OpenRaiser/Intern-Atlas huggingface dataset:
A Retrieval-Based Approach to Medical Procedure Matching in Romanian
Venue: arXiv cs.CL 2025
Year: 2025

Accurately mapping medical procedure names from healthcare providers to standardized terminology used by insurance companies is a crucial yet complex task. Inconsistencies in naming conventions lead to missclasified procedures, causing administrative inefficiencies and insurance claim problems in private healthcare settings. Many companies still use human resources for manual mapping, while there is a clear opportunity for automation. This paper proposes a retrieval-based architecture leveraging sentence embeddings for medical name matching in the Romanian healthcare system. This challenge is significantly more difficult in underrepresented languages such as Romanian, where existing pretrained language models lack domain-specific adaptation to medical text. We evaluate multiple embedding models, including Romanian, multilingual, and medical-domain-specific representations, to identify the most effective solution for this task. Our findings contribute to the broader field of medical NLP for low-resource languages such as Romanian.
Optimizing Case-Based Reasoning System for Functional Test Script Generation with Large Language Models
Venue: arXiv cs.SE 2025
Year: 2025

In this work, we explore the potential of large language models (LLMs) for generating functional test scripts, which necessitates understanding the dynamically evolving code structure of the target software. To achieve this, we propose a case-based reasoning (CBR) system utilizing a 4R cycle (i.e., retrieve, reuse, revise, and retain), which maintains and leverages a case bank of test intent descriptions and corresponding test scripts to facilitate LLMs for test script generation. To improve user experience further, we introduce Re4, an optimization method for the CBR system, comprising reranking-based retrieval finetuning and reinforced reuse finetuning. Specifically, we first identify positive examples with high semantic and script similarity, providing reliable pseudo-labels for finetuning the retriever model without costly labeling. Then, we apply supervised finetuning, followed by a reinforcement learning finetuning stage, to align LLMs with our production scenarios, ensuring the faithful reuse of retrieved cases. Extensive experimental results on two product development units from Huawei Datacom demonstrate the superiority of the proposed CBR+Re4. Notably, we also show that the proposed Re4 method can help alleviate the repetitive generation issues with LLMs.
Is Reuse All You Need? A Systematic Comparison of Regular Expression Composition Strategies
Venue: arXiv cs.SE 2025
Year: 2025

Composing regexes is a common but challenging engineering activity. Software engineers struggle with regex complexity, leading to defects, performance issues, and security vulnerabilities. Researchers have proposed tools to synthesize regexes automatically, and recent advances in LLMs have also shown promise in generating regexes. Meanwhile, developers commonly reuse existing regexes from codebases and internet sources. No work to date has compared these various regex composition strategies, leaving software engineers unaware about which to use and researchers uncertain about open problems. We address this gap through a systematic evaluation of regex reuse, formal synthesis, and LLM-based generation strategies. We curate a novel dataset of 901,516 regexes mined from open-source software projects and internet sources (RegexReuseDB), accompanied by a set of 55,448 regex composition tasks defined by a target regex and its corresponding positive and negative string pairs (RegexCompBench). To address the absence of an automated regex reuse formulation, we design and implement reuse-by-example, the first programming by example approach that leverages RegexReuseDB. Our evaluation then benchmarks reuse-by-example, formal synthesizers, and LLMs on many aspects of interest to software engineers, including accuracy, maintainability, computational efficiency, and result diversity. Although all three approaches solve most composition tasks accurately, only reuse-by-example and LLMs excel over the range of metrics we applied, and reuse-by-example in particular offers engineers the variance in candidates that they say they find helpful. Ceteris paribus, prefer the cheaper solution--for regex composition, perhaps reuse is all you need. Our findings provide insights for developers selecting regex composition strategies and inform the design of tools to improve regex reliability in software systems.
Feature Statistics with Uncertainty Help Adversarial Robustness
Venue: arXiv cs.LG 2025
Year: 2025

Despite the remarkable success of deep neural networks (DNNs), the security threat of adversarial attacks poses a significant challenge to the reliability of DNNs. In this paper, both theoretically and empirically, we discover a universal phenomenon that has been neglected in previous works, i.e., adversarial attacks tend to shift the distributions of feature statistics. Motivated by this finding, and by leveraging the advantages of uncertainty-aware stochastic methods in building robust models efficiently, we propose an uncertainty-driven feature statistics adjustment module for robustness enhancement, named Feature Statistics with Uncertainty (FSU). It randomly resamples channel-wise feature means and standard deviations of examples from multivariate Gaussian distributions, which helps to reconstruct the perturbed examples and calibrate the shifted distributions. The calibration recovers some domain characteristics of the data for classification, thereby mitigating the influence of perturbations and weakening the ability of attacks to deceive models. The proposed FSU module has universal applicability in training, attacking, predicting, and fine-tuning, demonstrating impressive robustness enhancement ability at a trivial additional time cost. For example, by fine-tuning the well-established models with FSU, the state-of-the-art methods achieve up to 17.13% and 34.82% robustness improvement against powerful AA and CW attacks on benchmark datasets.
Dual-Issue Execution of Mixed Integer and Floating-Point Workloads on Energy-Efficient In-Order RISC-V Cores
Venue: arXiv cs.AR 2025
Year: 2025

To meet the computational requirements of modern workloads under tight energy constraints, general-purpose accelerator architectures have to integrate an ever-increasing number of extremely area- and energy-efficient processing elements (PEs). In this context, single-issue in-order cores are commonplace, but lean dual-issue cores could boost PE IPC, especially for the common case of mixed integer and floating-point workloads. We develop the COPIFT methodology and RISC-V ISA extensions to enable low-cost and flexible dual-issue execution of mixed integer and floating-point instruction sequences. On such kernels, our methodology achieves speedups of 1.47x, reaching a peak 1.75 instructions per cycle, and 1.37x energy improvements on average, over optimized RV32G baselines.
NotebookOS: A Replicated Notebook Platform for Interactive Training with On-Demand GPUs
Venue: arXiv cs.DC 2025
Year: 2025

Interactive notebook programming is universal in modern ML and AI workflows, with interactive deep learning training (IDLT) emerging as a dominant use case. To ensure responsiveness, platforms like Jupyter and Colab reserve GPUs for long-running notebook sessions, despite their intermittent and sporadic GPU usage, leading to extremely low GPU utilization and prohibitively high costs. In this paper, we introduce NotebookOS, a GPU-efficient notebook platform tailored for the unique requirements of IDLT. NotebookOS employs replicated notebook kernels with Raft-synchronized replicas distributed across GPU servers. To optimize GPU utilization, NotebookOS oversubscribes server resources, leveraging high interarrival times in IDLT workloads, and allocates GPUs only during active cell execution. It also supports replica migration and automatic cluster scaling under high load. Altogether, this design enables interactive training with minimal delay. In evaluation on production workloads, NotebookOS saved over 1,187 GPU hours in 17.5 hours of real-world IDLT, while significantly improving interactivity.
AV-TLX for measuring (mental) workload while driving AVs: Born from NASA-TLX but developed for the era of automated vehicles
Venue: arXiv cs.HC 2025
Year: 2025

The introduction of automated vehicles has redefined the level of interaction between the driver and the vehicle, introducing new tasks and so impose different workloads. Existing tools such as NASA-TLX and DALI are still used to assess driving workload in automated vehicles, despite not accounting for new tasks. This study introduces AV-TLX, a specialized tool for measuring workload in Level 3 automated driving. The development process began with a narrative literature review to identify the primary factors influencing workload. This was followed by a series of qualitative sessions during which the dimensions and later the questions of the questionnaire were designed. The tools validity was first assessed using CVR and CVI indices, and its reliability and convergent validity were evaluated using a dynamic driving simulator with high fidelity. The final version of AV-TLX comprises 19 questions across 8 subscales, demonstrating excellent reliability (0.86) and validity (CVR > 0.78). An agreement scores between the results of AV-TLX and NASA-TLX in the simulation study was 0.6, which is considered acceptable for the consistency of two questionnaires. Furthermore, this questionnaire can be utilized in two ways. First by reporting the overall workload and/or divided into 8 primary subscales, or by categorizing the questions into two groups including takeover task workload and automated driving task workload. The final version of this questionnaire, as presented in the paper, is available for use in future studies focusing on Level 3 automated driving.
ProFed: a Benchmark for Proximity-based non-IID Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

In recent years, cro:flFederated learning (FL) has gained significant attention within the machine learning community. Although various FL algorithms have been proposed in the literature, their performance often degrades when data across clients is non-independently and identically distributed (non-IID). This skewness in data distribution often emerges from geographic patterns, with notable examples including regional linguistic variations in text data or localized traffic patterns in urban environments. Such scenarios result in IID data within specific regions but non-IID data across regions. However, existing FL algorithms are typically evaluated by randomly splitting non-IID data across devices, disregarding their spatial distribution. To address this gap, we introduce ProFed, a benchmark that simulates data splits with varying degrees of skewness across different regions. We incorporate several skewness methods from the literature and apply them to well-known datasets, including MNIST, FashionMNIST, CIFAR-10, and CIFAR-100. Our goal is to provide researchers with a standardized framework to evaluate FL algorithms more effectively and consistently against established baselines.
Collaborative Storytelling and LLM: A Linguistic Analysis of Automatically-Generated Role-Playing Game Sessions
Venue: arXiv cs.CL 2025
Year: 2025

Role-playing games (RPG) are games in which players interact with one another to create narratives. The role of players in the RPG is largely based on the interaction between players and their characters. This emerging form of shared narrative, primarily oral, is receiving increasing attention. In particular, many authors investigated the use of an LLM as an actor in the game. In this paper, we aim to discover to what extent the language of Large Language Models (LLMs) exhibit oral or written features when asked to generate an RPG session without human interference. We will conduct a linguistic analysis of the lexical and syntactic features of the generated texts and compare the results with analyses of conversations, transcripts of human RPG sessions, and books. We found that LLMs exhibit a pattern that is distinct from all other text categories, including oral conversations, human RPG sessions and books. Our analysis has shown how training influences the way LLMs express themselves and provides important indications of the narrative capabilities of these tools.
PVLens: Enhancing Pharmacovigilance Through Automated Label Extraction
Venue: arXiv cs.CL 2025
Year: 2025

Reliable drug safety reference databases are essential for pharmacovigilance, yet existing resources like SIDER are outdated and static. We introduce PVLens, an automated system that extracts labeled safety information from FDA Structured Product Labels (SPLs) and maps terms to MedDRA. PVLens integrates automation with expert oversight through a web-based review tool. In validation against 97 drug labels, PVLens achieved an F1 score of 0.882, with high recall (0.983) and moderate precision (0.799). By offering a scalable, more accurate and continuously updated alternative to SIDER, PVLens enhances real-time pharamcovigilance with improved accuracy and contemporaneous insights.
Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging
Venue: arXiv cs.CL 2025
Year: 2025

The transition from System 1 to System 2 reasoning in large language models (LLMs) has marked significant advancements in handling complex tasks through deliberate, iterative thinking. However, this progress often comes at the cost of efficiency, as models tend to overthink, generating redundant reasoning steps without proportional improvements in output quality. Long-to-Short (L2S) reasoning has emerged as a promising solution to this challenge, aiming to balance reasoning depth with practical efficiency. While existing approaches, such as supervised fine-tuning (SFT), reinforcement learning (RL), and prompt engineering, have shown potential, they are either computationally expensive or unstable. Model merging, on the other hand, offers a cost-effective and robust alternative by integrating the quick-thinking capabilities of System 1 models with the methodical reasoning of System 2 models. In this work, we present a comprehensive empirical study on model merging for L2S reasoning, exploring diverse methodologies, including task-vector-based, SVD-based, and activation-informed merging. Our experiments reveal that model merging can reduce average response length by up to 55% while preserving or even improving baseline performance. We also identify a strong correlation between model scale and merging efficacy with extensive evaluations on 1.5B/7B/14B/32B models. Furthermore, we investigate the merged model's ability to self-critique and self-correct, as well as its adaptive response length based on task complexity. Our findings highlight model merging as a highly efficient and effective paradigm for L2S reasoning, offering a practical solution to the overthinking problem while maintaining the robustness of System 2 reasoning. This work can be found on Github https://github.com/hahahawu/Long-to-Short-via-Model-Merging.
Inclusion with repetitions and Boolean constants -- implication problems revisited
Venue: arXiv cs.LO 2025
Year: 2025

Inclusion dependencies form one of the most widely used dependency classes. We extend existing results on the axiomatization and computational complexity of their implication problem to two extended variants. We present an alternative completeness proof for standard inclusion dependencies and extend it to inclusion dependencies with repetitions that can express equalities between attributes. The proof uses only two values, enabling us to work in the Boolean setting. Furthermore, we study inclusion dependencies with Boolean constants, provide a complete axiomatization and show that no such system is k-ary. Additionally, the decision problems for both extended versions remain PSPACE-complete. The extended inclusion dependencies examined are common in team semantics, which serves as the formal framework for the results.
TN-Eval: Rubric and Evaluation Protocols for Measuring the Quality of Behavioral Therapy Notes
Venue: arXiv cs.CL 2025
Year: 2025

Behavioral therapy notes are important for both legal compliance and patient care. Unlike progress notes in physical health, quality standards for behavioral therapy notes remain underdeveloped. To address this gap, we collaborated with licensed therapists to design a comprehensive rubric for evaluating therapy notes across key dimensions: completeness, conciseness, and faithfulness. Further, we extend a public dataset of behavioral health conversations with therapist-written notes and LLM-generated notes, and apply our evaluation framework to measure their quality. We find that: (1) A rubric-based manual evaluation protocol offers more reliable and interpretable results than traditional Likert-scale annotations. (2) LLMs can mimic human evaluators in assessing completeness and conciseness but struggle with faithfulness. (3) Therapist-written notes often lack completeness and conciseness, while LLM-generated notes contain hallucination. Surprisingly, in a blind test, therapists prefer and judge LLM-generated notes to be superior to therapist-written notes.
AutoRad-Lung: A Radiomic-Guided Prompting Autoregressive Vision-Language Model for Lung Nodule Malignancy Prediction
Venue: arXiv cs.CV 2025
Year: 2025

Lung cancer remains one of the leading causes of cancer-related mortality worldwide. A crucial challenge for early diagnosis is differentiating uncertain cases with similar visual characteristics and closely annotation scores. In clinical practice, radiologists rely on quantitative, hand-crafted Radiomic features extracted from Computed Tomography (CT) images, while recent research has primarily focused on deep learning solutions. More recently, Vision-Language Models (VLMs), particularly Contrastive Language-Image Pre-Training (CLIP)-based models, have gained attention for their ability to integrate textual knowledge into lung cancer diagnosis. While CLIP-Lung models have shown promising results, we identified the following potential limitations: (a) dependence on radiologists' annotated attributes, which are inherently subjective and error-prone, (b) use of textual information only during training, limiting direct applicability at inference, and (c) Convolutional-based vision encoder with randomly initialized weights, which disregards prior knowledge. To address these limitations, we introduce AutoRad-Lung, which couples an autoregressively pre-trained VLM, with prompts generated from hand-crafted Radiomics. AutoRad-Lung uses the vision encoder of the Large-Scale Autoregressive Image Model (AIMv2), pre-trained using a multi-modal autoregressive objective. Given that lung tumors are typically small, irregularly shaped, and visually similar to healthy tissue, AutoRad-Lung offers significant advantages over its CLIP-based counterparts by capturing pixel-level differences. Additionally, we introduce conditional context optimization, which dynamically generates context-specific prompts based on input Radiomics, improving cross-modal alignment.
Mitigating Low-Level Visual Hallucinations Requires Self-Awareness: Database, Model and Training Strategy
Venue: arXiv cs.CV 2025
Year: 2025

The rapid development of multimodal large language models has resulted in remarkable advancements in visual perception and understanding, consolidating several tasks into a single visual question-answering framework. However, these models are prone to hallucinations, which limit their reliability as artificial intelligence systems. While this issue is extensively researched in natural language processing and image captioning, there remains a lack of investigation of hallucinations in Low-level Visual Perception and Understanding (HLPU), especially in the context of image quality assessment tasks. We consider that these hallucinations arise from an absence of clear self-awareness within the models. To address this issue, we first introduce the HLPU instruction database, the first instruction database specifically focused on hallucinations in low-level vision tasks. This database contains approximately 200K question-answer pairs and comprises four subsets, each covering different types of instructions. Subsequently, we propose the Self-Awareness Failure Elimination (SAFEQA) model, which utilizes image features, salient region features and quality features to improve the perception and comprehension abilities of the model in low-level vision tasks. Furthermore, we propose the Enhancing Self-Awareness Preference Optimization (ESA-PO) framework to increase the model's awareness of knowledge boundaries, thereby mitigating the incidence of hallucination. Finally, we conduct comprehensive experiments on low-level vision tasks, with the results demonstrating that our proposed method significantly enhances self-awareness of the model in these tasks and reduces hallucinations. Notably, our proposed method improves both accuracy and self-awareness of the proposed model and outperforms close-source models in terms of various evaluation metrics.
MMMORRF: Multimodal Multilingual Modularized Reciprocal Rank Fusion
Venue: arXiv cs.CV 2025
Year: 2025

Videos inherently contain multiple modalities, including visual events, text overlays, sounds, and speech, all of which are important for retrieval. However, state-of-the-art multimodal language models like VAST and LanguageBind are built on vision-language models (VLMs), and thus overly prioritize visual signals. Retrieval benchmarks further reinforce this bias by focusing on visual queries and neglecting other modalities. We create a search system MMMORRF that extracts text and features from both visual and audio modalities and integrates them with a novel modality-aware weighted reciprocal rank fusion. MMMORRF is both effective and efficient, demonstrating practicality in searching videos based on users' information needs instead of visual descriptive queries. We evaluate MMMORRF on MultiVENT 2.0 and TVR, two multimodal benchmarks designed for more targeted information needs, and find that it improves nDCG@20 by 81% over leading multimodal encoders and 37% over single-modality retrieval, demonstrating the value of integrating diverse modalities.
Ontology-based Semantic Similarity Measures for Clustering Medical Concepts in Drug Safety
Venue: arXiv cs.CL 2025
Year: 2025

Semantic similarity measures (SSMs) are widely used in biomedical research but remain underutilized in pharmacovigilance. This study evaluates six ontology-based SSMs for clustering MedDRA Preferred Terms (PTs) in drug safety data. Using the Unified Medical Language System (UMLS), we assess each method's ability to group PTs around medically meaningful centroids. A high-throughput framework was developed with a Java API and Python and R interfaces support large-scale similarity computations. Results show that while path-based methods perform moderately with F1 scores of 0.36 for WUPALMER and 0.28 for LCH, intrinsic information content (IC)-based measures, especially INTRINSIC-LIN and SOKAL, consistently yield better clustering accuracy (F1 score of 0.403). Validated against expert review and standard MedDRA queries (SMQs), our findings highlight the promise of IC-based SSMs in enhancing pharmacovigilance workflows by improving early signal detection and reducing manual review.