Tootfinder

Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models
Mingjie Chen (Zhejiang University), Tiancheng Zhu (Huazhong University of Science,Technology), Mingxue Zhang (The State Key Laboratory of Blockchain,Data Security, Zhejiang University,Hangzhou High-Tech Zone), Yiling He (University College London), Minghao Lin (University of Southern California), Penghui Li (Columbia University), Kui Ren (The State Key Laboratory of Blockchain,Data Security, Z…

Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models
Binary code similarity detection (BCSD) serves as a fundamental technique for various software engineering tasks, e.g., vulnerability detection and classification. Attacks against such models have therefore drawn extensive attention, aiming at misleading the models to generate erroneous predictions. Prior works have explored various approaches to generating semantic-preserving variants, i.e., adversarial samples, to evaluate the robustness of the models against adversarial attacks. However, the…

This https://arxiv.org/abs/2408.09568 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair
Large Language Models (LLMs) have shown high capabilities in several software development-related tasks such as program repair, documentation, code refactoring, debugging, and testing. However, training these models requires massive amount of data and significant computational resources. Adapters are specialized, small modules designed for parameter efficient fine-tuning of LLMs for specific tasks, domains, or applications without requiring extensive retraining of the entire model. These adapte…

MatBYIB: A Matlab-based code for Bayesian inference of extreme mass-ratio inspiral binary with arbitrary eccentricity
Gen-Liang Li, Shu-Jie Zhao, Huai-Ke Guo, Jing-Yu Su, Zhen-Heng Lin
https://arxiv.org/abs/2506.05954

MatBYIB: A Matlab-based code for Bayesian inference of extreme mass-ratio inspiral binary with arbitrary eccentricity
Accurate parameter estimation(PE) of gravitational waves(GW) is essential for GW data analysis. In extreme mass-ratio inspiral binary(EMRI) systems, orbital eccentricity is a critical parameter for PE. However, current software for for PE of GW often neglects the direct estimation of orbital eccentricity. To fill this gap, we have developed the MatBYIB, a MATLAB-based software package for PE of GW with arbitrary eccentricity. The MatBYIB employs the Analytical Kludge (AK) waveform as a computat…

hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation
Charles Hong, Brendan Roberts, Huijae An, Alex Um, Advay Ratan, Yakun Sophia Shao
https://arxiv.org/abs/2506.04544

hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation
Large language models (LLMs) are playing an increasingly large role in domains such as code generation, including hardware code generation, where Verilog is the key language. However, the amount of publicly available Verilog code pales in comparison to the amount of code available for software languages like Python. In this work, we present hdl2v ("HDL-to-Verilog"), a dataset which seeks to increase the amount of available human-written Verilog data by translating or compiling three other hardw…

SLURM Heterogeneous Jobs for Hybrid Classical-Quantum Workflows
Aniello Esposito, Utz-Uwe Haus
https://arxiv.org/abs/2506.03846 https://

SLURM Heterogeneous Jobs for Hybrid Classical-Quantum Workflows
A method for efficient scheduling of hybrid classical-quantum workflows is presented, based on standard tools available on common supercomputer systems. Moderate interventions by the user are required, such as splitting a monolithic workflow in to basic building blocks and ensuring the data flow. This bares the potential to significantly reduce idle time of the quantum resource as well as overall wall time of co-scheduled workflows. Relevant pseudo-code samples and scripts are provided to demon…

Improving Code Switching with Supervised Fine Tuning and GELU Adapters
Linh Pham
https://arxiv.org/abs/2506.00291 https://arxiv.org/p…

Improving Code Switching with Supervised Fine Tuning and GELU Adapters
There are few code switching datasets, labeled or unlabled, that exist today. As a result, ASR requires new methods to utilize the vast monolingual data and models that exist. This paper uses OpenAI's open source ASR model, Whisper, which has been pre-trained on 680K hours of audio to perform monolingual ASR tasks. In Part 1, this paper examines how exploiting Whisper's monolingual ability to individually tokenize training text, called "Switching Tokenizers Method", improves transcription accur…

MGS3: A Multi-Granularity Self-Supervised Code Search Framework
Rui Li, Junfeng Kang, Qi Liu, Liyang He, Zheng Zhang, Yunhao Sha, Linbo Zhu, Zhenya Huang
https://arxiv.org/abs/2505.24274

MGS3: A Multi-Granularity Self-Supervised Code Search Framework
In the pursuit of enhancing software reusability and developer productivity, code search has emerged as a key area, aimed at retrieving code snippets relevant to functionalities based on natural language queries. Despite significant progress in self-supervised code pre-training utilizing the vast amount of code data in repositories, existing methods have primarily focused on leveraging contrastive learning to align natural language with function-level code snippets. These studies have overlooke…

Searching Clinical Data Using Generative AI
Karan Hanswadkar, Anika Kanchi, Shivani Tripathi, Shi Qiao, Rony Chatterjee, Alekh Jindal
https://arxiv.org/abs/2505.24090

Searching Clinical Data Using Generative AI
Artificial Intelligence (AI) is making a major impact on healthcare, particularly through its application in natural language processing (NLP) and predictive analytics. The healthcare sector has increasingly adopted AI for tasks such as clinical data analysis and medical code assignment. However, searching for clinical information in large and often unorganized datasets remains a manual and error-prone process. Assisting this process with automations can help physicians improve their operationa…

Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Fangyu Lei, Jinxiang Meng, Yiming Huang, Tinghong Chen, Yun Zhang, Shizhu He, Jun Zhao, Kang Liu
https://arxiv.org/abs/2506.01710

Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Table reasoning, encompassing tasks such as table question answering, fact verification, and text-to-SQL, requires precise understanding of structured tabular data, coupled with numerical computation and code manipulation for effective inference. Supervised fine-tuning (SFT) approaches have achieved notable success but often struggle with generalization and robustness due to biases inherent in imitative learning. We introduce Reasoning-Table, the first application of reinforcement learning (RL)…

This https://arxiv.org/abs/2502.16517 has been replaced.
link: https://scholar.google.com/scholar?q=a

Annotation-guided AoS-to-SoA conversions and GPU offloading with data views in C++
The C++ programming language provides classes and structs as fundamental modeling entities. Consequently, C++ code tends to favour array-of-structs (AoS) for encoding data sequences, even though structure-of-arrays (SoA) yields better performance for some calculations. We propose a C++ language extension based on attributes that allows developers to guide the compiler in selecting memory arrangements, i.e.~to select the optimal choice between AoS and SoA dynamically depending on both the execut…

eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems
Ruilin Xu, Zongxuan Xie, Pengfei Chen
https://arxiv.org/abs/2506.02007

eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems
We present eACGM, a full-stack AI/ML system monitoring framework based on eBPF. eACGM collects real-time performance data from key hardware components, including the GPU and network communication layer, as well as from key software stacks such as CUDA, Python, and PyTorch, all without requiring any code instrumentation or modifications. Additionally, it leverages libnvml to gather process-level GPU resource usage information. By applying a Gaussian Mixture Model (GMM) to the collected multidime…

This https://arxiv.org/abs/2408.16028 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data
Supervised-learning-based vulnerability detectors often fall short due to limited labelled training data. In contrast, Large Language Models (LLMs) like GPT-4 are trained on vast unlabelled code corpora, yet perform only marginally better than coin flips when directly prompted to detect vulnerabilities. In this paper, we reframe vulnerability detection as anomaly detection, based on the premise that vulnerable code is rare and thus anomalous relative to patterns learned by LLMs. We introduce AN…

This https://arxiv.org/abs/2405.15927 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Application based Evaluation of an Efficient Spike-Encoder, "Spiketrum"
Spike-based encoders represent information as sequences of spikes or pulses, which are transmitted between neurons. A prevailing consensus suggests that spike-based approaches demonstrate exceptional capabilities in capturing the temporal dynamics of neural activity and have the potential to provide energy-efficient solutions for low-power applications. The Spiketrum encoder efficiently compresses input data using spike trains or code sets (for non-spiking applications) and is adaptable to both…

LLMs are now part of our daily work, making coding easier. Join Ivan Dolgov at this year's Berlin Buzzwords to learn how they built an in-house LLM for AI code completion in JetBrains products, covering design choices, data preparation, training and model evaluation.
Learn more: https://

This https://arxiv.org/abs/2505.19713 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric Reward
In this work, we introduce CAD-Coder, a novel framework that reformulates text-to-CAD as the generation of CadQuery scripts - a Python-based, parametric CAD language. This representation enables direct geometric validation, a richer modeling vocabulary, and seamless integration with existing LLMs. To further enhance code validity and geometric fidelity, we propose a two-stage learning pipeline: (1) supervised fine-tuning on paired text-CadQuery data, and (2) reinforcement learning with Group Re…

This morning I null routed another dozen IP addresses for scraping my personal git server using repeated http requests. As per usual, a quick inspection reveals that at least some of them are scraping for LLM data. As always, I have not consented to this use of my non-maintained code, experiments, college coursework, and miscellaneous crap that I for whatever reason decided to self host rather than pushing it to Codeberg.
I mean, if you really want to feed your LLM on a diet that inclu…

Towards Industrial Convergence : Understanding the evolution of scientific norms and practices in the field of AI
Antoine Houssard
https://arxiv.org/abs/2505.17945

Towards Industrial Convergence : Understanding the evolution of scientific norms and practices in the field of AI
In the field of artificial intelligence (AI) research, there seems to be a rapprochement between academics and industrial forces. The aim of this study is to assess whether and to what extent industrial domination in the field as well as the ever more frequent switch between academia and industry resulted in the adoption of industrial norms and practices by academics. Using bibliometric information and data on scientific code, we aimed to understand academic and industrial researchers' practice…

Probing Audio-Generation Capabilities of Text-Based Language Models
Arjun Prasaath Anbazhagan, Parteek Kumar, Ujjwal Kaur, Aslihan Akalin, Kevin Zhu, Sean O'Brien
https://arxiv.org/abs/2506.00003

Probing Audio-Generation Capabilities of Text-Based Language Models
How does textual representation of audio relate to the Large Language Model's (LLMs) learning about the audio world? This research investigates the extent to which LLMs can be prompted to generate audio, despite their primary training in textual data. We employ a three-tier approach, progressively increasing the complexity of audio generation: 1) Musical Notes, 2) Environmental Sounds, and 3) Human Speech. To bridge the gap between text and audio, we leverage code as an intermediary, prompting …

MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning
Yong-Cheng Liaw, Shuo-Han Chen
https://arxiv.org/abs/2505.23254 https://

MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning
Owing to the huge success of generative artificial intelligence (AI), large language models (LLMs) have emerged as a core subclass, underpinning applications such as question answering, text generation, and code completion. While fine-tuning these models on domain-specific data can yield significant performance gains, it also poses daunting computational challenges, especially for researchers and small organizations with limited hardware resources. Although SSD offloading (i.e., ZeRO-Infinity) …

Done mapping all 10 #barangays of Hadji Muhtamad, Basilan, #Philippines 🇵🇭 in #OpenStreetMap, creating their #Wikidata

OmniGenBench: A Modular Platform for Reproducible Genomic Foundation Models Benchmarking
Heng Yang, Jack Cole, Yuan Li, Renzhi Chen, Geyong Min, Ke Li
https://arxiv.org/abs/2505.14402

OmniGenBench: A Modular Platform for Reproducible Genomic Foundation Models Benchmarking
The code of nature, embedded in DNA and RNA genomes since the origin of life, holds immense potential to impact both humans and ecosystems through genome modeling. Genomic Foundation Models (GFMs) have emerged as a transformative approach to decoding the genome. As GFMs scale up and reshape the landscape of AI-driven genomics, the field faces an urgent need for rigorous and reproducible evaluation. We present OmniGenBench, a modular benchmarking platform designed to unify the data, model, bench…