Tootfinder

The most effective way to destroy an organization is to make it more bureaucratic. In 1944, the Office of Strategic Services, the precursor to the CIA, was aware of this. What they didn't know was that their blueprint for sabotaging Nazi operations would become the operating manual for modern corporations.
https://www.alephic.com/sabotage

Simple Sabotage Field Manual
Simple Sabotage Field Manual - A book by the Alephic team.

Sola, which wants to use AI agents to automate businesses' repetitive, manual tasks, raised $21M, including a $3.5M seed and a $17.5M Series A led by a16z (Emma Hinchliffe/Fortune)
https://fortune.com/2025/08/14/sola-solutions-ai-startup-r…

Exclusive: Two MIT dropouts raised $21 million for their AI startup that does legacy businesses’ grunt work
Sola Solutions' technology, backed by Andreessen Horowitz and Sarah Guo, automates grunt work for businesses, replacing RPA.

SignClip: Leveraging Mouthing Cues for Sign Language Translation by Multimodal Contrastive Fusion
Wenfang Wu, Tingting Yuan, Yupeng Li, Daling Wang, Xiaoming Fu
https://arxiv.org/abs/2509.10266

SignClip: Leveraging Mouthing Cues for Sign Language Translation by Multimodal Contrastive Fusion
Sign language translation (SLT) aims to translate natural language from sign language videos, serving as a vital bridge for inclusive communication. While recent advances leverage powerful visual backbones and large language models, most approaches mainly focus on manual signals (hand gestures) and tend to overlook non-manual cues like mouthing. In fact, mouthing conveys essential linguistic information in sign languages and plays a crucial role in disambiguating visually similar signs. In this…

Using the Pepper Robot to Support Sign Language Communication
Giulia Botta, Marco Botta, Cristina Gena, Alessandro Mazzei, Massimo Donini, Alberto Lillo
https://arxiv.org/abs/2509.09889

Using the Pepper Robot to Support Sign Language Communication
Social robots are increasingly experimented in public and assistive settings, but their accessibility for Deaf users remains quite underexplored. Italian Sign Language (LIS) is a fully-fledged natural language that relies on complex manual and non-manual components. Enabling robots to communicate using LIS could foster more inclusive human robot interaction, especially in social environments such as hospitals, airports, or educational settings. This study investigates whether a commercial socia…

Bridging Modality Gaps in e-Commerce Products via Vision-Language Alignment
Yipeng Zhang, Hongju Yu, Aritra Mandal, Canran Xu, Qunzhi Zhou, Zhe Wu
https://arxiv.org/abs/2508.10116

Bridging Modality Gaps in e-Commerce Products via Vision-Language Alignment
Item information, such as titles and attributes, is essential for effective user engagement in e-commerce. However, manual or semi-manual entry of structured item specifics often produces inconsistent quality, errors, and slow turnaround, especially for Customer-to-Customer sellers. Generating accurate descriptions directly from item images offers a promising alternative. Existing retrieval-based solutions address some of these issues but often miss fine-grained visual details and struggle with…

Any Epson-HX20 people got a copy of the _eprom_ version of the Intext manual?
Mine has the Eprom in the expansion box, and it starts up with the error 'Memory too small. See Manual.' FRE(0) shows 29275 - so that I guess means at least BASIC sees the expansion RAM.
#retrocomputing

Series B, Episode 10 - Voice from the Past
VILA: Blake, I've just been checking on Del Ten. It's an ultra planet, fantastic mountain scenery, and the gravity's so low you can practically fly.
BLAKE: Switch to manual.
ZEN: Confirmed
https://blake.torpidity.net/m/210/0 B7…

WildElder: A Chinese Elderly Speech Dataset from the Wild with Fine-Grained Manual Annotations
Hui Wang, Jiaming Zhou, Jiabei He, Haoqin Sun, Yong Qin
https://arxiv.org/abs/2510.09344

WildElder: A Chinese Elderly Speech Dataset from the Wild with Fine-Grained Manual Annotations
Elderly speech poses unique challenges for automatic processing due to age-related changes such as slower articulation and vocal tremors. Existing Chinese datasets are mostly recorded in controlled environments, limiting their diversity and real-world applicability. To address this gap, we present WildElder, a Mandarin elderly speech corpus collected from online videos and enriched with fine-grained manual annotations, including transcription, speaker age, gender, and accent strength. Combining…

The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science
Woong Shin, Renan Souza, Daniel Rosendo, Fr\'ed\'eric Suter, Feiyi Wang, Prasanna Balaprakash, Rafael Ferreira da Silva
https://arxiv.org/abs/2509.09915

The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science
Modern scientific discovery increasingly requires coordinating distributed facilities and heterogeneous resources, forcing researchers to act as manual workflow coordinators rather than scientists. Advances in AI leading to AI agents show exciting new opportunities that can accelerate scientific discovery by providing intelligence as a component in the ecosystem. However, it is unclear how this new capability would materialize and integrate in the real world. To address this, we propose a conce…

A large-scale, unsupervised pipeline for automatic corpus annotation using LLMs: variation and change in the English consider construction
Cameron Morin, Matti Marttinen Larsson
https://arxiv.org/abs/2510.12306

A large-scale, unsupervised pipeline for automatic corpus annotation using LLMs: variation and change in the English consider construction
As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic work. We address this challenge by presenting a scalable, unsupervised pipeline for automating grammatical annotation in voluminous corpora using large language models (LLMs). Unlike previous supervised and iterative approaches, our method employs a four-phase workflow: prompt engineering, pre-hoc evaluation, automated batch processing, and post-hoc…

Israel-based Terra Security, which offers an AI-driven penetration testing platform, raised a $30M Series A led by Felicis, bringing its total funding to $38M (Meir Orbach/CTech)
https://www.calcalistech.com/ctechnews/article/awdq1yv5k

AI startup Terra Security raises $30M Series A for continuous penetration testing
Israeli startup aims to scale offensive security testing beyond manual and costly methods.

REFN: A Reinforcement-Learning-From-Network Framework against 1-day/n-day Exploitations
Tianlong Yu, Lihong Liu, Ziyi Zhou, Fudu Xing, Kailong Wang, Yang Yang
https://arxiv.org/abs/2508.10701

REFN: A Reinforcement-Learning-From-Network Framework against 1-day/n-day Exploitations
The exploitation of 1 day or n day vulnerabilities poses severe threats to networked devices due to massive deployment scales and delayed patching (average Mean Time To Patch exceeds 60 days). Existing defenses, including host based patching and network based filtering, are inadequate due to limited scalability across diverse devices, compatibility issues especially with embedded or legacy systems, and error prone deployment process (manual patch validation). To address these issues, we introdu…

Advancing Autonomous Incident Response: Leveraging LLMs and Cyber Threat Intelligence
Amine Tellache, Abdelaziz Amara Korba, Amdjed Mokhtari, Horea Moldovan, Yacine Ghamri-Doudane
https://arxiv.org/abs/2508.10677

Advancing Autonomous Incident Response: Leveraging LLMs and Cyber Threat Intelligence
Effective incident response (IR) is critical for mitigating cyber threats, yet security teams are overwhelmed by alert fatigue, high false-positive rates, and the vast volume of unstructured Cyber Threat Intelligence (CTI) documents. While CTI holds immense potential for enriching security operations, its extensive and fragmented nature makes manual analysis time-consuming and resource-intensive. To bridge this gap, we introduce a novel Retrieval-Augmented Generation (RAG)-based framework that …

Robot guide with multi-agent control and automatic scenario generation with LLM
Elizaveta D. Moskovskaya, Anton D. Moscowsky
https://arxiv.org/abs/2509.10317 https://

Robot guide with multi-agent control and automatic scenario generation with LLM
The work describes the development of a hybrid control architecture for an anthropomorphic tour guide robot, combining a multi-agent resource management system with automatic behavior scenario generation based on large language models. The proposed approach aims to overcome the limitations of traditional systems, which rely on manual tuning of behavior scenarios. These limitations include manual configuration, low flexibility, and lack of naturalness in robot behavior. The process of preparing …

Hey Dashboard!: Supporting Voice, Text, and Pointing Modalities in Dashboard Onboarding
Vaishali Dhanoa, Gabriela Molina Le\'on, Eve Hoggan, Eduard Gr\"oller, Marc Streit, Niklas Elmqvist
https://arxiv.org/abs/2510.12386

Hey Dashboard!: Supporting Voice, Text, and Pointing Modalities in Dashboard Onboarding
Visualization dashboards are regularly used for data exploration and analysis, but their complex interactions and interlinked views often require time-consuming onboarding sessions from dashboard authors. Preparing these onboarding materials is labor-intensive and requires manual updates when dashboards change. Recent advances in multimodal interaction powered by large language models (LLMs) provide ways to support self-guided onboarding. We present DIANA (Dashboard Interactive Assistant for Na…

"Smart Manual": KI-Chatbot hilft bei Eigenmarken von MediaMarkt und Saturn
MediaMarktSaturn veröffentlicht den KI-Chatbot "Smart Manual", der rund um die Uhr für Fragen zu Eigenmarken-Produkten hilft – zunächst in Deutschland.

QONNECT: A QoS-Aware Orchestration System for Distributed Kubernetes Clusters
Haci Ismail Aslan, Syed Muhammad Mahmudul Haque, Joel Witzke, Odej Kao
https://arxiv.org/abs/2510.09851

QONNECT: A QoS-Aware Orchestration System for Distributed Kubernetes Clusters
Modern applications increasingly span across cloud, fog, and edge environments, demanding orchestration systems that can adapt to diverse deployment contexts while meeting Quality-of-Service (QoS) requirements. Standard Kubernetes schedulers do not account for user-defined objectives such as energy efficiency, cost optimization, and global performance, often leaving operators to make manual, cluster-by-cluster placement decisions. To address this need, we present QONNECT, a vendor-agnostic orch…

Towards An Approach to Identify Divergences in Hardware Designs for HPC Workloads
Doru Thom Popovici, Mario Vega, Angelos Ioannou, Fabien Chaix, Dania Mosuli, Blair Reasoner, Tan Nguyen, Xiaokun Yang, John Shalf
https://arxiv.org/abs/2509.09774

Towards An Approach to Identify Divergences in Hardware Designs for HPC Workloads
Developing efficient hardware accelerators for mathematical kernels used in scientific applications and machine learning has traditionally been a labor-intensive task. These accelerators typically require low-level programming in Verilog or other hardware description languages, along with significant manual optimization effort. Recently, to alleviate this challenge, high-level hardware design tools like Chisel and High-Level Synthesis have emerged. However, as with any compiler, some of the gen…

Iterative Data Curation with Theoretical Guarantees
V\"ain\"o Yrj\"an\"ainen Johan Jonasson, M{\aa}ns Magnusson
https://arxiv.org/abs/2510.11428 https://

Iterative Data Curation with Theoretical Guarantees
In recent years, more and more large data sets have become available. Data accuracy, the absence of verifiable errors in data, is crucial for these large materials to enable high-quality research, downstream applications, and model training. This results in the problem of how to curate or improve data accuracy in such large and growing data, especially when the data is too large for manual curation to be feasible. This paper presents a unified procedure for iterative and continuous improvement …

pyspect: An Extensible Toolbox for Automatic Construction of Temporal Logic Trees via Reachability Analysis
Kaj Munhoz Arfvidsson, Loizos Hadjiloizou, Frank J. Jiang, Karl H. Johansson, Jonas M{\aa}rtensson
https://arxiv.org/abs/2510.11316

pyspect: An Extensible Toolbox for Automatic Construction of Temporal Logic Trees via Reachability Analysis
In this paper, we present pyspect, a Python toolbox that simplifies the use of reachability analysis for temporal logic problems. Currently, satisfying complex requirements in cyber-physical systems requires significant manual effort and domain expertise to develop the underlying reachability programs. This high development effort limits the broader adoption of reachability analysis for complex verification problems. To address this, pyspect provides a method-agnostic approach to performing rea…

Quest for a clinically relevant medical image segmentation metric: the definition and implementation of Medical Similarity Index
Szuzina Fazekas, Bettina Katalin Budai, Viktor B\'erczi, P\'al Maurovich-Horvat, Zsolt Vizi
https://arxiv.org/abs/2508.09722

Quest for a clinically relevant medical image segmentation metric: the definition and implementation of Medical Similarity Index
Background: In the field of radiology and radiotherapy, accurate delineation of tissues and organs plays a crucial role in both diagnostics and therapeutics. While the gold standard remains expert-driven manual segmentation, many automatic segmentation methods are emerging. The evaluation of these methods primarily relies on traditional metrics that only incorporate geometrical properties and fail to adapt to various applications. Aims: This study aims to develop and implement a clinically re…

Ensemble-Based Data Assimilation for Material Model Characterization in High-Velocity Impact
Rong Jin, Guangyao Wang, Xingsheng Sun
https://arxiv.org/abs/2510.09703 https://

Ensemble-Based Data Assimilation for Material Model Characterization in High-Velocity Impact
High-fidelity simulations are essential for understanding and predicting the behavior of materials under high-velocity impact (HVI) in both fundamental research and practical applications. However, their accuracy relies on material models and parameters that are traditionally obtained through manual fitting to multiple time- and labor-intensive experiments. This study presents an ensemble-based data assimilation (DA) framework to automatically and simultaneously calibrate plasticity, fracture, …

HelixVS: Deep Learning Enhanced Structure-based Virtual Screening Platform for Hit Discovery
Shanzhuo Zhang, Xianbin Ye, Donglong He, Yueyang Huang, Xiaonan Zhang, Xiaomin Fang
https://arxiv.org/abs/2508.10262

HelixVS: Deep Learning Enhanced Structure-based Virtual Screening Platform for Hit Discovery
Drug discovery through virtual screening (VS) has become a popular strategy for identifying hits against protein targets. VS has the potential to reduce the cost and time needed for manual selection and wet-laboratory experiments. Improving the cost-effectiveness of virtual screening is a significant challenge, aiming to explore larger compound libraries while maintaining lower screening costs. Here, we present HelixVS, a structure-based VS platform enhanced by deep learning models. HelixVS int…

ToonComposer: Streamlining Cartoon Production with Generative Post-Keyframing
Lingen Li, Guangzhi Wang, Zhaoyang Zhang, Yaowei Li, Xiaoyu Li, Qi Dou, Jinwei Gu, Tianfan Xue, Ying Shan
https://arxiv.org/abs/2508.10881

ToonComposer: Streamlining Cartoon Production with Generative Post-Keyframing
Traditional cartoon and anime production involves keyframing, inbetweening, and colorization stages, which require intensive manual effort. Despite recent advances in AI, existing methods often handle these stages separately, leading to error accumulation and artifacts. For instance, inbetweening approaches struggle with large motions, while colorization methods require dense per-frame sketches. To address this, we introduce ToonComposer, a generative model that unifies inbetweening and coloriz…

Best thing of the day? Finally cracking that volume change on the River wm, keybinds working now! What cracked it? Reading that effing manual of course 😅 And not deleting code you deem too much, but is essential to get it working 🤣
#freebsd #riverwm

An Automated Multi-Modal Evaluation Framework for Mobile Intelligent Assistants
Meiping Wang, Jian Zhong, Rongduo Han, Liming Kang, Zhengkun Shi, Xiao Liang, Xing Lin, Nan Gao, Haining Zhang
https://arxiv.org/abs/2508.09507

An Automated Multi-Modal Evaluation Framework for Mobile Intelligent Assistants
With the rapid development of mobile intelligent assistant technologies, multi-modal AI assistants have become essential interfaces for daily user interactions. However, current evaluation methods face challenges including high manual costs, inconsistent standards, and subjective bias. This paper proposes an automated multi-modal evaluation framework based on large language models and multi-agent collaboration. The framework employs a three-tier agent architecture consisting of interaction eval…

Jailbreaking Commercial Black-Box LLMs with Explicitly Harmful Prompts
Chiyu Zhang, Lu Zhou, Xiaogang Xu, Jiafei Wu, Liming Fang, Zhe Liu
https://arxiv.org/abs/2508.10390 https:…

Jailbreaking Commercial Black-Box LLMs with Explicitly Harmful Prompts
Evaluating jailbreak attacks is challenging when prompts are not overtly harmful or fail to induce harmful outputs. Unfortunately, many existing red-teaming datasets contain such unsuitable prompts. To evaluate attacks accurately, these datasets need to be assessed and cleaned for maliciousness. However, existing malicious content detection methods rely on either manual annotation, which is labor-intensive, or large language models (LLMs), which have inconsistent accuracy in harmful types. To b…

PyMigTool: a tool for end-to-end Python library migration
Mohayeminul Islam, Ajay Kumar Jha, May Mahmoud, Sarah Nadi
https://arxiv.org/abs/2510.08810 https://

PyMigTool: a tool for end-to-end Python library migration
Library migration is the process of replacing a library with a similar one in a software project. Manual library migration is time consuming and error prone, as it requires developers to understand the Application Programming Interfaces (API) of both libraries, map equivalent APIs, and perform the necessary code transformations. Due to the difficulty of the library migration process, most of the existing automated techniques and tooling stop at the API mapping stage or support a limited set of …

New on blog: "How we incidentally uncovered a 7-year old bug in gentoo-ci"
"""
“Gentoo CI” is the service providing periodic linting for the Gentoo repository. It is a part of the Repository mirror and CI project that I’ve started in 2015. Of course, it all started as a temporary third-party solution, but it persisted, was integrated into Gentoo Infrastructure and grew organically into quite a monstrosity.
It’s imperfect in many ways. In particular, it has only some degree of error recovery and when things go wrong beyond that, it requires a manual fix. Often the “fix” is to stop mirroring a problematic repository. Over time, I’ve started having serious doubts about the project, and proposed sunsetting most of it.
Lately, things have been getting worse. What started as a minor change in behavior of Git triggered a whole cascade of failures, leading to me finally announcing the deadline for sunsetting the mirroring of third-party repositories, and starting ripping non-critical bits out of it. Interesting enough, this whole process led me to finally discover the root cause of most of these failures — a bug that has existed since the very early version of the code, but happened to be hidden by the hacky error recovery code. Here’s the story of it.
"""
#Gentoo

Extract tables from pdfs with {tabulapdf} #rstats #datasciece

Designing and Evaluating AI Margin Notes in Document Reader Software
Nikhita Joshi, Daniel Vogel
https://arxiv.org/abs/2509.09840 https://arxiv.org/pdf/250…

Designing and Evaluating AI Margin Notes in Document Reader Software
AI capabilities for document reader software are usually presented in separate chat interfaces. We explore integrating AI into document comments, a concept we formalize as AI margin notes. Three design parameters characterize this approach: margin notes are integrated with the text while chat interfaces are not; selecting text for a margin note can be automated through AI or manual; and the generation of a margin note can involve AI to various degrees. Two experiments investigate integration an…

4 updates to old posts this week:
1. Embeds Beau Vass’s id24 video:
https://adrianroselli.com/2023/01/comparing-manual-and-free-automated-wcag-reviews.html#Update07
2. Embeds Lola Odelola’s id24 video:

Comparing Manual and Free Automated WCAG Reviews
Automated accessibility testing tools cannot test 100% of WCAG. This position is not controversial. Other than overlay vendors, no automated tool maker makes that claim. This is partly because WCAG is targeted at humans, not code, and so nuance and context apply. Free automated accessibility testing tools may have even…

Motive-level Analysis of Form-functions Association in Korean Folk song
Danbinaerin Han, Dasaem Jeong, Juhan Nam
https://arxiv.org/abs/2508.10472 https://a…

Motive-level Analysis of Form-functions Association in Korean Folk song
Computational analysis of folk song audio is challenging due to structural irregularities and the need for manual annotation. We propose a method for automatic motive segmentation in Korean folk songs by fine-tuning a speech transcription model on audio lyric with motif boundary annotation. Applying this to 856 songs, we extracted motif count and duration entropy as structural features. Statistical analysis revealed that these features vary systematically according to the social function of the…

Man-Made Heuristics Are Dead. Long Live Code Generators!
Rohit Dwivedula, Divyanshu Saxena, Aditya Akella, Swarat Chaudhuri, Daehyeok Kim
https://arxiv.org/abs/2510.08803 https:…

Man-Made Heuristics Are Dead. Long Live Code Generators!
Policy design for various systems controllers has conventionally been a manual process, with domain experts carefully tailoring heuristics for the specific instance in which the policy will be deployed. In this paper, we re-imagine policy design via a novel automated search technique fueled by recent advances in generative models, specifically Large Language Model (LLM)-driven code generation. We outline the design and implementation of PolicySmith, a framework that applies LLMs to synthesize i…

MVP4D: Multi-View Portrait Video Diffusion for Animatable 4D Avatars
Felix Taubner, Ruihang Zhang, Mathieu Tuli, Sherwin Bahmani, David B. Lindell
https://arxiv.org/abs/2510.12785

MVP4D: Multi-View Portrait Video Diffusion for Animatable 4D Avatars
Digital human avatars aim to simulate the dynamic appearance of humans in virtual environments, enabling immersive experiences across gaming, film, virtual reality, and more. However, the conventional process for creating and animating photorealistic human avatars is expensive and time-consuming, requiring large camera capture rigs and significant manual effort from professional 3D artists. With the advent of capable image and video generation models, recent methods enable automatic rendering o…

Biomedical Signal Processing: EEG and ECG Classification with Discrete Wavelet Transforms, Energy Distribution, and Convolutional Neural Networks
Justin London
https://arxiv.org/abs/2508.08602

Biomedical Signal Processing: EEG and ECG Classification with Discrete Wavelet Transforms, Energy Distribution, and Convolutional Neural Networks
Biomedical signal processing extract meaningful information from physiological signals like electrocardiograms (ECGs), electroencephalograms (EEGs), and electromyograms (EMGs) to diagnose, monitor, and treat medical conditions and diseases such as seizures, cardiomyopathy, and neuromuscular disorders, respectively. Traditional manual physician analysis of electrical recordings is prone to human error as subtle anomolies may not be detected. Recently, advanced deep learning has significantly imp…

Zero-Shot Large Language Model Agents for Fully Automated Radiotherapy Treatment Planning
Dongrong Yang, Xin Wu, Yibo Xie, Xinyi Li, Qiuwen Wu, Jackie Wu, Yang Sheng
https://arxiv.org/abs/2510.11754

Zero-Shot Large Language Model Agents for Fully Automated Radiotherapy Treatment Planning
Radiation therapy treatment planning is an iterative, expertise-dependent process, and the growing burden of cancer cases has made reliance on manual planning increasingly unsustainable, underscoring the need for automation. In this study, we propose a workflow that leverages a large language model (LLM)-based agent to navigate inverse treatment planning for intensity-modulated radiation therapy (IMRT). The LLM agent was implemented to directly interact with a clinical treatment planning system…

Quantifying the Value of Seismic Structural Health Monitoring for post-earthquake recovery of electric power system in terms of resilience enhancement
Huangbin Liang, Beatriz Moya, Francisco Chinesta, Eleni Chatzi
https://arxiv.org/abs/2508.10318

Quantifying the Value of Seismic Structural Health Monitoring for post-earthquake recovery of electric power system in terms of resilience enhancement
Post-earthquake recovery of electric power networks (EPNs) is critical to community resilience. Traditional recovery processes often rely on prolonged and imprecise manual inspections for damage diagnosis, leading to suboptimal repair prioritization and extended service disruptions. Seismic Structural Health Monitoring (SSHM) offers the potential to expedite recovery by enabling more accurate and timely damage assessment. However, SSHM deployment incurs costs, and its system-level resilience be…

Human-AI Collaboration Increases Efficiency in Regulatory Writing
Umut Eser, Yael Gozin, L. Jay Stallons, Ari Caroline, Martin Preusse, Brandon Rice, Scott Wright, Andrew Robertson
https://arxiv.org/abs/2509.09738

Human-AI Collaboration Increases Efficiency in Regulatory Writing
Background: Investigational New Drug (IND) application preparation is time-intensive and expertise-dependent, slowing early clinical development. Objective: To evaluate whether a large language model (LLM) platform (AutoIND) can reduce first-draft composition time while maintaining document quality in regulatory submissions. Methods: Drafting times for IND nonclinical written summaries (eCTD modules 2.6.2, 2.6.4, 2.6.6) generated by AutoIND were directly recorded. For comparison, manual draftin…

The “Project 2025” manual is to Darth #Trump and his administration what “Mein Kampf” was to #Hitler and the #NSDAP.
This must be clearly recognized and acknowledged.

The Top 100 Things I'd Do
If I Ever Became An Evil Overlord
http://www.eviloverlord.com/lists/overlord.html

Real-time deep learning phase imaging flow cytometer reveals blood cell aggregate biomarkers for haematology diagnostics
Kerem Delikoyun, Qianyu Chen, Liu Wei, Si Ko Myo, Johannes Krell, Martin Schlegel, Win Sen Kuan, John Tshon Yit Soong, Gerhard Schneider, Clarissa Prazeres da Costa, Percy A. Knolle, Laurent Renia, Matthew Edward Cove, Hwee Kuan Lee, Klaus Diepold, Oliver Hayden

Real-time deep learning phase imaging flow cytometer reveals blood cell aggregate biomarkers for haematology diagnostics
While analysing rare blood cell aggregates remains challenging in automated haematology, they could markedly advance label-free functional diagnostics. Conventional flow cytometers efficiently perform cell counting with leukocyte differentials but fail to identify aggregates with flagged results, requiring manual reviews. Quantitative phase imaging flow cytometry captures detailed aggregate morphologies, but clinical use is hampered by massive data storage and offline processing. Incorporating …

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 …

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…

Self-Augmented Robot Trajectory: Efficient Imitation Learning via Safe Self-augmentation with Demonstrator-annotated Precision
Hanbit Oh, Masaki Murooka, Tomohiro Motoda, Ryoichi Nakajo, Yukiyasu Domae
https://arxiv.org/abs/2509.09893

Self-Augmented Robot Trajectory: Efficient Imitation Learning via Safe Self-augmentation with Demonstrator-annotated Precision
Imitation learning is a promising paradigm for training robot agents; however, standard approaches typically require substantial data acquisition -- via numerous demonstrations or random exploration -- to ensure reliable performance. Although exploration reduces human effort, it lacks safety guarantees and often results in frequent collisions -- particularly in clearance-limited tasks (e.g., peg-in-hole) -- thereby, necessitating manual environmental resets and imposing additional human burden.…

Manual do Manifestante que Bate Ponto e Assina Chamada
Quando pensamos nas tragédias que afligem o mundo — fome, guerras, desigualdade, mudanças climšticas — é fšcil nos sentirmos sobrecarregados. Como transformar a realidade se ainda precisamos entregar relatórios no trabalho e estudar para as provas?
htt…

Manual do Manifestante que Bate Ponto e Assina Chamada
Quando pensamos nas tragédias que afligem o mundo — fome, guerras, desigualdade, mudanças climáticas — é fácil nos sentirmos sobrecarregados. Como transformar a realidade se ainda precisamos entreg…

Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs
Yumin Choi, Dongki Kim, Jinheon Baek, Sung Ju Hwang
https://arxiv.org/abs/2510.09201 https://

Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs
Large Language Models (LLMs) have shown remarkable success, and their multimodal expansions (MLLMs) further unlock capabilities spanning images, videos, and other modalities beyond text. However, despite this shift, prompt optimization approaches, designed to reduce the burden of manual prompt crafting while maximizing performance, remain confined to text, ultimately limiting the full potential of MLLMs. Motivated by this gap, we introduce the new problem of multimodal prompt optimization, whic…

Towards Scalable Training for Handwritten Mathematical Expression Recognition
Haoyang Li, Jiaqing Li, Jialun Cao, Zongyuan Yang, Yongping Xiong
https://arxiv.org/abs/2508.09220 …

Towards Scalable Training for Handwritten Mathematical Expression Recognition
Large foundation models have achieved significant performance gains through scalable training on massive datasets. However, the field of \textbf{H}andwritten \textbf{M}athematical \textbf{E}xpression \textbf{R}ecognition (HMER) has been impeded by the scarcity of data, primarily due to the arduous and costly process of manual annotation. To bridge this gap, we propose a novel method integrating limited handwritten formulas with large-scale LaTeX-rendered formulas by developing a scalable data e…

My #Lenovo #x13 manual says:
"Maximum #altitude (without pressurization): 3048 m (10,000 ft)"
🤔
Well, that's actually interesting to know because here in

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

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…

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…

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…

Panicked message from the boss. Looks like one of the documentation files is corrupted. We haven't had the time to cross train that process. Too many manual steps to extract the information from the repository, download to the network work folder, import into Word format, locate specific items and run the macro to format those, export to PDF and then reindex the production doc folder with the change.

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

Hybrid Explanation-Guided Learning for Transformer-Based Chest X-Ray Diagnosis
Shelley Zixin Shu, Haozhe Luo, Alexander Poellinger, Mauricio Reyes
https://arxiv.org/abs/2510.12704

Hybrid Explanation-Guided Learning for Transformer-Based Chest X-Ray Diagnosis
Transformer-based deep learning models have demonstrated exceptional performance in medical imaging by leveraging attention mechanisms for feature representation and interpretability. However, these models are prone to learning spurious correlations, leading to biases and limited generalization. While human-AI attention alignment can mitigate these issues, it often depends on costly manual supervision. In this work, we propose a Hybrid Explanation-Guided Learning (H-EGL) framework that combines…

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.

NL2GenSym: Natural Language to Generative Symbolic Rules for SOAR Cognitive Architecture via Large Language Models
Fang Yuan, Junjie Zeng, Yue Hu, Zhengqiu Zhu, Quanjun Yin, Yuxiang Xie
https://arxiv.org/abs/2510.09355

NL2GenSym: Natural Language to Generative Symbolic Rules for SOAR Cognitive Architecture via Large Language Models
SOAR, a classic symbol-based cognitive architecture, has been fostering the development of general, human-like intelligent agents. Nevertheless, its practical adoption is hindered by the laborious manual rule coding. Emerging Large Language Models (LLMs) present the immense potential for efficient rules generation. However, there is a critical gap that current research predominantly focuses on conceptual frameworks and lacks robust experimental validation. To bridge this gap, we propose \textit…

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…

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…

Enhanced SegNet with Integrated Grad-CAM for Interpretable Retinal Layer Segmentation in OCT Images
S M Asiful Islam Saky, Ugyen Tshering
https://arxiv.org/abs/2509.07795 https:…

Enhanced SegNet with Integrated Grad-CAM for Interpretable Retinal Layer Segmentation in OCT Images
Optical Coherence Tomography (OCT) is essential for diagnosing conditions such as glaucoma, diabetic retinopathy, and age-related macular degeneration. Accurate retinal layer segmentation enables quantitative biomarkers critical for clinical decision-making, but manual segmentation is time-consuming and variable, while conventional deep learning models often lack interpretability. This work proposes an improved SegNet-based deep learning framework for automated and interpretable retinal layer s…

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 …

AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators
Jason Chou, Ao Liu, Yuchi Deng, Zhiying Zeng, Tao Zhang, Haotian Zhu, Jianwei Cai, Yue Mao, Chenchen Zhang, Lingyun Tan, Ziyan Xu, Bohui Zhai, Hengyi Liu, Speed Zhu, Wiggin Zhou, Fengzong Lian
https://arxiv.org/abs/2508.09101

AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, with code generation emerging as a key area of focus. While numerous benchmarks have been proposed to evaluate their code generation abilities, these benchmarks face several critical limitations. First, they often rely on manual annotations, which are time-consuming and difficult to scale across different programming languages and problem complexities. Second, most existing benchmarks focus primarily …

PySeizure: A single machine learning classifier framework to detect seizures in diverse datasets
Bartlomiej Chybowski, Shima Abdullateef, Hollan Haule, Alfredo Gonzalez-Sulser, Javier Escudero
https://arxiv.org/abs/2508.07253

PySeizure: A single machine learning classifier framework to detect seizures in diverse datasets
Reliable seizure detection is critical for diagnosing and managing epilepsy, yet clinical workflows remain dependent on time-consuming manual EEG interpretation. While machine learning has shown promise, existing approaches often rely on dataset-specific optimisations, limiting their real-world applicability and reproducibility. Here, we introduce an innovative, open-source machine-learning framework that enables robust and generalisable seizure detection across varied clinical datasets. We eva…

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…

Placeit! A Framework for Learning Robot Object Placement Skills
Amina Ferrad, Johann Huber, Fran\c{c}ois H\'el\'enon, Julien Gleyze, Mahdi Khoramshahi, St\'ephane Doncieux
https://arxiv.org/abs/2510.09267

Placeit! A Framework for Learning Robot Object Placement Skills
Robotics research has made significant strides in learning, yet mastering basic skills like object placement remains a fundamental challenge. A key bottleneck is the acquisition of large-scale, high-quality data, which is often a manual and laborious process. Inspired by Graspit!, a foundational work that used simulation to automatically generate dexterous grasp poses, we introduce Placeit!, an evolutionary-computation framework for generating valid placement positions for rigid objects. Placei…

An Interactive Platform for Unified Assessment of Drug-Drug Interactions Using Descriptive and Pharmacokinetic Data
Nadezhda Diadkina
https://arxiv.org/abs/2508.08351 https://…

An Interactive Platform for Unified Assessment of Drug-Drug Interactions Using Descriptive and Pharmacokinetic Data
Drug-drug interactions (DDIs) are a major concern in polypharmacy. Public databases often provide only qualitative descriptions without pharmacokinetic context. We present an interactive web tool that integrates 191,541 descriptive DDI records from DrugBank with 3,779 AUC-based interactions from the PK-DDIP dataset, extracted from FDA-approved drug labeling (DailyMed) using natural language processing and manual verification. Using multi-step name harmonization (exact, fuzzy, synonym expansion,…

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 …

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…

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 …

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…

SpaceVista: All-Scale Visual Spatial Reasoning from mm to km
Peiwen Sun, Shiqiang Lang, Dongming Wu, Yi Ding, Kaituo Feng, Huadai Liu, Zhen Ye, Rui Liu, Yun-Hui Liu, Jianan Wang, Xiangyu Yue
https://arxiv.org/abs/2510.09606

SpaceVista: All-Scale Visual Spatial Reasoning from mm to km
With the current surge in spatial reasoning explorations, researchers have made significant progress in understanding indoor scenes, but still struggle with diverse applications such as robotics and autonomous driving. This paper aims to advance all-scale spatial reasoning across diverse scenarios by tackling two key challenges: 1) the heavy reliance on indoor 3D scans and labor-intensive manual annotations for dataset curation; 2) the absence of effective all-scale scene modeling, which often …

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…

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…

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…

Automated Classification of Tutors' Dialogue Acts Using Generative AI: A Case Study Using the CIMA Corpus
Liqun He, Jiaqi Xu
https://arxiv.org/abs/2509.09125 https://…

Automated Classification of Tutors' Dialogue Acts Using Generative AI: A Case Study Using the CIMA Corpus
This study explores the use of generative AI for automating the classification of tutors' Dialogue Acts (DAs), aiming to reduce the time and effort required by traditional manual coding. This case study uses the open-source CIMA corpus, in which tutors' responses are pre-annotated into four DA categories. Both GPT-3.5-turbo and GPT-4 models were tested using tailored prompts. Results show that GPT-4 achieved 80% accuracy, a weighted F1-score of 0.81, and a Cohen's Kappa of 0.74, surpassing base…

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…

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 …

Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement: A Multilingual RAG Approach
Imene Kolli, Ario Saeid Vaghefi, Chiara Colesanti Senni, Shantam Raj, Markus Leippold
https://arxiv.org/abs/2509.08907

Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement: A Multilingual RAG Approach
InfluenceMap's LobbyMap Platform monitors the climate policy engagement of over 500 companies and 250 industry associations, assessing each entity's support or opposition to science-based policy pathways for achieving the Paris Agreement's goal of limiting global warming to 1.5°C. Although InfluenceMap has made progress with automating key elements of the analytical workflow, a significant portion of the assessment remains manual, making it time- and labor-intensive and susceptible to human er…

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…

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…

AutoStub: Genetic Programming-Based Stub Creation for Symbolic Execution
Felix M\"achtle, Nils Loose, Jan-Niclas Serr, Jonas Sander, Thomas Eisenbarth
https://arxiv.org/abs/2509.08524

AutoStub: Genetic Programming-Based Stub Creation for Symbolic Execution
Symbolic execution is a powerful technique for software testing, but suffers from limitations when encountering external functions, such as native methods or third-party libraries. Existing solutions often require additional context, expensive SMT solvers, or manual intervention to approximate these functions through symbolic stubs. In this work, we propose a novel approach to automatically generate symbolic stubs for external functions during symbolic execution that leverages Genetic Programmi…

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)…

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…

MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline
Rushi Qiang, Yuchen Zhuang, Anikait Singh, Percy Liang, Chao Zhang, Sherry Yang, Bo Dai
https://arxiv.org/abs/2510.07307

MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline
While Language Models (LMs) have made significant progress in automating machine learning engineering (MLE), the acquisition of high-quality MLE training data is significantly constrained. Current MLE benchmarks suffer from low scalability and limited applicability because they rely on static, manually curated tasks, demanding extensive time and manual effort to produce. We introduce MLE-Smith, a fully automated multi-agent pipeline, to transform raw datasets into competition-style MLE challeng…

Past, Present, and Future of Bug Tracking in the Generative AI Era
Utku Boran Torun, Mehmet Taha Demircan, Mahmut Furkan G\"on, Eray T\"uz\"un
https://arxiv.org/abs/2510.08005

Past, Present, and Future of Bug Tracking in the Generative AI Era
Traditional bug tracking systems rely heavily on manual reporting, reproduction, triaging, and resolution, each carried out by different stakeholders such as end users, customer support, developers, and testers. This division of responsibilities requires significant coordination and widens the communication gap between non-technical users and technical teams, slowing the process from bug discovery to resolution. Moreover, current systems are highly asynchronous; users often wait hours or days f…

Using Robotics to Improve Transcatheter Edge-to-Edge Repair of the Mitral Valve
L\'ea Pistorius, Namrata U. Nayar, Phillip Tran, Sammy Elmariah, Pierre E. Dupont
https://arxiv.org/abs/2510.04178

Using Robotics to Improve Transcatheter Edge-to-Edge Repair of the Mitral Valve
Transcatheter valve repair presents significant challenges due to the mechanical limitations and steep learning curve associated with manual catheter systems. This paper investigates the use of robotics to facilitate transcatheter procedures in the context of mitral valve edge-to-edge repair. The complex handle-based control of a clinical repair device is replaced by intuitive robotic joint-based control via a game controller. Manual versus robotic performance is analyzed by decomposing the ove…

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…

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…

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…

HotBugs.jar: A Benchmark of Hot Fixes for Time-Critical Bugs
Carol Hanna, Federica Sarro, Mark Harman, Justyna Petke
https://arxiv.org/abs/2510.07529 https://

HotBugs.jar: A Benchmark of Hot Fixes for Time-Critical Bugs
Hot fixes are urgent, unplanned changes deployed to production systems to address time-critical issues. Despite their importance, no existing evaluation benchmark focuses specifically on hot fixes. We present HotBugs.jar, the first dataset dedicated to real-world hot fixes. From an initial mining of 10 active Apache projects totaling over 190K commits and 150K issue reports, we identified 746 software patches that met our hot-fix criteria. After manual evaluation, 679 were confirmed as genuine …

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…

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…

MoA-VR: A Mixture-of-Agents System Towards All-in-One Video Restoration
Lu Liu, Chunlei Cai, Shaocheng Shen, Jianfeng Liang, Weimin Ouyang, Tianxiao Ye, Jian Mao, Huiyu Duan, Jiangchao Yao, Xiaoyun Zhang, Qiang Hu, Guangtao Zhai
https://arxiv.org/abs/2510.08508

MoA-VR: A Mixture-of-Agents System Towards All-in-One Video Restoration
Real-world videos often suffer from complex degradations, such as noise, compression artifacts, and low-light distortions, due to diverse acquisition and transmission conditions. Existing restoration methods typically require professional manual selection of specialized models or rely on monolithic architectures that fail to generalize across varying degradations. Inspired by expert experience, we propose MoA-VR, the first \underline{M}ixture-\underline{o}f-\underline{A}gents \underline{V}ideo …

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…

Learning to Rewrite Prompts for Bootstrapping LLMs on Downstream Tasks
Qinhao Zhou, Xiang Xiang, Kun He, John E. Hopcroft
https://arxiv.org/abs/2510.06695 https://

Learning to Rewrite Prompts for Bootstrapping LLMs on Downstream Tasks
In recent years, the growing interest in Large Language Models (LLMs) has significantly advanced prompt engineering, transitioning from manual design to model-based optimization. Prompts for LLMs generally comprise two components: the \textit{instruction}, which defines the task or objective, and the \textit{input}, which is tailored to the instruction type. In natural language generation (NLG) tasks such as machine translation, the \textit{input} component is particularly critical, while the \…

MATRIX: Multimodal Agent Tuning for Robust Tool-Use Reasoning
Tajamul Ashraf, Umair Nawaz, Abdelrahman M. Shaker, Rao Anwer, Philip Torr, Fahad Shahbaz Khan, Salman Khan
https://arxiv.org/abs/2510.08567

MATRIX: Multimodal Agent Tuning for Robust Tool-Use Reasoning
Vision language models (VLMs) are increasingly deployed as controllers with access to external tools for complex reasoning and decision-making, yet their effectiveness remains limited by the scarcity of high-quality multimodal trajectories and the cost of manual annotation. We address this challenge with a vision-centric agent tuning framework that automatically synthesizes multimodal trajectories, generates step-wise preference pairs, and trains a VLM controller for robust tool-use reasoning. …

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…