Tootfinder

A Two-Staged LLM-Based Framework for CI/CD Failure Detection and Remediation with Industrial Validation
Weiyuan Xu, Juntao Luo, Tao Huang, Kaixin Sui, Jie Geng, Qijun Ma, Isami Akasaka, Xiaoxue Shi, Jing Tang, Peng Cai
https://arxiv.org/abs/2506.03691

A Two-Staged LLM-Based Framework for CI/CD Failure Detection and Remediation with Industrial Validation
Continuous Integration and Continuous Deployment (CI/CD) pipelines are pivotal to modern software engineering, yet diagnosing and resolving their failures remains a complex and labor-intensive challenge. In this paper, we present LogSage, the first end-to-end LLM-powered framework that performs root cause analysis and solution generation from failed CI/CD pipeline logs. During the root cause analysis stage, LogSage employs a specialized log preprocessing pipeline tailored for LLMs, which extrac…

Adjusting Tissue Puncture Omnidirectionally In Situ with Pneumatic Rotatable Biopsy Mechanism and Hierarchical Airflow Management in Tortuous Luminal Pathways
Botao Lin, Tinghua Zhang, Sishen Yuan, Tiantian Wang, Jiaole Wang, Wu Yuan, Hongliang Ren
https://arxiv.org/abs/2506.03017

Adjusting Tissue Puncture Omnidirectionally In Situ with Pneumatic Rotatable Biopsy Mechanism and Hierarchical Airflow Management in Tortuous Luminal Pathways
In situ tissue biopsy with an endoluminal catheter is an efficient approach for disease diagnosis, featuring low invasiveness and few complications. However, the endoluminal catheter struggles to adjust the biopsy direction by distal endoscope bending or proximal twisting for tissue sampling within the tortuous luminal organs, due to friction-induced hysteresis and narrow spaces. Here, we propose a pneumatically-driven robotic catheter enabling the adjustment of the sampling direction without t…

This https://arxiv.org/abs/2505.23718 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCC_…

Fast Compressed-Domain N-Point Discrete Fourier Transform
This paper presents a novel algorithm for computing the N-point Discrete Fourier Transform (DFT) based solely on recursive Rectangular Index Compression (RIC) [1][2] and structured frequency shifts. The RIC DFT algorithm compresses a signal from $N=CL$ to $C\in[2,N/2]$ points at the expense of $N-1$ complex additions and no complex multiplication. It is shown that a $C$-point DFT on the compressed signal corresponds exactly to $C$ DFT coefficients of the original $N$-point DFT, namely, $X_{kL}$…

Search for millicharged particles in proton-proton collisions at $\sqrt{s} = 13.6$ TeV
S. Alcott, Z. Bhatti, J. Brooke, C. Campagnari, M. Carrigan, M. Citron, R. De Los Santos, A. De Roeck, C. Dorofeev, T. Du, J. Goldstein, F. Golf, N. Gonzalez, A. Haas, J. Heymann, C. S. Hill, D. Imani, M. Joyce, K. Larina, R. Loos, S. Lowette, H. Mei, D. W. Miller, B. Peng, S. N. Santpu, I. Reed, E. Schaffer, R. Schmitz, J. Steenis, D. Stuart, J. S. Tafoya Vargas, D. Vannerom, T. Wybouw, Z. Xiao, H. …

Search for millicharged particles in proton-proton collisions at $\sqrt{s} = 13.6$ TeV
We report on a search for elementary particles with charges much smaller than the electron charge using a data sample of proton-proton collisions provided by the CERN Large Hadron Collider in 2023--24, corresponding to an integrated luminosity of 124.7~fb$^{-1}$ at a center-of-mass energy of 13.6~TeV. The analysis presented uses the completed Run 3 milliQan bar detector to set the most stringent constraints to date for particles with charges $\leq0.24~\rm{e}$ and masses $\geq0.45~\rm{GeV}$.

This https://arxiv.org/abs/2505.19901 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Dynamic-I2V: Exploring Image-to-Video Generaion Models via Multimodal LLM
Recent advancements in image-to-video (I2V) generation have shown promising performance in conventional scenarios. However, these methods still encounter significant challenges when dealing with complex scenes that require a deep understanding of nuanced motion and intricate object-action relationships. To address these challenges, we present Dynamic-I2V, an innovative framework that integrates Multimodal Large Language Models (MLLMs) to jointly encode visual and textual conditions for a diffus…

This https://arxiv.org/abs/2505.23397 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

A Unified Framework for Human AI Collaboration in Security Operations Centers with Trusted Autonomy
This article presents a structured framework for Human-AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, and Human-in-the-loop decision making. Existing frameworks in SOCs often focus narrowly on automation, lacking systematic structures to manage human oversight, trust calibration, and scalable autonomy with AI. Many assume static or binary autonomy settings, failing to account for the varied complexity, criticality, and risk across SOC tasks c…

This https://arxiv.org/abs/2505.19755 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

One Model to Rank Them All: Unifying Online Advertising with End-to-End Learning
Modern industrial advertising systems commonly employ Multi-stage Cascading Architectures (MCA) to balance computational efficiency with ranking accuracy. However, this approach presents two fundamental challenges: (1) performance inconsistencies arising from divergent optimization targets and capability differences between stages, and (2) failure to account for advertisement externalities - the complex interactions between candidate ads during ranking. These limitations ultimately compromise s…

This https://arxiv.org/abs/2309.14546 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Fall Prediction for Bipedal Robots: The Standing Phase
This paper presents a novel approach to fall prediction for bipedal robots, specifically targeting the detection of potential falls while standing caused by abrupt, incipient, and intermittent faults. Leveraging a 1D convolutional neural network (CNN), our method aims to maximize lead time for fall prediction while minimizing false positive rates. The proposed algorithm uniquely integrates the detection of various fault types and estimates the lead time for potential falls. Our contributions in…

The Impact of Software Testing with Quantum Optimization Meets Machine Learning
Gopichand Bandarupalli
https://arxiv.org/abs/2506.02090 https://

The Impact of Software Testing with Quantum Optimization Meets Machine Learning
Modern software systems complexity challenges efficient testing, as traditional machine learning (ML) struggles with large test suites. This research presents a hybrid framework integrating Quantum Annealing with ML to optimize test case prioritization in CI/CD pipelines. Leveraging quantum optimization, it achieves a 25 percent increase in defect detection efficiency and a 30 percent reduction in test execution time versus classical ML, validated on the Defects4J dataset. A simulated CI/CD env…

Ten Simple Rules for Catalyzing Collaborations and Building Bridges between Research Software Engineers and Software Engineering Researchers
Nasir U. Eisty, Jeffrey C. Carver, Johanna Cohoon, Ian A. Cosden, Carole Goble, Samuel Grayson
https://arxiv.org/abs/2506.03012

Ten Simple Rules for Catalyzing Collaborations and Building Bridges between Research Software Engineers and Software Engineering Researchers
In the evolving landscape of scientific and scholarly research, effective collaboration between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) is pivotal for advancing innovation and ensuring the integrity of computational methodologies. This paper presents ten strategic guidelines aimed at fostering productive partnerships between these two distinct yet complementary communities. The guidelines emphasize the importance of recognizing and respecting the cultural …