Tootfinder

RefModel: Detecting Refactorings using Foundation Models
Pedro Sim\~oes, Rohit Gheyi, Rian Melo, Jonhnanthan Oliveira, M\'arcio Ribeiro, Wesley K. G. Assun\c{c}\~ao
https://arxiv.org/abs/2507.11346

RefModel: Detecting Refactorings using Foundation Models
Refactoring is a common software engineering practice that improves code quality without altering program behavior. Although tools like ReExtractor+, RefactoringMiner, and RefDiff have been developed to detect refactorings automatically, they rely on complex rule definitions and static analysis, making them difficult to extend and generalize to other programming languages. In this paper, we investigate the viability of using foundation models for refactoring detection, implemented in a tool nam…

KELPS: A Framework for Verified Multi-Language Autoformalization via Semantic-Syntactic Alignment
Jiyao Zhang, Chengli Zhong, Hui Xu, Qige Li, Yi Zhou
https://arxiv.org/abs/2507.08665

KELPS: A Framework for Verified Multi-Language Autoformalization via Semantic-Syntactic Alignment
Modern large language models (LLMs) show promising progress in formalizing informal mathematics into machine-verifiable theorems. However, these methods still face bottlenecks due to the limited quantity and quality of multilingual parallel corpora. In this paper, we propose a novel neuro-symbolic framework KELPS (Knowledge-Equation based Logical Processing System) to address these problems. KELPS is an iterative framework for translating, synthesizing, and filtering informal data into multiple…

Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models
Rihui Jin, Zheyu Xin, Xing Xie, Zuoyi Li, Guilin Qi, Yongrui Chen, Xinbang Dai, Tongtong Wu, Gholamreza Haffari
https://arxiv.org/abs/2506.06137

Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models
Table reasoning (TR) requires structured reasoning over semi-structured tabular data and remains challenging, particularly for small language models (SLMs, e.g., LLaMA-8B) due to their limited capacity compared to large LMs (LLMs, e.g., GPT-4o). To narrow this gap, we explore program-based TR (P-TR), which circumvents key limitations of text-based TR (T-TR), notably in numerical reasoning, by generating executable programs. However, applying P-TR to SLMs introduces two challenges: (i) vulnerabi…

Designing DSIC Mechanisms for Data Sharing in the Era of Large Language Models
Seyed Moein Ayyoubzadeh, Kourosh Shahnazari, Mohammmadali Keshtparvar, MohammadAmin Fazli
https://arxiv.org/abs/2506.05379

Designing DSIC Mechanisms for Data Sharing in the Era of Large Language Models
Training large language models (LLMs) requires vast amounts of high-quality data from institutions that face legal, privacy, and strategic constraints. Existing data procurement methods often rely on unverifiable trust or ignore heterogeneous provider costs. We introduce a mechanism-design framework for truthful, trust-minimized data sharing that ensures dominant-strategy incentive compatibility (DSIC), individual rationality, and weak budget balance, while rewarding data based on both quality …

TTrace: Lightweight Error Checking and Diagnosis for Distributed Training
Haitian Jiang, Shaowei Zhu, Zhen Zhang, Zhenyu Song, Xinwei Fu, Zhen Jia, Yida Wang, Jinyang Li
https://arxiv.org/abs/2506.09280

TTrace: Lightweight Error Checking and Diagnosis for Distributed Training
Distributed training is essential for scaling the training of large neural network models, such as large language models (LLMs), across thousands of GPUs. However, the complexity of distributed training programs makes them particularly prone to silent bugs, which do not produce explicit error signal but lead to incorrect training outcome. Effectively detecting and localizing such silent bugs in distributed training is challenging. Common debugging practice using metrics like training loss or gr…

This https://arxiv.org/abs/2406.06095 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMS_…

An extension of C++ with memory-centric specifications for HPC to reduce memory footprints and streamline MPI development
The C++ programming language and its cousins lean towards a memory-inefficient storage of structs: The compiler inserts helper bits such that individual instance variables fit to byte or cache boundaries, while it is not able to exploit knowledge about the range of integers, enums or bitsets. Furthermore, the language provides neither support for data exchange via MPI nor for arbitrary floating-point precisions. We propose C++ attributes through which developers can guide the compiler what memo…

Beyond Simple Edits: X-Planner for Complex Instruction-Based Image Editing
Chun-Hsiao Yeh, Yilin Wang, Nanxuan Zhao, Richard Zhang, Yuheng Li, Yi Ma, Krishna Kumar Singh
https://arxiv.org/abs/2507.05259

Beyond Simple Edits: X-Planner for Complex Instruction-Based Image Editing
Recent diffusion-based image editing methods have significantly advanced text-guided tasks but often struggle to interpret complex, indirect instructions. Moreover, current models frequently suffer from poor identity preservation, unintended edits, or rely heavily on manual masks. To address these challenges, we introduce X-Planner, a Multimodal Large Language Model (MLLM)-based planning system that effectively bridges user intent with editing model capabilities. X-Planner employs chain-of-thou…

This https://arxiv.org/abs/2412.01753 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Human-Machine Interfaces for Subsea Telerobotics: From Soda-straw to Natural Language Interactions
This review explores the evolution of human-machine interfaces (HMIs) in subsea telerobotics, charting the progression from traditional first-person "soda-straw" consoles -- characterized by narrow field-of-view camera feeds -- to contemporary interfaces leveraging gesture recognition, virtual reality, and natural language processing. We systematically analyze the state-of-the-art literature through three interrelated perspectives: operator experience (including immersive feedback, cognitive wo…

Beyond C/C : Probabilistic and LLM Methods for Next-Generation Software Reverse Engineering
Zhuo Zhuo, Xiangyu Zhang
https://arxiv.org/abs/2506.03504 http…

Beyond C/C++: Probabilistic and LLM Methods for Next-Generation Software Reverse Engineering
This proposal discusses the growing challenges in reverse engineering modern software binaries, particularly those compiled from newer system programming languages such as Rust, Go, and Mojo. Traditional reverse engineering techniques, developed with a focus on C and C++, fall short when applied to these newer languages due to their reliance on outdated heuristics and failure to fully utilize the rich semantic information embedded in binary programs. These challenges are exacerbated by the limi…

DesignBench: A Comprehensive Benchmark for MLLM-based Front-end Code Generation
Jingyu Xiao, Ming Wang, Man Ho Lam, Yuxuan Wan, Junliang Liu, Yintong Huo, Michael R. Lyu
https://arxiv.org/abs/2506.06251

DesignBench: A Comprehensive Benchmark for MLLM-based Front-end Code Generation
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in automated front-end engineering, e.g., generating UI code from visual designs. However, existing front-end UI code generation benchmarks have the following limitations: (1) While framework-based development becomes predominant in modern front-end programming, current benchmarks fail to incorporate mainstream development frameworks. (2) Existing evaluations focus solely on the UI code generation task, whereas p…