in $k$ variables over the field $\mathbb{F}_2$, considered as an unstable module over the Steenrod algebra $\mathcal{A}$. Current approaches to this problem still rely heavily on manual computations, which are highly prone to errors due to the intricate…

CLARA: A Developer's Companion for Code Comprehension and Analysis
Ahmed Adnan, Mushfiqur Rahman, Saad Sakib Noor, Kazi Sakib
https://arxiv.org/abs/2509.09072 https://

CLARA: A Developer's Companion for Code Comprehension and Analysis
Code comprehension and analysis of open-source project codebases is a task frequently performed by developers and researchers. However, existing tools that practitioners use for assistance with such tasks often require prior project setup, lack context-awareness, and involve significant manual effort. To address this, we present CLARA, a browser extension that utilizes a state-of-the-art inference model to assist developers and researchers in: (i) comprehending code files and code fragments, (i…

Adding complex numbers to expression template algorithmic differentiation tools
Max Sagebaum, Nicolas R. Gauger
https://arxiv.org/abs/2508.05371 https://ar…

Adding complex numbers to expression template algorithmic differentiation tools
Operator overloading algorithmic differentiation (AD) tools are usually only developed for floating-point values. Algorithmic optimization for, e.g., linear systems solvers or matrix-matrix multiplications are often introduced via external functions or manual function specializations. Complex numbers can be viewed as aggregates of two floating-point values on which specialized operations are applied. Typically, these operations can be handled by the regular floating-point operations from the AD…

The Spatiotemporal Organization of Motor Cortex Activity Supporting Manual Dexterity
Nicholas Chehade
https://arxiv.org/abs/2506.21738 https://

The Spatiotemporal Organization of Motor Cortex Activity Supporting Manual Dexterity
Motor cortex (M1) is a crucial brain area for controlling voluntary movements, such as reaching and grasping for a cup of coffee. M1 is organized in a somatotopic manner, such that M1 output driving movement to different parts of the body is organized along the cortical surface. In primates, the arm and hand are represented in M1 as separate but overlapping territories. Unit activity recorded from the M1 forelimb representation comodulates with parameters related to reaching and/or grasping. Th…

Creative Intelligence is the only book that will give you the foundation, the ability for you to create your own floor. While others scramble to hit AI usage metrics and come up with bland ideas, you'll be building the skills and capabilities that actually matter and set you apart.
https://

AuraGenome: An LLM-Powered Framework for On-the-Fly Reusable and Scalable Circular Genome Visualizations
Chi Zhang, Yu Dong, Yang Wang, Yuetong Han, Guihua Shan, Bixia Tang
https://arxiv.org/abs/2507.02877

AuraGenome: An LLM-Powered Framework for On-the-Fly Reusable and Scalable Circular Genome Visualizations
Circular genome visualizations are essential for exploring structural variants and gene regulation. However, existing tools often require complex scripting and manual configuration, making the process time-consuming, error-prone, and difficult to learn. To address these challenges, we introduce AuraGenome, an LLM-powered framework for rapid, reusable, and scalable generation of multi-layered circular genome visualizations. AuraGenome combines a semantic-driven multi-agent workflow with an inter…

Pretty sure my mom gets that “flying-an-airplane-without-a-manual” feeling whenever she walks in and catches sight of my anarchist flag waving next to the rainbow, trans, and neurodiversity pride flags.
#Autism #ActuallyAutistic

Oh no it happened - client for a research project I’m working on got upset that we’re doing manual data analysis of survey responses, and complained about why we are so slow when their internal team working on a different report got “everything done in a couple of days with #AI tools”
And then they told us that waiting for proper human analysis is a “waste of time” and that we need to just chuck our dataset into AI and “get it over with”
I really don’t know what to do right now 🥲
Trying to do this properly on their expected timeline will mean very little sleep for multiple days, but giving up on the project quality and dumping it into AI is will make this entire project a waste of time. (As I wouldn’t be able to trust the output of the analysis, or be proud of it to showcase the final report as an example of our work, and not to mention that I don’t want to support this expectation to rush everything at work with these AI models)

Automated Radiographic Total Sharp Score (ARTSS) in Rheumatoid Arthritis: A Solution to Reduce Inter-Intra Reader Variation and Enhancing Clinical Practice
Hajar Moradmand, Lei Ren
https://arxiv.org/abs/2509.06854

Automated Radiographic Total Sharp Score (ARTSS) in Rheumatoid Arthritis: A Solution to Reduce Inter-Intra Reader Variation and Enhancing Clinical Practice
Assessing the severity of rheumatoid arthritis (RA) using the Total Sharp/Van Der Heijde Score (TSS) is crucial, but manual scoring is often time-consuming and subjective. This study introduces an Automated Radiographic Sharp Scoring (ARTSS) framework that leverages deep learning to analyze full-hand X-ray images, aiming to reduce inter- and intra-observer variability. The research uniquely accommodates patients with joint disappearance and variable-length image sequences. We developed ARTSS us…

Series B, Episode 02 - Shadow
GAN: The manual controls won't respond.
VILA: Do something, Gan.
GAN: Something is draining off the power. [Lights dim]
VILA: Orac, it has to be Orac.
https://blake.torpidity.net/m/202/565 B7B3

MagicGUI: A Foundational Mobile GUI Agent with Scalable Data Pipeline and Reinforcement Fine-tuning
Liujian Tang, Shaokang Dong, Yijia Huang, Minqi Xiang, Hongtao Ruan, Bin Wang, Shuo Li, Zhihui Cao, Hailiang Pang, Heng Kong, He Yang, Mingxu Chai, Zhilin Gao, Xingyu Liu, Yingnan Fu, Jiaming Liu, Tao Gui, Xuanjing Huang, Yu-Gang Jiang, Qi Zhang, Kang Wang, Yunke Zhang, Yuran Wang

MagicGUI: A Foundational Mobile GUI Agent with Scalable Data Pipeline and Reinforcement Fine-tuning
This paper presents MagicGUI, a foundational mobile GUI agent designed to address critical challenges in perception, grounding, and reasoning within real-world mobile GUI environments. The framework is underpinned by following six key components: (1) a comprehensive and accurate dataset, constructed via the scalable GUI Data Pipeline, which aggregates the largest and most diverse GUI-centric multimodal data to date from open-source repositories, automated crawling, and targeted manual annotatio…

LatticeWorld: A Multimodal Large Language Model-Empowered Framework for Interactive Complex World Generation
Yinglin Duan, Zhengxia Zou, Tongwei Gu, Wei Jia, Zhan Zhao, Luyi Xu, Xinzhu Liu, Hao Jiang, Kang Chen, Shuang Qiu
https://arxiv.org/abs/2509.05263

LatticeWorld: A Multimodal Large Language Model-Empowered Framework for Interactive Complex World Generation
Recent research has been increasingly focusing on developing 3D world models that simulate complex real-world scenarios. World models have found broad applications across various domains, including embodied AI, autonomous driving, entertainment, etc. A more realistic simulation with accurate physics will effectively narrow the sim-to-real gap and allow us to gather rich information about the real world conveniently. While traditional manual modeling has enabled the creation of virtual 3D scenes…

Can Large Language Models Automate the Refinement of Cellular Network Specifications?
Jianshuo Dong, Tianyi Zhang, Feng Yan, Yuanjie Li, Hewu Li, Han Qiu
https://arxiv.org/abs/2507.04214

Can Large Language Models Automate the Refinement of Cellular Network Specifications?
Cellular networks serve billions of users globally, yet concerns about reliability and security persist due to weaknesses in 3GPP standards. However, traditional analysis methods, including manual inspection and automated tools, struggle with increasingly expanding cellular network specifications. This paper investigates the feasibility of Large Language Models (LLMs) for automated cellular network specification refinement. To advance it, we leverage 200,000+ approved 3GPP Change Requests (CRs)…

Combining TSL and LLM to Automate REST API Testing: A Comparative Study
Thiago Barradas, Aline Paes, V\^ania de Oliveira Neves
https://arxiv.org/abs/2509.05540 https://

Combining TSL and LLM to Automate REST API Testing: A Comparative Study
The effective execution of tests for REST APIs remains a considerable challenge for development teams, driven by the inherent complexity of distributed systems, the multitude of possible scenarios, and the limited time available for test design. Exhaustive testing of all input combinations is impractical, often resulting in undetected failures, high manual effort, and limited test coverage. To address these issues, we introduce RestTSLLM, an approach that uses Test Specification Language (TSL) …

Understanding and Mitigating Errors of LLM-Generated RTL Code
Jiazheng Zhang, Cheng Liu, Huawei Li
https://arxiv.org/abs/2508.05266 https://arxiv.org/pdf/2…

Understanding and Mitigating Errors of LLM-Generated RTL Code
Despite the promising potential of large language model (LLM) based register-transfer-level (RTL) code generation, the overall success rate remains unsatisfactory. Errors arise from various factors, with limited understanding of specific failure causes hindering improvement. To address this, we conduct a comprehensive error analysis and manual categorization. Our findings reveal that most errors stem not from LLM reasoning limitations, but from insufficient RTL programming knowledge, poor under…

A Low-Cost Open-Source BLE-Based Asian Hornet Tracking System
Gilles Callebaut, Jan Van Moer
https://arxiv.org/abs/2509.03979 https://arxiv.org/pdf/2509.03…

A Low-Cost Open-Source BLE-Based Asian Hornet Tracking System
The Asian hornet (Vespa velutina) poses a serious threat to ecosystems and beekeeping. Locating nests is essential, but usually involves time-consuming manual triangulation. We present a low-cost, open-source tracking system based on Bluetooth Low Energy (BLE). The system consists of a lightweight BLE tag and a software-defined radio (SDR) receiver implemented in GNU Radio. By bypassing the BLE stack, we embed a custom pseudo-noise (PN) sequence in the uncoded PHY for correlation-based detectio…

Exploring LLM Capabilities in Extracting DCAT-Compatible Metadata for Data Cataloging
Lennart Busch, Daniel Tebernum, Gissel Velarde
https://arxiv.org/abs/2507.05282

Exploring LLM Capabilities in Extracting DCAT-Compatible Metadata for Data Cataloging
Efficient data exploration is crucial as data becomes increasingly important for accelerating processes, improving forecasts and developing new business models. Data consumers often spend 25-98 % of their time searching for suitable data due to the exponential growth, heterogeneity and distribution of data. Data catalogs can support and accelerate data exploration by using metadata to answer user queries. However, as metadata creation and maintenance is often a manual process, it is time-consum…

Improve Retinal Artery/Vein Classification via Channel Couplin
Shuang Zeng, Chee Hong Lee, Kaiwen Li, Boxu Xie, Ourui Fu, Hangzhou He, Lei Zhu, Yanye Lu, Fangxiao Cheng
https://arxiv.org/abs/2508.03738

Improve Retinal Artery/Vein Classification via Channel Couplin
Retinal vessel segmentation plays a vital role in analyzing fundus images for the diagnosis of systemic and ocular diseases. Building on this, classifying segmented vessels into arteries and veins (A/V) further enables the extraction of clinically relevant features such as vessel width, diameter and tortuosity, which are essential for detecting conditions like diabetic and hypertensive retinopathy. However, manual segmentation and classification are time-consuming, costly and inconsistent. With…

Leveraging Digital Twin-as-a-Service Towards Continuous and Automated Cybersecurity Certification
Ioannis Koufos, Abdul Rehman Qureshi, Adrian Asensio, Allen Abishek, Efstathios Zaragkas, Ricard Vilalta, Maria Souvalioti, George Xilouris, Michael-Alexandros Kourtis
https://arxiv.org/abs/2509.07649 …

Leveraging Digital Twin-as-a-Service Towards Continuous and Automated Cybersecurity Certification
Traditional risk assessments rely on manual audits and system scans, often causing operational disruptions and leaving security gaps. To address these challenges, this work presents Security Digital Twin-as-a-Service (SDT-aaS), a novel approach that leverages Digital Twin (DT) technology for automated, non-intrusive security compliance. SDT-aaS enables real-time security assessments by mirroring real-world assets, collecting compliance artifacts, and creating machine-readable evidence. The prop…

#OpenTelemetry Autoinstrumentation in #Java
https://www.dash0.com/guides/opentelemetry

Through the Expert's Eyes: Exploring Asynchronous Expert Perspectives and Gaze Visualizations in XR
Clara Sayffaerth, Annika K\"ohler, Julian Rasch, Albrecht Schmidt, Florian M\"uller
https://arxiv.org/abs/2509.00944

Through the Expert's Eyes: Exploring Asynchronous Expert Perspectives and Gaze Visualizations in XR
Transferring knowledge across generations is fundamental to human civilization, yet the challenge of passing on complex practical skills persists. Methods without a physically present instructor, such as videos, often fail to explain complex manual tasks, where spatial and social factors are critical. Technologies such as eXtended Reality and Artificial Intelligence hold the potential to retain expert knowledge and facilitate the creation of tailored, contextualized, and asynchronous explanatio…

from my link log —
The Nickel configuration language.
https://nickel-lang.org/user-manual/
saved 2025-08-19 https://dotat.at/…

The ngscopeclient about dialog now has a (hopefully) full list of all 77 authors credited! Very manual generation process for now, but I'm going to try and automate the list generation for the future.
One less thing to do before we ship the v0.1 release.
We now have only six release blocking tickets and then we can ship it:
* Fix some shaders assuming unlimited X axis thread block size is possible
* Binary packaging for MacOS
* Window resize events not handled …

The Secrets Must Not Flow: Scaling Security Verification to Large Codebases (extended version)
Linard Arquint, Samarth Kishor, Jason R. Koenig, Joey Dodds, Daniel Kroening, Peter M\"uller
https://arxiv.org/abs/2507.00595

The Secrets Must Not Flow: Scaling Security Verification to Large Codebases (extended version)
Existing program verifiers can prove advanced properties about security protocol implementations, but are difficult to scale to large codebases because of the manual effort required. We develop a novel methodology called *Diodon* that addresses this challenge by splitting the codebase into the protocol implementation (the *Core*) and the remainder (the *Application*). This split allows us to apply powerful semi-automated verification techniques to the security-critical Core, while fully-automat…

Vanilla-Converter: A Tool for Converting Camunda 7 BPMN Models into Camunda 8 Models
Dragana Sunaric, Charlotte Verbruggen, Dominik Bork
https://arxiv.org/abs/2508.04352 https:/…

Vanilla-Converter: A Tool for Converting Camunda 7 BPMN Models into Camunda 8 Models
As organizations prepare for the end-of-life of Camunda 7, manual migration remains complex due to fundamental differences between the two platforms. We present Vanilla-Converter, a command-line tool that facilitates the migration of BPMN models from Camunda 7 to Camunda 8. Vanilla-Converter automates the transformation process, supports a wide range of BPMN elements, and produces a transformed model and a detailed transformation log indicating automatic changes and remaining manual conversion …

MultiStream-LLM: Bridging Modalities for Robust Sign Language Translation
Marshall Thomas, Edward Fish, Richard Bowden
https://arxiv.org/abs/2509.00030 https://

MultiStream-LLM: Bridging Modalities for Robust Sign Language Translation
Despite progress in gloss-free Sign Language Translation (SLT), monolithic end-to-end models consistently fail on two critical components of natural signing: the precise recognition of high-speed fingerspelling and the integration of asynchronous non-manual cues from the face. Recent progress in Automated Sign Language Translation with Large Language Models has side stepped this challenge, forcing a single network to learn these simultaneously resulting in poor performance when tasked with tran…

No time to train! Training-Free Reference-Based Instance Segmentation
Miguel Espinosa, Chenhongyi Yang, Linus Ericsson, Steven McDonagh, Elliot J. Crowley
https://arxiv.org/abs/2507.02798

No time to train! Training-Free Reference-Based Instance Segmentation
The performance of image segmentation models has historically been constrained by the high cost of collecting large-scale annotated data. The Segment Anything Model (SAM) alleviates this original problem through a promptable, semantics-agnostic, segmentation paradigm and yet still requires manual visual-prompts or complex domain-dependent prompt-generation rules to process a new image. Towards reducing this new burden, our work investigates the task of object segmentation when provided with, al…

EvolveSignal: A Large Language Model Powered Coding Agent for Discovering Traffic Signal Control Algorithms
Leizhen Wang, Peibo Duan, Hao Wang, Yue Wang, Jian Xu, Nan Zheng, Zhenliang Ma
https://arxiv.org/abs/2509.03335

EvolveSignal: A Large Language Model Powered Coding Agent for Discovering Traffic Signal Control Algorithms
In traffic engineering, the fixed-time traffic signal control remains widely used for its low cost, stability, and interpretability. However, its design depends on hand-crafted formulas (e.g., Webster) and manual re-timing by engineers to adapt to demand changes, which is labor-intensive and often yields suboptimal results under heterogeneous or congested conditions. This paper introduces the EvolveSignal, a large language models (LLMs) powered coding agent to automatically discover new traffic…

Efficient Detection of Intermittent Job Failures Using Few-Shot Learning
Henri A\"idasso, Francis Bordeleau, Ali Tizghadam
https://arxiv.org/abs/2507.04173

Efficient Detection of Intermittent Job Failures Using Few-Shot Learning
One of the main challenges developers face in the use of continuous integration (CI) and deployment pipelines is the occurrence of intermittent job failures, which result from unexpected non-deterministic issues (e.g., flaky tests or infrastructure problems) rather than regular code-related errors such as bugs. Prior studies developed machine-learning (ML) models trained on large datasets of job logs to classify job failures as either intermittent or regular. As an alternative to costly manual …

Identifying Origins of Place Names via Retrieval Augmented Generation
Alexis Horde Vo, Matt Duckham, Estrid He, Rafe Benli
https://arxiv.org/abs/2509.01030 https://

Identifying Origins of Place Names via Retrieval Augmented Generation
Who is the "Batman" behind "Batman Street" in Melbourne? Understanding the historical, cultural, and societal narratives behind place names can reveal the rich context that has shaped a community. Although place names serve as essential spatial references in gazetteers, they often lack information about place name origins. Enriching these place names in today's gazetteers is a time-consuming, manual process that requires extensive exploration of a vast archive of documents and text sources. Rec…

autoPET IV challenge: Incorporating organ supervision and human guidance for lesion segmentation in PET/CT
Junwei Huang, Yingqi Hao, Yitong Luo, Ziyu Wang, Mingxuan Liu, Yifei Chen, Yuanhan Wang, Lei Xiang, Qiyuan Tian
https://arxiv.org/abs/2509.02402

autoPET IV challenge: Incorporating organ supervision and human guidance for lesion segmentation in PET/CT
Lesion Segmentation in PET/CT scans is an essential part of modern oncological workflows. To address the challenges of time-intensive manual annotation and high inter-observer variability, the autoPET challenge series seeks to advance automated segmentation methods in complex multi-tracer and multi-center settings. Building on this foundation, autoPET IV introduces a human-in-the-loop scenario to efficiently utilize interactive human guidance in segmentation tasks. In this work, we incorporated…

BayesL: Towards a Logical Framework for Bayesian Networks
Stefano M. Nicoletti, Mari\"elle Stoelinga
https://arxiv.org/abs/2506.23773 https://

BayesL: Towards a Logical Framework for Bayesian Networks
We introduce BayesL, a novel logical framework for specifying, querying, and verifying the behaviour of Bayesian networks (BNs). BayesL (pronounced "Basil") is a structured language that allows for the creation of queries over BNs. It facilitates versatile reasoning concerning causal and evidence-based relationships, and permits comprehensive what-if scenario evaluations without the need for manual modifications to the model.

A Digital Twin for Robotic Post Mortem Tissue Sampling using Virtual Reality
Maximilian Neidhardt, Ludwig Bosse, Vidas Raudonis, Kristina Allgoewer, Axel Heinemann, Benjamin Ondruschka, Alexander Schlaefer
https://arxiv.org/abs/2509.02760

A Digital Twin for Robotic Post Mortem Tissue Sampling using Virtual Reality
Studying tissue samples obtained during autopsies is the gold standard when diagnosing the cause of death and for understanding disease pathophysiology. Recently, the interest in post mortem minimally invasive biopsies has grown which is a less destructive approach in comparison to an open autopsy and reduces the risk of infection. While manual biopsies under ultrasound guidance are more widely performed, robotic post mortem biopsies have been recently proposed. This approach can further reduce…

Automated Bug Frame Retrieval from Gameplay Videos Using Vision-Language Models
Wentao Lu, Alexander Senchenko, Abram Hindle, Cor-Paul Bezemer
https://arxiv.org/abs/2508.04895 h…

Automated Bug Frame Retrieval from Gameplay Videos Using Vision-Language Models
Modern game studios deliver new builds and patches at a rapid pace, generating thousands of bug reports, many of which embed gameplay videos. To verify and triage these bug reports, developers must watch the submitted videos. This manual review is labour-intensive, slow, and hard to scale. In this paper, we introduce an automated pipeline that reduces each video to a single frame that best matches the reported bug description, giving developers instant visual evidence that pinpoints the bug. …

Automating Thematic Review of Prevention of Future Deaths Reports: Replicating the ONS Child Suicide Study using Large Language Models
Sam Osian, Arpan Dutta, Sahil Bhandari, Iain E. Buchan, Dan W. Joyce
https://arxiv.org/abs/2507.20786

Automating Thematic Review of Prevention of Future Deaths Reports: Replicating the ONS Child Suicide Study using Large Language Models
Prevention of Future Deaths (PFD) reports, issued by coroners in England and Wales, flag systemic hazards that may lead to further loss of life. Analysis of these reports has previously been constrained by the manual effort required to identify and code relevant cases. In 2025, the Office for National Statistics (ONS) published a national thematic review of child-suicide PFD reports ($\leq$ 18 years), identifying 37 cases from January 2015 to November 2023 - a process based entirely on manual c…

VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification
C\'edric Bonhomme, Alexandre Dulaunoy
https://arxiv.org/abs/2507.03607 …

VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification
This paper presents VLAI, a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service.

Evaluating Multiple Instance Learning Strategies for Automated Sebocyte Droplet Counting
Maryam Adelipour, Gustavo Carneiro, Jeongkwon Kim
https://arxiv.org/abs/2509.04895 https…

Evaluating Multiple Instance Learning Strategies for Automated Sebocyte Droplet Counting
Sebocytes are lipid-secreting cells whose differentiation is marked by the accumulation of intracellular lipid droplets, making their quantification a key readout in sebocyte biology. Manual counting is labor-intensive and subjective, motivating automated solutions. Here, we introduce a simple attention-based multiple instance learning (MIL) framework for sebocyte image analysis. Nile Red-stained sebocyte images were annotated into 14 classes according to droplet counts, expanded via data augme…

Stable Tracking of Eye Gaze Direction During Ophthalmic Surgery
Tinghe Hong, Shenlin Cai, Boyang Li, Kai Huang
https://arxiv.org/abs/2507.00635 https://

Stable Tracking of Eye Gaze Direction During Ophthalmic Surgery
Ophthalmic surgical robots offer superior stability and precision by reducing the natural hand tremors of human surgeons, enabling delicate operations in confined surgical spaces. Despite the advancements in developing vision- and force-based control methods for surgical robots, preoperative navigation remains heavily reliant on manual operation, limiting the consistency and increasing the uncertainty. Existing eye gaze estimation techniques in the surgery, whether traditional or deep learning-…

Cryo-RL: automating prostate cancer cryoablation planning with reinforcement learning
Trixia Simangan, Ahmed Nadeem Abbasi, Yipeng Hu, Shaheer U. Saeed
https://arxiv.org/abs/2509.04886

Cryo-RL: automating prostate cancer cryoablation planning with reinforcement learning
Cryoablation is a minimally invasive localised treatment for prostate cancer that destroys malignant tissue during de-freezing, while sparing surrounding healthy structures. Its success depends on accurate preoperative planning of cryoprobe placements to fully cover the tumour and avoid critical anatomy. This planning is currently manual, expertise-dependent, and time-consuming, leading to variability in treatment quality and limited scalability. In this work, we introduce Cryo-RL, a reinforcem…

ShikkhaChain: A Blockchain-Powered Academic Credential Verification System for Bangladesh
Ahsan Farabi, Israt Khandaker, Nusrat Jahan, Ibrahim Khalil Shanto
https://arxiv.org/abs/2508.05334

ShikkhaChain: A Blockchain-Powered Academic Credential Verification System for Bangladesh
Academic credential fraud threatens educational integrity, especially in developing countries like Bangladesh, where verification methods are primarily manual and inefficient. To address this challenge, we present ShikkhaChain, a blockchain-powered certificate management platform designed to securely issue, verify, and revoke academic credentials in a decentralized and tamper-proof manner. Built on Ethereum smart contracts and utilizing IPFS for off-chain storage, the platform offers a transpar…

Prompt-to-Product: Generative Assembly via Bimanual Manipulation
Ruixuan Liu, Philip Huang, Ava Pun, Kangle Deng, Shobhit Aggarwal, Kevin Tang, Michelle Liu, Deva Ramanan, Jun-Yan Zhu, Jiaoyang Li, Changliu Liu
https://arxiv.org/abs/2508.21063

Prompt-to-Product: Generative Assembly via Bimanual Manipulation
Creating assembly products demands significant manual effort and expert knowledge in 1) designing the assembly and 2) constructing the product. This paper introduces Prompt-to-Product, an automated pipeline that generates real-world assembly products from natural language prompts. Specifically, we leverage LEGO bricks as the assembly platform and automate the process of creating brick assembly structures. Given the user design requirements, Prompt-to-Product generates physically buildable brick…

A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners
Dibyayan Patra, Pasindu Ranasinghe, Bikram Banerjee, Simit Raval
https://arxiv.org/abs/2506.20464

A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners
Rock bolts are crucial components of the subterranean support systems in underground mines that provide adequate structural reinforcement to the rock mass to prevent unforeseen hazards like rockfalls. This makes frequent assessments of such bolts critical for maintaining rock mass stability and minimising risks in underground mining operations. Where manual surveying of rock bolts is challenging due to the low light conditions in the underground mines and the time-intensive nature of the proces…

Meta-Fair: AI-Assisted Fairness Testing of Large Language Models
Miguel Romero-Arjona, Jos\'e A. Parejo, Juan C. Alonso, Ana B. S\'anchez, Aitor Arrieta, Sergio Segura
https://arxiv.org/abs/2507.02533

Meta-Fair: AI-Assisted Fairness Testing of Large Language Models
Fairness--the absence of unjustified bias--is a core principle in the development of Artificial Intelligence (AI) systems, yet it remains difficult to assess and enforce. Current approaches to fairness testing in large language models (LLMs) often rely on manual evaluation, fixed templates, deterministic heuristics, and curated datasets, making them resource-intensive and difficult to scale. This work aims to lay the groundwork for a novel, automated method for testing fairness in LLMs, reducin…

RX-INT: A Kernel Engine for Real-Time Detection and Analysis of In-Memory Threats
Arjun Juneja
https://arxiv.org/abs/2508.03879 https://arxiv.org/pdf/2508.…

RX-INT: A Kernel Engine for Real-Time Detection and Analysis of In-Memory Threats
Malware and cheat developers use fileless execution techniques to evade traditional, signature-based security products. These methods include various types of manual mapping, module stomping, and threadless injection which work entirely within the address space of a legitimate process, presenting a challenge for detection due to ambiguity between what is legitimate and what isn't. Existing tools often have weaknesses, such as a dependency on Portable Executable (PE) structures or a vulnerabilit…