Tootfinder

Series D, Episode 09 - Sand
SLAVE: Er, slipping, Master.
AVON: Soolin, take over.
SOOLIN: Give me manual control, Slave. [Goes to the flight controls]
SLAVE: With great relief, Madam. I am somewhat disorien- disor- confused.
VILA: Sounds drunk to me.
https://blake.torpidity.net/m/409/82

Straight from the training manual:
```
1. Transit Fare Inspectors are not authorized to take violators into physical custody and
book them. Instead, a citation should be issued.
2. If the person has no satisfactory identification or refuses to sign the Civil Citation, it is SFMTA’s policy that Transit Fare Inspectors shall not physically detain the patron nor take the patron into custody. If the ind'l still refuses to provide satisfactory ID after that, we often accep…

holy shit, the STM32 reference manual contains information presented so much better than ADIv5.2

Taiichi Ohno: I wrote about how items waiting in a queue are a form of waste, as a cautionary tale
Scrum guys coming up with the backlog: we have created the work queue from the classic manual "Don't Create A Work Queue"

Logic Mining from Process Logs: Towards Automated Specification and Verification
Radoslaw Klimek, Julia Witek
https://arxiv.org/abs/2506.08628 https://

Logic Mining from Process Logs: Towards Automated Specification and Verification
Logical specifications play a key role in the formal analysis of behavioural models. Automating the derivation of such specifications is particularly valuable in complex systems, where manual construction is time-consuming and error-prone. This article presents an approach for generating logical specifications from process models discovered via workflow mining, combining pattern-based translation with automated reasoning techniques. In contrast to earlier work, we evaluate the method on both ge…

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…

from my link log —
Alan Kay did not invent object-oriented programming.
https://www.hillelwayne.com/post/alan-kay/
saved 2025-05-11 https://

This https://arxiv.org/abs/2412.05561 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Can the Rookies Cut the Tough Cookie? Exploring the Use of LLMs for SQL Equivalence Checking
Equivalence checking of SQL queries is an intractable problem often encountered in settings ranging from grading SQL submissions to debugging query optimizers. Despite recent work toward developing practical solutions, only simple queries written using a small subset of SQL are supported, leaving the equivalence checking of sophisticated SQL queries at the mercy of intensive, potentially error-prone, manual analysis. In this paper, we explore how LLMs can be used to reason with SQL queries to a…

Just Say Better or Worse: A Human-AI Collaborative Framework for Medical Image Segmentation Without Manual Annotations
Yizhe Zhang
https://arxiv.org/abs/2507.05815

Just Say Better or Worse: A Human-AI Collaborative Framework for Medical Image Segmentation Without Manual Annotations
Manual annotation of medical images is a labor-intensive and time-consuming process, posing a significant bottleneck in the development and deployment of robust medical imaging AI systems. This paper introduces a novel Human-AI collaborative framework for medical image segmentation that substantially reduces the annotation burden by eliminating the need for explicit manual pixel-level labeling. The core innovation lies in a preference learning paradigm, where human experts provide minimal, intu…

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.

Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment
Yongpeng Zhao, Maik Pfefferkorn, Maximilian Templer, Rolf Findeisen
https://arxiv.org/abs/2506.08719

Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment
Parameter tuning for vehicle controllers remains a costly and time-intensive challenge in automotive development. Traditional approaches rely on extensive real-world testing, making the process inefficient. We propose a multi-fidelity Bayesian optimization approach that efficiently learns optimal controller parameters by leveraging both low-fidelity simulation data and a very limited number of real-world experiments. Our approach significantly reduces the need for manual tuning and expensive fi…

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…

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

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…

Ah, yes, the 62 genders. Including “MALE” and “FRANCE.”
https://www.php.net/manual/en/class.gender.php

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…

Local transit nerds: the most recent MUNI fare inspector training manual is now public with only one redaction. https://sanfrancisco.nextrequest.com/requests/25-3160

ChemGraph: An Agentic Framework for Computational Chemistry Workflows
Thang D. Pham, Aditya Tanikanti, Murat Ke\c{c}eli
https://arxiv.org/abs/2506.06363 ht…

ChemGraph: An Agentic Framework for Computational Chemistry Workflows
Atomistic simulations are essential tools in chemistry and materials science, accelerating the discovery of novel catalysts, energy storage materials, and pharmaceuticals. However, running these simulations remains challenging due to the wide range of computational methods, diverse software ecosystems, and the need for expert knowledge and manual effort for the setup, execution, and validation stages. In this work, we present ChemGraph, an agentic framework powered by artificial intelligence an…

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 …

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)…

AI-powered Contextual 3D Environment Generation: A Systematic Review
Miguel Silva, Alexandre Valle de Carvalho
https://arxiv.org/abs/2506.05449 https://

AI-powered Contextual 3D Environment Generation: A Systematic Review
The generation of high-quality 3D environments is crucial for industries such as gaming, virtual reality, and cinema, yet remains resource-intensive due to the reliance on manual processes. This study performs a systematic review of existing generative AI techniques for 3D scene generation, analyzing their characteristics, strengths, limitations, and potential for improvement. By examining state-of-the-art approaches, it presents key challenges such as scene authenticity and the influence of te…

A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data
Lama Alqazlan, Zheng Fang, Michael Castelle, Rob Procter
https://arxiv.org/abs/2506.06083

A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data
The availability of big data has significantly influenced the possibilities and methodological choices for conducting large-scale behavioural and social science research. In the context of qualitative data analysis, a major challenge is that conventional methods require intensive manual labour and are often impractical to apply to large datasets. One effective way to address this issue is by integrating emerging computational methods to overcome scalability limitations. However, a critical conc…

Is the new #UNIL logo better than the old one? Hard to say without seeing the rest of the new CD. And that’s what I find strange: why announce and use the new logo when the CD manual isn’t ready yet? Why the hurry?
https://www.uni…

Is the new #UNIL logo better than the old one? Hard to say without seeing the rest of the new CD. And that’s what I find strange: why announce and use the new logo when the CD manual isn’t ready yet? Why the hurry?
https://www.uni…

Is the new #UNIL logo better than the old one? Hard to say without seeing the rest of the new CD. And that’s what I find strange: why announce and use the new logo when the CD manual isn’t ready yet? Why the hurry?
https://www.uni…

Never too many walkthroughs for a release. It may be a bit of overkill, but it works for us...
1. Pre-release the day before to go over the schedule of activities and assignments.
2. Schedule release and related backups.
3. Day of release and after schedule created for run another walkthrough to verify that the backups are where they need to be, checkpoints are there, start of the process is on manual hold.
4. Schedule an open teams call for the leads, devs and supp…

This https://arxiv.org/abs/2409.12949 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

A Learning-based Quadcopter Controller with Extreme Adaptation
This paper introduces a learning-based low-level controller for quadcopters, which adaptively controls quadcopters with significant variations in mass, size, and actuator capabilities. Our approach leverages a combination of imitation learning and reinforcement learning, creating a fast-adapting and general control framework for quadcopters that eliminates the need for precise model estimation or manual tuning. The controller estimates a latent representation of the vehicle's system parameters …

This https://arxiv.org/abs/2505.22990 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MenTeR: A fully-automated Multi-agenT workflow for end-to-end RF/Analog Circuits Netlist Design
RF/Analog design is essential for bridging digital technologies with real-world signals, ensuring the functionality and reliability of a wide range of electronic systems. However, analog design procedures are often intricate, time-consuming and reliant on expert intuition, and hinder the time and cost efficiency of circuit development. To overcome the limitations of the manual circuit design, we introduce MenTeR - a multiagent workflow integrated into an end-to-end analog design framework. By e…

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

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…

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…

On Reconfigurable Bisimulation, with an Application to the Distributed Synthesis Problem
Yehia Abd Alrahman, Nir Piterman
https://arxiv.org/abs/2505.21672 …

On Reconfigurable Bisimulation, with an Application to the Distributed Synthesis Problem
We consider the problem of distributing a centralised transition system to a set of asynchronous agents recognising the same language. Existing solutions are either manual or involve a huge explosion in the number of states from the centralised system. The difficulty arises from the need to keep a rigid communication scheme, specifying a fixed mapping from events to those who can participate in them. Thus, individual agents need to memorise seen events and their order to dynamically compare the…

LLM-Guided Scenario-based GUI Testing
Shengcheng Yu, Yuchen Ling, Chunrong Fang, Quan Zhou, Chunyang Chen, Shaomin Zhu, Zhenyu Chen
https://arxiv.org/abs/2506.05079

LLM-Guided Scenario-based GUI Testing
The assurance of mobile app GUI is more and more significant. Automated GUI testing approaches of different strategies have been developed, while there are still huge gaps between the approaches and the app business logic, not taking the completion of specific testing scenarios as the exploration target, leading to the exploration missing of critical app functionalities. Learning from the manual testing, which takes testing scenarios with app business logic as the basic granularity, in this pap…

Towards Data-Driven Model-Free Safety-Critical Control
Zhe Shen, Yitaek Kim, Christoffer Sloth
https://arxiv.org/abs/2506.06931 https://

Towards Data-Driven Model-Free Safety-Critical Control
This paper presents a framework for enabling safe velocity control of general robotic systems using data-driven model-free Control Barrier Functions (CBFs). Model-free CBFs rely on an exponentially stable velocity controller and a design parameter (e.g. alpha in CBFs); this design parameter depends on the exponential decay rate of the controller. However, in practice, the decay rate is often unavailable, making it non-trivial to use model-free CBFs, as it requires manual tuning for alpha. To ad…

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…

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

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 …

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.

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks
Tri Kurniawan Wijaya, Xinyang Shao, Gonzalo Fiz Pontiveros, Edoardo D'Amico
https://arxiv.org/abs/2506.03391

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks
Recommender systems are pivotal in delivering personalized experiences across industries, yet their adoption and scalability remain hindered by the need for extensive dataset- and task-specific configurations. Existing systems often require significant manual intervention, domain expertise, and engineering effort to adapt to new datasets or tasks, creating barriers to entry and limiting reusability. In contrast, recent advancements in large language models (LLMs) have demonstrated the transform…

Anyone using #JupyterHub (services.jupyterhub.enable=true) on :nixos: #NixOS? Then you should have run into the database migration issue here:

JupyterHub database migration
I am struggling hard to get my JupyterHub working again. After an update (longer ago), JupyterHub often requires a manual database migration and refuses to start: ❯ journalctl -efu jupyterhub ... Jun 04 22:10:41 yann-desktop-nixos jupyterhub[1038575]: Found database schema version 0eee8c825d24 != 4621fec11365. Backup your database and run `jupyterhub upgrade-db` to upgrade to the latest schema. ... Jun 04 22:10:42 yann-desktop-nixos systemd[1]: Failed to start Jupyterhub development server. …

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 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…

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

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…

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…

Advancement and Field Evaluation of a Dual-arm Apple Harvesting Robot
Keyi Zhu, Kyle Lammers, Kaixiang Zhang, Chaaran Arunachalam, Siddhartha Bhattacharya, Jiajia Li, Renfu Lu, Zhaojian Li
https://arxiv.org/abs/2506.05714

Advancement and Field Evaluation of a Dual-arm Apple Harvesting Robot
Apples are among the most widely consumed fruits worldwide. Currently, apple harvesting fully relies on manual labor, which is costly, drudging, and hazardous to workers. Hence, robotic harvesting has attracted increasing attention in recent years. However, existing systems still fall short in terms of performance, effectiveness, and reliability for complex orchard environments. In this work, we present the development and evaluation of a dual-arm harvesting robot. The system integrates a ToF c…

#Blakes7 Series D, Episode 11 - Orbit
VILA: I think so. Try it now.
AVON: Switching to manual. Maximum power on all drives.
[Biodome]
PINDER: Egrorian.
EGRORIAN: They've broken the autocontrol and switched to manual. We've allowed for that.

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

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…

This https://arxiv.org/abs/2305.12844 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

An Optimized Ensemble Deep Learning Model For Brain Tumor Classification
Brain tumors present a grave risk to human life, demanding precise and timely diagnosis for effective treatment. Inaccurate identification of brain tumors can significantly diminish life expectancy, underscoring the critical need for precise diagnostic methods. Manual identification of brain tumors within vast Magnetic Resonance Imaging (MRI) image datasets is arduous and time-consuming. Thus, the development of a reliable deep learning (DL) model is essential to enhance diagnostic accuracy and…

Computational Detection of Intertextual Parallels in Biblical Hebrew: A Benchmark Study Using Transformer-Based Language Models
David M. Smiley
https://arxiv.org/abs/2506.24117

Computational Detection of Intertextual Parallels in Biblical Hebrew: A Benchmark Study Using Transformer-Based Language Models
Identifying parallel passages in biblical Hebrew is foundational in biblical scholarship for uncovering intertextual relationships. Traditional methods rely on manual comparison, which is labor-intensive and prone to human error. This study evaluates the potential of pre-trained transformer-based language models, including E5, AlephBERT, MPNet, and LaBSE, for detecting textual parallels in the Hebrew Bible. Focusing on known parallels between the books of Samuel/Kings and Chronicles, I assessed…

The FreeBSD Forums versus the Internet Archive Wayback Machine
Recently: "Save Page Now could not capture this URL because it was unreachable. If the site is online, it may be blocking access from our service."
I assume that the block is inadvertent.
Pictured below: an attempt to capture

I've been using em dashes since I picked up the LaTeX manual in 1986 and I'm not going back just because some text extruding software uses them more than most humans.
Also, my grandfather was a printer and I knew from an early age that "em" and "en" were legit scrabble words.
#OldManYellsAtCloud

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…

This https://arxiv.org/abs/2412.06481 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

DeePC-Hunt: Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization
This paper introduces Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization (DeePC-Hunt), a backpropagation-based method for automatic hyperparameter tuning of the DeePC algorithm. The necessity for such a method arises from the importance of hyperparameter selection to achieve satisfactory closed-loop DeePC performance. The standard methods for hyperparameter selection are to either optimize the open-loop performance, or use manual guess-and-check. Optimizing th…

It's all very well to suggest people suddenly switch from the jobs that AI has taken, but who's going to hire all those manual workers?
There's no money any more... the very few have it all.
🤔

A question I was not expecting to be asked on a Monday morning...
"You’re at the edge of the desert.
🐛 Ride the Sandworm:
...or manual setup — the slow path through the sand."

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 …

DiffCkt: A Diffusion Model-Based Hybrid Neural Network Framework for Automatic Transistor-Level Generation of Analog Circuits
Chengjie Liu, Jiajia Li, Yabing Feng, Wenhao Huang, Weiyu Chen, Yuan Du, Jun Yang, Li Du
https://arxiv.org/abs/2507.00444

DiffCkt: A Diffusion Model-Based Hybrid Neural Network Framework for Automatic Transistor-Level Generation of Analog Circuits
Analog circuit design consists of the pre-layout and layout phases. Among them, the pre-layout phase directly decides the final circuit performance, but heavily depends on experienced engineers to do manual design according to specific application scenarios. To overcome these challenges and automate the analog circuit pre-layout design phase, we introduce DiffCkt: a diffusion model-based hybrid neural network framework for the automatic transistor-level generation of analog circuits, which can …

A Comparison of Bacterial Colonies Count from Petri Dishes Utilizing Hough Transform and Traditional Manual Counting
Areesha Rehman, Zikria Saleem, Jarrar Amjad, Syed Rehan Shah, Kamran Siddique
https://arxiv.org/abs/2505.20365

A Comparison of Bacterial Colonies Count from Petri Dishes Utilizing Hough Transform and Traditional Manual Counting
Bacterial colony enumeration is an essential stage in microbiological research, allowing susceptibility to antibiotics assessment, monitoring of the environment, and clinical diagnostics. Traditional manual counting methods are costly and susceptible to human mistakes, prompting the creation of automated detection systems. This research compares the efficacy of the Hough Circle Transform method for automated colony detection to hand counting of E. coli, S. aureus, and P. aeruginosa colonies on …

As many of you may have already noticed when you try to update your Arch-based distros, you will receive an error mentioning that linux-firmware-nvidia "exists in the filesystem".
To fix this you should run `sudo pacman -Rdd linux-firmware` and then `sudo pacman -Syu linux-firmware`.

Lunar Time Ephemeris LTE440: User Manual
Xu Lu, Tian-Ning Yang, Yi Xie
https://arxiv.org/abs/2506.19213 https://arxiv.org/pdf/2506.19…

Lunar Time Ephemeris LTE440: User Manual
We present the numerical lunar time ephemeris LTE440 based on the definition of Lunar Coordinate Time (TCL) given by the International Astronomical Union (IAU) in IAU 2024 Resolution II. LTE440 can be used to obtain the numerical transformation between TCL and Solar System Barycentric Dynamical Time (TCL-TDB) or Solar System Barycentric Coordinate Time (TCL-TCB). The theoretical model, numerical method, and performance of LTE440 are discussed. The secular drifts between TCL and TDB and between …

#Blakes7 Series D, Episode 11 - Orbit
VILA: I think so. Try it now.
AVON: Switching to manual. Maximum power on all drives.
[Biodome]
PINDER: Egrorian.
EGRORIAN: They've broken the autocontrol and switched to manual. We've allowed for that.

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Beyond the Desktop: XR-Driven Segmentation with Meta Quest 3 and MX Ink
Lisle Faray de Paiva, Gijs Luijten, Ana Sofia Ferreira Santos, Moon Kim, Behrus Puladi, Jens Kleesiek, Jan Egger
https://arxiv.org/abs/2506.04858

Beyond the Desktop: XR-Driven Segmentation with Meta Quest 3 and MX Ink
Medical imaging segmentation is essential in clinical settings for diagnosing diseases, planning surgeries, and other procedures. However, manual annotation is a cumbersome and effortful task. To mitigate these aspects, this study implements and evaluates the usability and clinical applicability of an extended reality (XR)-based segmentation tool for anatomical CT scans, using the Meta Quest 3 headset and Logitech MX Ink stylus. We develop an immersive interface enabling real-time interaction w…

Jailbreak-R1: Exploring the Jailbreak Capabilities of LLMs via Reinforcement Learning
Weiyang Guo, Zesheng Shi, Zhuo Li, Yequan Wang, Xuebo Liu, Wenya Wang, Fangming Liu, Min Zhang, Jing Li
https://arxiv.org/abs/2506.00782

Jailbreak-R1: Exploring the Jailbreak Capabilities of LLMs via Reinforcement Learning
As large language models (LLMs) grow in power and influence, ensuring their safety and preventing harmful output becomes critical. Automated red teaming serves as a tool to detect security vulnerabilities in LLMs without manual labor. However, most existing methods struggle to balance the effectiveness and diversity of red-team generated attack prompts. To address this challenge, we propose \ourapproach, a novel automated red teaming training framework that utilizes reinforcement learning to ex…

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…

POST: Photonic Swin Transformer for Automated and Efficient Prediction of PCSEL
Qi Xin, Hai Huang, Chenyu Li, Kewei Shi, Zhaoyu Zhang
https://arxiv.org/abs/2507.01316

POST: Photonic Swin Transformer for Automated and Efficient Prediction of PCSEL
This work designs a model named POST based on the Vision Transformer (ViT) approach. Across single, double, and even triple lattices, as well as various non-circular complex hole structures, POST enables prediction of multiple optical properties of photonic crystal layers in Photonic Crystal Surface Emitting Lasers (PCSELs) with high speed and accuracy, without requiring manual intervention, which serves as a comprehensive surrogate for the optical field simulation. In the predictions of Qualit…

This https://arxiv.org/abs/2503.09409 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation
Despite the widespread adoption of industrial robots in automotive assembly, wire harness installation remains a largely manual process, as it requires precise and flexible manipulation. To address this challenge, we design a novel AI-based framework that automates cable connector mating by integrating force control with deep visuotactile learning. Our system optimizes search-and-insertion strategies using first-order optimization over a multimodal transformer architecture trained on visual, ta…

End-to-End Learning of Probabilistic Constellation Shaping through Importance Sampling
Shrinivas Chimmalgi, Laurent Schmalen, Vahid Aref
https://arxiv.org/abs/2506.16098

End-to-End Learning of Probabilistic Constellation Shaping through Importance Sampling
Probabilistic constellation shaping enables easy rate adaption and has been proven to reduce the gap to Shannon capacity. Constellation point probabilities are optimized to maximize either the mutual information or the bit-wise mutual information. The optimization problem is however challenging even for simple channel models. While autoencoder-based machine learning has been applied successfully to solve this problem [1], it requires manual computation of additional terms for the gradient which…

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

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…

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.

Digital Transformation and the Restructuring of Employment: Evidence from Chinese Listed Firms
Yubo Cheng
https://arxiv.org/abs/2506.23230 https://

Digital Transformation and the Restructuring of Employment: Evidence from Chinese Listed Firms
This paper examines how digital transformation reshapes employment structures within Chinese listed firms, focusing on occupational functions and task intensity. Drawing on recruitment data classified under ISCO-08 and the Chinese Standard Occupational Classification 2022, we categorize jobs into five functional groups: management, professional, technical, auxiliary, and manual. Using a task-based framework, we construct routine, abstract, and manual task intensity indices through keyword analy…

This https://arxiv.org/abs/2412.06481 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

DeePC-Hunt: Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization
This paper introduces Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization (DeePC-Hunt), a backpropagation-based method for automatic hyperparameter tuning of the DeePC algorithm. The necessity for such a method arises from the importance of hyperparameter selection to achieve satisfactory closed-loop DeePC performance. The standard methods for hyperparameter selection are to either optimize the open-loop performance, or use manual guess-and-check. Optimizing th…

Many are complaining about CISA removing the RSS feed for KEV. Just a reminder: we expose a lot of the API via RSS and Atom in vulnerability-lookup. KEV is included.
🔗 https://www.vulnerability-lookup.org/user-manual/feed-syndication/

Growing a Modular Framework for Modal Systems- HOLMS: a HOL Light Library
Antonella Bilotta
https://arxiv.org/abs/2506.10048 https://…

Growing a Modular Framework for Modal Systems- HOLMS: a HOL Light Library
The present dissertation introduces the research project on HOLMS (\textbf{HOL} Light Library for \textbf{M}odal \textbf{S}ystems), a growing modular framework for modal reasoning within the HOL Light proof assistant. To provide an accessible introduction to the library, the fundamentals of modal logic are outlined first, followed by a concise manual for the proof assistant itself. The core contribution of this work on HOLMS is the development of a unified and modular strategy for proving adequ…

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 Learned Cost Model-based Cross-engine Optimizer for SQL Workloads
Andr\'as Strausz, Niels Pardon, Ioana Giurgiu
https://arxiv.org/abs/2506.02802 http…

A Learned Cost Model-based Cross-engine Optimizer for SQL Workloads
Lakehouse systems enable the same data to be queried with multiple execution engines. However, selecting the engine best suited to run a SQL query still requires a priori knowledge of the query computational requirements and an engine capability, a complex and manual task that only becomes more difficult with the emergence of new engines and workloads. In this paper, we address this limitation by proposing a cross-engine optimizer that can automate engine selection for diverse SQL queries throu…

Scalable Language Agnostic Taint Tracking using Explicit Data Dependencies
Sedick David Baker Effendi, Xavier Pinho, Andrei Michael Dreyer, Fabian Yamaguchi
https://arxiv.org/abs/2506.06247

Scalable Language Agnostic Taint Tracking using Explicit Data Dependencies
Taint analysis using explicit whole-program data-dependence graphs is powerful for vulnerability discovery but faces two major challenges. First, accurately modeling taint propagation through calls to external library procedures requires extensive manual annotations, which becomes impractical for large ecosystems. Second, the sheer size of whole-program graph representations leads to serious scalability and performance issues, particularly when quick analysis is needed in continuous development…

This https://arxiv.org/abs/2503.16512 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Multimodal Sensing and Machine Learning to Compare Printed and Verbal Assembly Instructions Delivered by a Social Robot
In this paper, we compare a manual assembly task communicated to workers using both printed and robot-delivered instructions. The comparison was made using physiological signals (blood volume pulse (BVP) and electrodermal activity (EDA)) collected from individuals during an experimental study. In addition, we also collected responses of individuals using the NASA Task Load Index (TLX) survey. Furthermore, we mapped the collected physiological signals to the responses of participants for NASA TL…

Bi-Manual Joint Camera Calibration and Scene Representation
Haozhan Tang, Tianyi Zhang, Matthew Johnson-Roberson, Weiming Zhi
https://arxiv.org/abs/2505.24819

Bi-Manual Joint Camera Calibration and Scene Representation
Robot manipulation, especially bimanual manipulation, often requires setting up multiple cameras on multiple robot manipulators. Before robot manipulators can generate motion or even build representations of their environments, the cameras rigidly mounted to the robot need to be calibrated. Camera calibration is a cumbersome process involving collecting a set of images, with each capturing a pre-determined marker. In this work, we introduce the Bi-Manual Joint Calibration and Representation Fra…

Evaluating Sound Similarity Metrics for Differentiable, Iterative Sound-Matching
Amir Salimi, Abram Hindle, Osmar R. Zaiane
https://arxiv.org/abs/2506.22628

Evaluating Sound Similarity Metrics for Differentiable, Iterative Sound-Matching
Manual sound design with a synthesizer is inherently iterative: an artist compares the synthesized output to a mental target, adjusts parameters, and repeats until satisfied. Iterative sound-matching automates this workflow by continually programming a synthesizer under the guidance of a loss function (or similarity measure) toward a target sound. Prior comparisons of loss functions have typically favored one metric over another, but only within narrow settings: limited synthesis methods, few l…

This https://arxiv.org/abs/2505.24739 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI
Accurate placental segmentation is essential for quantitative analysis of the placenta. However, this task is particularly challenging in T2*-weighted placental imaging due to: (1) weak and inconsistent boundary contrast across individual echoes; (2) the absence of manual ground truth annotations for all echo times; and (3) motion artifacts across echoes caused by fetal and maternal movement. In this work, we propose a contrast-augmented segmentation framework that leverages complementary infor…

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…

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-…

Literature-Grounded Novelty Assessment of Scientific Ideas
Simra Shahid, Marissa Radensky, Raymond Fok, Pao Siangliulue, Daniel S. Weld, Tom Hope
https://arxiv.org/abs/2506.22026 …

Literature-Grounded Novelty Assessment of Scientific Ideas
Automated scientific idea generation systems have made remarkable progress, yet the automatic evaluation of idea novelty remains a critical and underexplored challenge. Manual evaluation of novelty through literature review is labor-intensive, prone to error due to subjectivity, and impractical at scale. To address these issues, we propose the Idea Novelty Checker, an LLM-based retrieval-augmented generation (RAG) framework that leverages a two-stage retrieve-then-rerank approach. The Idea Nove…

Towards Effective Complementary Security Analysis using Large Language Models
Jonas Wagner, Simon M\"uller, Christian N\"ather, Jan-Philipp Stegh\"ofer, Andreas Both
https://arxiv.org/abs/2506.16899

Towards Effective Complementary Security Analysis using Large Language Models
A key challenge in security analysis is the manual evaluation of potential security weaknesses generated by static application security testing (SAST) tools. Numerous false positives (FPs) in these reports reduce the effectiveness of security analysis. We propose using Large Language Models (LLMs) to improve the assessment of SAST findings. We investigate the ability of LLMs to reduce FPs while trying to maintain a perfect true positive rate, using datasets extracted from the OWASP Benchmark (v…

This https://arxiv.org/abs/2502.03783 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

UltraBones100k: A reliable automated labeling method and large-scale dataset for ultrasound-based bone surface extraction
Ultrasound-based bone surface segmentation is crucial in computer-assisted orthopedic surgery. However, ultrasound images have limitations, including a low signal-to-noise ratio, and acoustic shadowing, which make interpretation difficult. Existing deep learning models for bone segmentation rely primarily on costly manual labeling by experts, limiting dataset size and model generalizability. Additionally, the complexity of ultrasound physics and acoustic shadow makes the images difficult for hu…

This https://arxiv.org/abs/2202.13250 has been replaced.
link: https://scholar.google.com/scholar?q=a

TabID: Automatic Identification and Tabulation of Subproblems in Constraint Models
The performance of a constraint model can often be improved by converting a subproblem into a single table constraint (referred to as tabulation). Finding subproblems to tabulate is traditionally a manual and time-intensive process, even for expert modellers. This paper presents TabID, an entirely automated method to identify promising subproblems for tabulation in constraint programming. We introduce a diverse set of heuristics designed to identify promising candidates for tabulation, aiming t…

This https://arxiv.org/abs/2411.05185 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

PentestAgent: Incorporating LLM Agents to Automated Penetration Testing
Penetration testing is a critical technique for identifying security vulnerabilities, traditionally performed manually by skilled security specialists. This complex process involves gathering information about the target system, identifying entry points, exploiting the system, and reporting findings. Despite its effectiveness, manual penetration testing is time-consuming and expensive, often requiring significant expertise and resources that many organizations cannot afford. While automated pen…

This https://arxiv.org/abs/2505.23419 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

SWE-bench Goes Live!
The issue-resolving task, where a model generates patches to fix real-world bugs, has emerged as a critical benchmark for evaluating the capabilities of large language models (LLMs). While SWE-bench and its variants have become standard in this domain, they suffer from key limitations: they have not been updated since their initial releases, cover a narrow set of repositories, and depend heavily on manual effort for instance construction and environment setup. These factors hinder scalability a…

Tournament of Prompts: Evolving LLM Instructions Through Structured Debates and Elo Ratings
Anirudh Nair, Adi Banerjee, Laurent Mombaerts, Matthew Hagen, Tarik Borogovac
https://arxiv.org/abs/2506.00178

Tournament of Prompts: Evolving LLM Instructions Through Structured Debates and Elo Ratings
Prompt engineering represents a critical bottleneck to harness the full potential of Large Language Models (LLMs) for solving complex tasks, as it requires specialized expertise, significant trial-and-error, and manual intervention. This challenge is particularly pronounced for tasks involving subjective quality assessment, where defining explicit optimization objectives becomes fundamentally problematic. Existing automated prompt optimization methods falter in these scenarios, as they typicall…

PromptCanvas: Composable Prompting Workspaces Using Dynamic Widgets for Exploration and Iteration in Creative Writing
Rifat Mehreen Amin, Oliver Hans K\"uhle, Daniel Buschek, Andreas Butz
https://arxiv.org/abs/2506.03741

PromptCanvas: Composable Prompting Workspaces Using Dynamic Widgets for Exploration and Iteration in Creative Writing
We introduce PromptCanvas, a concept that transforms prompting into a composable, widget-based experience on an infinite canvas. Users can generate, customize, and arrange interactive widgets representing various facets of their text, offering greater control over AI-generated content. PromptCanvas allows widget creation through system suggestions, user prompts, or manual input, providing a flexible environment tailored to individual needs. This enables deeper engagement with the creative proce…

Critical Anatomy-Preserving & Terrain-Augmenting Navigation (CAPTAiN): Application to Laminectomy Surgical Education
Jonathan Wang, Hisashi Ishida, David Usevitch, Kesavan Venkatesh, Yi Wang, Mehran Armand, Rachel Bronheim, Amit Jain, Adnan Munawar
https://arxiv.org/abs/2506.20496

Critical Anatomy-Preserving & Terrain-Augmenting Navigation (CAPTAiN): Application to Laminectomy Surgical Education
Surgical training remains a crucial milestone in modern medicine, with procedures such as laminectomy exemplifying the high risks involved. Laminectomy drilling requires precise manual control to mill bony tissue while preserving spinal segment integrity and avoiding breaches in the dura: the protective membrane surrounding the spinal cord. Despite unintended tears occurring in up to 11.3% of cases, no assistive tools are currently utilized to reduce this risk. Variability in patient anatomy fu…

Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI
Xinliu Zhong, Ruiying Liu, Emily S. Nichols, Xuzhe Zhang, Andrew F. Laine, Emma G. Duerden, Yun Wang
https://arxiv.org/abs/2505.24739

Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI
Accurate placental segmentation is essential for quantitative analysis of the placenta. However, this task is particularly challenging in T2*-weighted placental imaging due to: (1) weak and inconsistent boundary contrast across individual echoes; (2) the absence of manual ground truth annotations for all echo times; and (3) motion artifacts across echoes caused by fetal and maternal movement. In this work, we propose a contrast-augmented segmentation framework that leverages complementary infor…

This https://arxiv.org/abs/2503.19449 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations
Auto-vectorization is a fundamental optimization for modern compilers to exploit SIMD parallelism. However, state-of-the-art approaches still struggle to handle intricate code patterns, often requiring manual hints or domain-specific expertise. Large language models (LLMs), with their ability to capture intricate patterns, provide a promising solution, yet their effective application in compiler optimizations remains an open challenge due to issues such as hallucinations and a lack of domain-sp…

Large Language Model-Driven Code Compliance Checking in Building Information Modeling
Soumya Madireddy, Lu Gao, Zia Din, Kinam Kim, Ahmed Senouci, Zhe Han, Yunpeng Zhang
https://arxiv.org/abs/2506.20551

Large Language Model-Driven Code Compliance Checking in Building Information Modeling
This research addresses the time-consuming and error-prone nature of manual code compliance checking in Building Information Modeling (BIM) by introducing a Large Language Model (LLM)-driven approach to semi-automate this critical process. The developed system integrates LLMs such as GPT, Claude, Gemini, and Llama, with Revit software to interpret building codes, generate Python scripts, and perform semi-automated compliance checks within the BIM environment. Case studies on a single-family res…

This https://arxiv.org/abs/2409.18745 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

A study on the effects of mixed explicit and implicit communications in human-virtual-agent interactions
Communication between humans and robots (or virtual agents) is essential for interaction and often inspired by human communication, which uses gestures, facial expressions, gaze direction, and other explicit and implicit means. This work presents an interaction experiment where humans and virtual agents interact through explicit (gestures, manual entries using mouse and keyboard, voice, sound, and information on screen) and implicit (gaze direction, location, facial expressions, and raise of ey…

SWE-bench Goes Live!
Linghao Zhang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Chengxing Xie, Junhao Wang, Maoquan Wang, Yufan Huang, Shengyu Fu, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang
https://arxiv.org/abs/2505.23419

SWE-bench Goes Live!
The issue-resolving task, where a model generates patches to fix real-world bugs, has emerged as a critical benchmark for evaluating the capabilities of large language models (LLMs). While SWE-bench and its variants have become standard in this domain, they suffer from key limitations: they have not been updated since their initial releases, cover a narrow set of repositories, and depend heavily on manual effort for instance construction and environment setup. These factors hinder scalability a…

SoK: Automated Vulnerability Repair: Methods, Tools, and Assessments
Yiwei Hu, Zhen Li, Kedie Shu, Shenghua Guan, Deqing Zou, Shouhuai Xu, Bin Yuan, Hai Jin
https://arxiv.org/abs/2506.11697

SoK: Automated Vulnerability Repair: Methods, Tools, and Assessments
The increasing complexity of software has led to the steady growth of vulnerabilities. Vulnerability repair investigates how to fix software vulnerabilities. Manual vulnerability repair is labor-intensive and time-consuming because it relies on human experts, highlighting the importance of Automated Vulnerability Repair (AVR). In this SoK, we present the systematization of AVR methods through the three steps of AVR workflow: vulnerability analysis, patch generation, and patch validation. We ass…

This https://arxiv.org/abs/2503.19449 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations
Auto-vectorization is a fundamental optimization for modern compilers to exploit SIMD parallelism. However, state-of-the-art approaches still struggle to handle intricate code patterns, often requiring manual hints or domain-specific expertise. Large language models (LLMs), with their ability to capture intricate patterns, provide a promising solution, yet their effective application in compiler optimizations remains an open challenge due to issues such as hallucinations and a lack of domain-sp…