OpenRaiser/Intern-Atlas huggingface dataset:
Large Language Models for Unit Testing: A Systematic Literature Review
Venue: arXiv cs.SE 2025
Year: 2025

Unit testing is a fundamental practice in modern software engineering, with the aim of ensuring the correctness, maintainability, and reliability of individual software components. Very recently, with the advances in Large Language Models (LLMs), a rapidly growing body of research has leveraged LLMs to automate various unit testing tasks, demonstrating remarkable performance and significantly reducing manual effort. However, due to ongoing explorations in the LLM-based unit testing field, it is challenging for researchers to understand existing achievements, open challenges, and future opportunities. This paper presents the first systematic literature review on the application of LLMs in unit testing until March 2025. We analyze \numpaper{} relevant papers from the perspectives of both unit testing and LLMs. We first categorize existing unit testing tasks that benefit from LLMs, e.g., test generation and oracle generation. We then discuss several critical aspects of integrating LLMs into unit testing research, including model usage, adaptation strategies, and hybrid approaches. We further summarize key challenges that remain unresolved and outline promising directions to guide future research in this area. Overall, our paper provides a systematic overview of the research landscape to the unit testing community, helping researchers gain a comprehensive understanding of achievements and promote future research. Our artifacts are publicly available at the GitHub repository: https://github.com/iSEngLab/AwesomeLLM4UT.
Research on Graph-Retrieval Augmented Generation Based on Historical Text Knowledge Graphs
Venue: arXiv cs.CL 2025
Year: 2025

This article addresses domain knowledge gaps in general large language models for historical text analysis in the context of computational humanities and AIGC technology. We propose the Graph RAG framework, combining chain-of-thought prompting, self-instruction generation, and process supervision to create a The First Four Histories character relationship dataset with minimal manual annotation. This dataset supports automated historical knowledge extraction, reducing labor costs. In the graph-augmented generation phase, we introduce a collaborative mechanism between knowledge graphs and retrieval-augmented generation, improving the alignment of general models with historical knowledge. Experiments show that the domain-specific model Xunzi-Qwen1.5-14B, with Simplified Chinese input and chain-of-thought prompting, achieves optimal performance in relation extraction (F1 = 0.68). The DeepSeek model integrated with GraphRAG improves F1 by 11% (0.08-0.19) on the open-domain C-CLUE relation extraction dataset, surpassing the F1 value of Xunzi-Qwen1.5-14B (0.12), effectively alleviating hallucinations phenomenon, and improving interpretability. This framework offers a low-resource solution for classical text knowledge extraction, advancing historical knowledge services and humanities research.
BCRNet: Enhancing Landmark Detection in Laparoscopic Liver Surgery via Bezier Curve Refinement
Venue: arXiv cs.CV 2025
Year: 2025

Laparoscopic liver surgery, while minimally invasive, poses significant challenges in accurately identifying critical anatomical structures. Augmented reality (AR) systems, integrating MRI/CT with laparoscopic images based on 2D-3D registration, offer a promising solution for enhancing surgical navigation. A vital aspect of the registration progress is the precise detection of curvilinear anatomical landmarks in laparoscopic images. In this paper, we propose BCRNet (Bezier Curve Refinement Net), a novel framework that significantly enhances landmark detection in laparoscopic liver surgery primarily via the Bezier curve refinement strategy. The framework starts with a Multi-modal Feature Extraction (MFE) module designed to robustly capture semantic features. Then we propose Adaptive Curve Proposal Initialization (ACPI) to generate pixel-aligned Bezier curves and confidence scores for reliable initial proposals. Additionally, we design the Hierarchical Curve Refinement (HCR) mechanism to enhance these proposals iteratively through a multi-stage process, capturing fine-grained contextual details from multi-scale pixel-level features for precise Bezier curve adjustment. Extensive evaluations on the L3D and P2ILF datasets demonstrate that BCRNet outperforms state-of-the-art methods, achieving significant performance improvements. Code will be available.
AI-driven visual monitoring of industrial assembly tasks
Venue: arXiv cs.CV 2025
Year: 2025

Visual monitoring of industrial assembly tasks is critical for preventing equipment damage due to procedural errors and ensuring worker safety. Although commercial solutions exist, they typically require rigid workspace setups or the application of visual markers to simplify the problem. We introduce ViMAT, a novel AI-driven system for real-time visual monitoring of assembly tasks that operates without these constraints. ViMAT combines a perception module that extracts visual observations from multi-view video streams with a reasoning module that infers the most likely action being performed based on the observed assembly state and prior task knowledge. We validate ViMAT on two assembly tasks, involving the replacement of LEGO components and the reconfiguration of hydraulic press molds, demonstrating its effectiveness through quantitative and qualitative analysis in challenging real-world scenarios characterized by partial and uncertain visual observations. Project page: https://tev-fbk.github.io/ViMAT
UXR Point of View on Product Feature Prioritization Prior To Multi-Million Engineering Commitments
Venue: arXiv cs.HC 2025
Year: 2025

This paper discusses a popular UX research activity, feature prioritization, using the User Experience Research Point of View (UXR PoV) Playbook framework. We describe an application of multinomial logistic regression, frequently marketed as MaxDiff, for prioritizing product features in consumer product development. It addresses challenges of traditional surveying techniques. We propose a solution using MaxDiff to generate a reliable preference list with a reasonable sample size. We also adapt the MaxDiff method to reduce the number of survey responses in half, making it less tedious from the survey takers' perspective. We present a case study using the adapted MaxDiff method for tablet feature prioritization research involving users with disabilities.
Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance
Venue: arXiv cs.LG 2025
Year: 2025

The rapid expansion of cross-border e-commerce (CBEC) has created significant opportunities for small- and medium-sized sellers, yet financing remains a critical challenge due to their limited credit histories. Third-party logistics (3PL)-led supply chain finance (SCF) has emerged as a promising solution, leveraging in-transit inventory as collateral. We propose an advanced credit risk management framework tailored for 3PL-led SCF, addressing the dual challenges of credit risk assessment and loan size determination. Specifically, we leverage conditional generative modeling of sales distributions through Quantile-Regression-based Generative Metamodeling (QRGMM) as the foundation for risk measures estimation. We propose a unified framework that enables flexible estimation of multiple risk measures while introducing a functional risk measure formulation that systematically captures the relationship between these risk measures and varying loan levels, supported by theoretical guarantees. To capture complex covariate interactions in e-commerce sales data, we integrate QRGMM with Deep Factorization Machines (DeepFM). Extensive experiments on synthetic and real-world data validate the efficacy of our model for credit risk assessment and loan size determination. This study explores the use of generative models in CBEC SCF risk management, illustrating their potential to strengthen credit assessment and support financing for small- and medium-sized sellers.
Case Study for Developing a UXR Point of View for FinOps Product Innovation
Venue: arXiv cs.HC 2025
Year: 2025

In the dynamic landscape of Cloud financial management, we are sharing a case study exploring the development of a User Experience Research (UXR) Point of View (PoV) to drive FinOps product innovation. We demonstrate how qualitative and quantitative research methods working together to navigate the challenges of understanding customer needs, aligning cross-functional teams, and prioritizing limited resources. Through a multi-phased research approach, the research team identifies opportunities, quantifies pain points, and segments diverse customer cohorts. This culminated in a UXR PoV that informed the creation of a differentiated product strategy, a 'one-stop shop' dashboard empowering FinOps practitioners with actionable insights and tools. This case study highlights the power of mixed-methods research in uncovering actionable insights that drive impactful product innovation.
COSMMIC: Comment-Sensitive Multimodal Multilingual Indian Corpus for Summarization and Headline Generation
Venue: arXiv cs.CL 2025
Year: 2025

Despite progress in comment-aware multimodal and multilingual summarization for English and Chinese, research in Indian languages remains limited. This study addresses this gap by introducing COSMMIC, a pioneering comment-sensitive multimodal, multilingual dataset featuring nine major Indian languages. COSMMIC comprises 4,959 article-image pairs and 24,484 reader comments, with ground-truth summaries available in all included languages. Our approach enhances summaries by integrating reader insights and feedback. We explore summarization and headline generation across four configurations: (1) using article text alone, (2) incorporating user comments, (3) utilizing images, and (4) combining text, comments, and images. To assess the dataset's effectiveness, we employ state-of-the-art language models such as LLama3 and GPT-4. We conduct a comprehensive study to evaluate different component combinations, including identifying supportive comments, filtering out noise using a dedicated comment classifier using IndicBERT, and extracting valuable insights from images with a multilingual CLIP-based classifier. This helps determine the most effective configurations for natural language generation (NLG) tasks. Unlike many existing datasets that are either text-only or lack user comments in multimodal settings, COSMMIC uniquely integrates text, images, and user feedback. This holistic approach bridges gaps in Indian language resources, advancing NLP research and fostering inclusivity.
Zero-Shot Reinforcement Learning Under Partial Observability
Venue: arXiv cs.LG 2025
Year: 2025

Recent work has shown that, under certain assumptions, zero-shot reinforcement learning (RL) methods can generalise to any unseen task in an environment after reward-free pre-training. Access to Markov states is one such assumption, yet, in many real-world applications, the Markov state is only partially observable. Here, we explore how the performance of standard zero-shot RL methods degrades when subjected to partially observability, and show that, as in single-task RL, memory-based architectures are an effective remedy. We evaluate our memory-based zero-shot RL methods in domains where the states, rewards and a change in dynamics are partially observed, and show improved performance over memory-free baselines. Our code is open-sourced via: https://enjeeneer.io/projects/bfms-with-memory/.
Warping and Matching Subsequences Between Time Series
Venue: arXiv cs.LG 2025
Year: 2025

Comparing time series is essential in various tasks such as clustering and classification. While elastic distance measures that allow warping provide a robust quantitative comparison, a qualitative comparison on top of them is missing. Traditional visualizations focus on point-to-point alignment and do not convey the broader structural relationships at the level of subsequences. This limitation makes it difficult to understand how and where one time series shifts, speeds up or slows down with respect to another. To address this, we propose a novel technique that simplifies the warping path to highlight, quantify and visualize key transformations (shift, compression, difference in amplitude). By offering a clearer representation of how subsequences match between time series, our method enhances interpretability in time series comparison.
Parallel Paradigms in Modern HPC: A Comparative Analysis of MPI, OpenMP, and CUDA
Venue: arXiv cs.DC 2025
Year: 2025

This paper presents a comprehensive comparison of three dominant parallel programming models in High Performance Computing (HPC): Message Passing Interface (MPI), Open Multi-Processing (OpenMP), and Compute Unified Device Architecture (CUDA). Selecting optimal programming approaches for modern heterogeneous HPC architectures has become increasingly critical. We systematically analyze these models across multiple dimensions: architectural foundations, performance characteristics, domain-specific suitability, programming complexity, and recent advancements. We examine each model's strengths, weaknesses, and optimization techniques. Our investigation demonstrates that MPI excels in distributed memory environments with near-linear scalability for communication-intensive applications, but faces communication overhead challenges. OpenMP provides strong performance and usability in shared-memory systems and loop-centric tasks, though it is limited by shared memory contention. CUDA offers substantial performance gains for data-parallel GPU workloads, but is restricted to NVIDIA GPUs and requires specialized expertise. Performance evaluations across scientific simulations, machine learning, and data analytics reveal that hybrid approaches combining two or more models often yield optimal results in heterogeneous environments. The paper also discusses implementation challenges, optimization best practices, and emerging trends such as performance portability frameworks, task-based programming, and the convergence of HPC and Big Data. This research helps developers and researchers make informed decisions when selecting programming models for modern HPC applications, emphasizing that the best choice depends on application requirements, hardware, and development constraints.
All is Not Lost: LLM Recovery without Checkpoints
Venue: arXiv cs.DC 2025
Year: 2025

Training LLMs on decentralized and wimpy computation nodes, e.g., multiple on-spot instances, lowers the training cost and enables model democratization. The inevitable challenge here is the churn of nodes due to failures and the operator's scheduling policies, leading to losing a stage - a part of the model. The conventional approaches to recover from failures are to either use checkpointing, where periodically a copy of the entire model is sent to an additional storage, or redundant computation. These approaches yield significant communication and/or computation overhead even in non-failure cases and scale poorly in settings with large models. In this paper, we propose, CheckFree, an efficient recovery method where a failing stage is substituted by a weighted average of the closest neighboring stages. In contrast to the state of the art, CheckFree requires no additional computation or storage. However, because of the nature of averaging neighbouring stages, it can only recover failures of intermediate stages. We further extend our method to CheckFree+ with out-of-order pipeline execution to tolerate crashes of the first and last stages. Thanks to out-of-order pipelining, behaviour of those stages is mimicked by their neighboring ones, which allows CheckFree+ to recover them by simply copying the weights from the immediate neighbour. To be able to recover the (de)embedding layers, CheckFree+ copies those layers to the neighboring stages, which requires relatively small storage overhead. We extensively evaluate our method on LLaMa models of model sizes from 124M to 1.5B with varying failure frequencies. In the case of low and medium failure rates (5-10%), CheckFree and CheckFree+ outperform both checkpointing and redundant computation in terms of convergence in wall-clock time by over 12%. Both of our proposals can be run via our code available at: https://github.com/gensyn-ai/CheckFree.
Towards Weight Distribution-Aware Polar Codes
Venue: arXiv cs.IT 2025
Year: 2025

Polar codes are constructed based on the reliability of sub-channels resulting from the polarization effect. However, this information-theoretic construction approach leads to a poor weight distribution. To address this issue, pre-transformed polar codes, such as CRC-polar codes and PAC codes, have been employed. In this paper, we focus on the structure of polar codes without applying any pre-transformations and explore methods, guided by the weight-contribution partial order, to design polar-like codes with enhanced weight distribution, notably without employing any search or optimization algorithms. Numerical results demonstrate improvement over a range of codes both with and without pre-transformation.
Creating User-steerable Projections with Interactive Semantic Mapping
Venue: arXiv cs.LG 2025
Year: 2025

Dimensionality reduction (DR) techniques map high-dimensional data into lower-dimensional spaces. Yet, current DR techniques are not designed to explore semantic structure that is not directly available in the form of variables or class labels. We introduce a novel user-guided projection framework for image and text data that enables customizable, interpretable, data visualizations via zero-shot classification with Multimodal Large Language Models (MLLMs). We enable users to steer projections dynamically via natural-language guiding prompts, to specify high-level semantic relationships of interest to the users which are not explicitly present in the data dimensions. We evaluate our method across several datasets and show that it not only enhances cluster separation, but also transforms DR into an interactive, user-driven process. Our approach bridges the gap between fully automated DR techniques and human-centered data exploration, offering a flexible and adaptive way to tailor projections to specific analytical needs.
Minimizing Communication for Parallel Symmetric Tensor Times Same Vector Computation
Venue: arXiv cs.DC 2025
Year: 2025

In this article, we focus on the parallel communication cost of multiplying the same vector along two modes of a $3$-dimensional symmetric tensor. This is a key computation in the higher-order power method for determining eigenpairs of a $3$-dimensional symmetric tensor and in gradient-based methods for computing a symmetric CP decomposition. We establish communication lower bounds that determine how much data movement is required to perform the specified computation in parallel. The core idea of the proof relies on extending a key geometric inequality for $3$-dimensional symmetric computations. We demonstrate that the communication lower bounds are tight by presenting an optimal algorithm where the data distribution is a natural extension of the triangle block partition scheme for symmetric matrices to 3-dimensional symmetric tensors.
Exploiting Music Source Separation for Automatic Lyrics Transcription with Whisper
Venue: arXiv cs.SD 2025
Year: 2025

Automatic lyrics transcription (ALT) remains a challenging task in the field of music information retrieval, despite great advances in automatic speech recognition (ASR) brought about by transformer-based architectures in recent years. One of the major challenges in ALT is the high amplitude of interfering audio signals relative to conventional ASR due to musical accompaniment. Recent advances in music source separation have enabled automatic extraction of high-quality separated vocals, which could potentially improve ALT performance. However, the effect of source separation has not been systematically investigated in order to establish best practices for its use. This work examines the impact of source separation on ALT using Whisper, a state-of-the-art open source ASR model. We evaluate Whisper's performance on original audio, separated vocals, and vocal stems across short-form and long-form transcription tasks. For short-form, we suggest a concatenation method that results in a consistent reduction in Word Error Rate (WER). For long-form, we propose an algorithm using source separation as a vocal activity detector to derive segment boundaries, which results in a consistent reduction in WER relative to Whisper's native long-form algorithm. Our approach achieves state-of-the-art results for an open source system on the Jam-ALT long-form ALT benchmark, without any training or fine-tuning. We also publish MUSDB-ALT, the first dataset of long-form lyric transcripts following the Jam-ALT guidelines for which vocal stems are publicly available.
Lessons from Training Grounded LLMs with Verifiable Rewards
Venue: arXiv cs.CL 2025
Year: 2025

Generating grounded and trustworthy responses remains a key challenge for large language models (LLMs). While retrieval-augmented generation (RAG) with citation-based grounding holds promise, instruction-tuned models frequently fail even in straightforward scenarios: missing explicitly stated answers, citing incorrectly, or refusing when evidence is available. In this work, we explore how reinforcement learning (RL) and internal reasoning can enhance grounding in LLMs. We use the GRPO (Group Relative Policy Optimization) method to train models using verifiable outcome-based rewards targeting answer correctness, citation sufficiency, and refusal quality, without requiring gold reasoning traces or expensive annotations. Through comprehensive experiments across ASQA, QAMPARI, ELI5, and ExpertQA we show that reasoning-augmented models significantly outperform instruction-only variants, especially in handling unanswerable queries and generating well-cited responses. A two-stage training setup, first optimizing answer and citation behavior and then refusal, further improves grounding by stabilizing the learning signal. Additionally, we revisit instruction tuning via GPT-4 distillation and find that combining it with GRPO enhances performance on long-form, generative QA tasks. Overall, our findings highlight the value of reasoning, stage-wise optimization, and outcome-driven RL for building more verifiable and reliable LLMs.
An efficient construction of Raz's two-source randomness extractor with improved parameters
Venue: arXiv cs.CR 2025
Year: 2025

Randomness extractors are algorithms that distill weak random sources into near-perfect random numbers. Two-source extractors enable this distillation process by combining two independent weak random sources. Raz's extractor (STOC '05) was the first to achieve this in a setting where one source has linear min-entropy (i.e., proportional to its length), while the other has only logarithmic min-entropy in its length. However, Raz's original construction is impractical due to a polynomial computation time of at least degree 4. Our work solves this problem by presenting an improved version of Raz's extractor with quasi-linear computation time, as well as a new analytic theorem with reduced entropy requirements. We provide comprehensive analytical and numerical comparisons of our construction with others in the literature, and we derive strong and quantum-proof versions of our efficient Raz extractor. Additionally, we offer an easy-to-use, open-source code implementation of the extractor and a numerical parameter calculation module.