计算机科学最新文献

{"title":"MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair.","authors":"Meghdad Dehghan, Jie Jw Wu, Fatemeh H Fard, Ali Ouni","doi":"10.1007/s10664-026-10943-y","DOIUrl":"10.1007/s10664-026-10943-y","url":null,"abstract":"<p><p>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 adapters offer a more efficient way to customize LLMs for particular needs, leveraging the pre-existing capabilities of the large model. Model (and adapter) merging have emerged as a technique to develop one model capable of multiple tasks, with minimal or no training required. Although model and adapter merging has shown promising performance in domains such as natural language processing and computer vision, its applicability to software engineering tasks remains underexplored. In this paper, we investigate the effectiveness of merged adapters within the context of software engineering, with a particular focus on the Automated Program Repair (APR) task, through our approach, <b>MergeRepair</b>. In particular, we merge multiple task-specific adapters using three different merging methods, including weight-averaging, TIES-Merging, and DARE-TIES, and evaluate the performance of the merged adapter on the APR task. We introduce a <b>continual merging</b> approach, a novel method in which we sequentially merge the task-specific adapters where the order and weight of the merged adapters play a significant role. We further compare the performance of our approach with a baseline method consisting of equal-weight merging applied on parameters of different adapters, where all adapters are of equal importance. Through continual merging, we explore the capability of merged adapters and the effect of task order, as it occurs in real-world software projects. To evaluate our approach, we consider two LLMs, StarCoder2 and Granite models on an APR benchmark, HumanEvalFix, with various combinations of the merged adapters trained on a subset of the CommitPackFT dataset that contains four tasks and APR in Python. The results show that merging task-specific adapters can enhance the performance of models on the APR task, <i>without additional training</i>. In particular, our approach achieves up to 2.38% performance improvement over APR task in terms of pass@1 score when merging APR with Improvement and Misc task-adapters on top of the StarCoder2 model. Performance rises to 4.01% for pass@10 score with the same model and merged adapters. This improvement could be significantly influenced by the base model and the specific task adapters merged, rather than only the number of adapters. The proposed continual merging approach can be effective if the order of merging is efficiently determined. Results show that performance improves when the most effective task-adapter is added last","PeriodicalId":11525,"journal":{"name":"Empirical Software Engineering","volume":"32 1","pages":"37"},"PeriodicalIF":3.4,"publicationDate":"2027-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149059353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"D3I-COMCF: Dissimilarity-driven dual-interaction via co-occurrence motifs and complementary fragments for drug-drug interaction prediction.","authors":"Wentao Xie, Min Jin, Mingjian Yang, Yulu Zhou, Xinhua Liu, Meng Li, Jagath C Rajapakse","doi":"10.1016/j.neunet.2026.109328","DOIUrl":"10.1016/j.neunet.2026.109328","url":null,"abstract":"<p><p>Drug-drug interaction (DDI) prediction seeks to identify pharmacodynamic and pharmacokinetic interactions arising from the co-administration of multiple drugs with differing physicochemical properties. However, most existing methods predominantly focus on global molecular or atomic topologies, overlooking the critical role of dynamically derived functional substructures (motifs) in modulating drug activity and the occurrence and strength of DDI. To this end, we propose a dissimilarity-driven dual-interaction via co-occurrence motifs and complementary fragments (D3I-COMCF) framework that deeply integrates molecular-, motif-, and atomic-level information for accurate DDI prediction. Specifically, we construct, for the first time, a co-occurrence motifs molecular interaction graph (CMMIG) that unifies the global molecular structure, local physicochemical properties of motifs, and molecule-motif interaction topology. By weighting rare, dissimilar but information-rich motifs, D3I-COMCF was guided to focus on more discriminative functional units. Moreover, based on the maximum common substructure mapping and its dissimilar one-hop neighbor extension, we dynamically incorporated complementary fragments attached to the shared backbone into a fragment interaction graph, revealing electronic complementarity and spatial cooperativity with computational efficiency. Finally, hierarchical multi-block integration of the three levels enables cross-scale joint representation of drug pairs. Extensive experiments on multiple public datasets verified the effectiveness of D3I-COMCF, which achieved strong and stable performance under standard benchmark protocols and showed promising cold-start generalization and interpretability. Additional hard-negative analysis further indicates that D3I-COMCF remains sensitive to known positive DDIs, while controlling false positives among structurally similar candidate pairs not annotated as known positives remains challenging.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"204 ","pages":"109328"},"PeriodicalIF":7.2,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148406673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A boundary-regularization-enhanced video anomaly detection network based on context-adaptive spatio-temporal conditional diffusion.","authors":"Huilin Liu, Guanghan Sun, Xiaolong Hu, Tian Liu, Wanqi Ma","doi":"10.1016/j.neunet.2026.109330","DOIUrl":"10.1016/j.neunet.2026.109330","url":null,"abstract":"<p><p>Unsupervised video anomaly detection aims to learn normal spatio-temporal patterns from unlabeled surveillance data and identify deviations during inference. Existing methods often struggle to capture long-term temporal dependencies and adapt to spatial variations caused by target deformation and viewpoint changes. Although diffusion-based models provide stable distribution modeling, their limited temporal conditioning and fixed spatial representations restrict anomaly discrimination in complex scenes. To address these issues, we propose a boundary regularization enhanced video anomaly detection network based on context-adaptive spatio-temporal conditional diffusion. Rather than relying on rigid feature extraction, our method explicitly models long-range motion evolution and dynamically adapts to spatial variations caused by target deformation and viewpoint shifts. Furthermore, to prevent overfitting to normal behaviors, we introduce a hybrid pseudo-anomaly generation strategy that acts as a boundary regularization constraint, explicitly sharpening the model's discriminative capability against abnormal patterns. Experiments on benchmark datasets including UCSD Ped2, CUHK Avenue, and ShanghaiTech demonstrate that our method significantly improves detection accuracy. By effectively integrating context-adaptive conditioning and boundary regularization, this work establishes a highly discriminative and generalizable paradigm for video anomaly detection, demonstrating strong robustness in complex, real-world surveillance scenarios.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"204 ","pages":"109330"},"PeriodicalIF":7.2,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148399058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical Kinematic Model and RCM Control of a Parallel-Serial Steady-Hand Eye Robot.","authors":"Botao Zhao, Mojtaba Esfandiari, Teng Long, Yub Heo, Haochen Wei, Adnan Munawar, Peter Gehlbach, Peter Kazanzides, Iulian Iordachita","doi":"10.1115/1.4072676","DOIUrl":"https://doi.org/10.1115/1.4072676","url":null,"abstract":"<p><p>Maintaining a precise remote center of motion is essential for safe and accurate tool manipulation in retinal microsurgery. However, existing numerical Jacobian identification methods for parallel manipulators often exhibit nonlinear, workspace-dependent inaccuracies and require frequent recalibration, limiting their reliability and clinical applicability. To address these challenges, this study presents an analytical kinematic framework for a hybrid parallel-serial robot designed for retinal surgery, known as the Steady-Hand Eye Robot (SHER 3.0). Closed-form solutions for forward and inverse kinematics, as well as analytical formulations of the direct and inverse Jacobians, are derived to ensure consistent motion estimation across the workspace. The kinematic performance of SHER 3.0 is analyzed to evaluate the manipulability and workspace efficiency. Building on these models, a model predictive control strategy is implemented on SHER 3.0, which maintains the remote center of motion constraint at the sclerotomy with sub-millimeter accuracy, with a root mean square error of 0.55 ± 0.12 mm, in a pilot study on a teleoperation experiment. Experimental results demonstrate the effectiveness of the proposed analytical models and control framework in enhancing robot motion stability and robustness of the controller in maintaining the remote center of motion constraints, enabling safe robot-assisted retinal microsurgery.</p>","PeriodicalId":49155,"journal":{"name":"Journal of Mechanisms and Robotics-Transactions of the Asme","volume":"18 11","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13656171/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149185595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic generalisable multimodal AI generated content detection via mixture of orthogonal decomposition and product manifold disentanglement","authors":"Qian Liu, Sonja Schimmler, Manfred Hauswirth","doi":"10.1007/s40747-026-02536-5","DOIUrl":"https://doi.org/10.1007/s40747-026-02536-5","url":null,"abstract":"The generalisability of AI-Generated Content (AIGC, e.g., images and videos) detection can be decomposed into two tightly coupled subspaces: a semantic subspace and a forgery subspace. State-of-the-art AIGC detectors primarily focus on enhancing generalisability within the forgery subspace, typically neglecting the influences of the semantic subspace. Moreover, most existing AIGC detection methods consolidate all forged images and videos produced by heterogeneous deepfake generation techniques into a single “fake” class. This design choice overlooks the distinctive characteristics and substantial variability associated with different deepfake generation techniques. To address these limitations, we propose a novel Semantic Generalisable Deepfake Detection (SGDFD) framework that integrates a sparsely gated Mixture of ORthogonal Decomposition (MoORD) architecture. The parameter-efficient fine-tuning strategy based on orthogonal decomposition enhances the generalisation capability of the SGDFD model in AIGC detection, while the MoORD architecture further improves its semantic generalisability. In addition, the SGDFD model learns a product-manifold representation that decomposes the monolithic “fake” class into multiple sub-classes, each corresponding to a distinct deepfake generation method. Extensive experiments conducted on 10 real-world AIGC detection datasets under 3 evaluation protocols demonstrate that the proposed SGDFD model achieves state-of-the-art generalisation performance in AIGC detection across both known and unknown semantic categories.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"65 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149221441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Test-Time Computing for Referring Multimodal Large Language Models","authors":"Mingrui Wu, Hao Chen, Jiayi Ji, Xiaoshuai Sun, Zhiyuan Liu, Liujuan Cao, Ming-Ming Cheng, Rongrong Ji","doi":"10.1007/s11263-026-03100-x","DOIUrl":"https://doi.org/10.1007/s11263-026-03100-x","url":null,"abstract":"","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"11 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149221438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized multi-scale dual-modality feature pyramid network for early eye disease classification using fundus and OCT imaging","authors":"R. Kiruthiga, A. Saran Kumar","doi":"10.1007/s40747-026-02507-w","DOIUrl":"https://doi.org/10.1007/s40747-026-02507-w","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"33 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149221440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Event-Guided Decomposition from a Long-Exposure Nighttime Snapshot into Video Sequence","authors":"Jianbo Cao, Yuqi Han, Siming Zheng, Guilin Xu, Qiang Zhang, Jinli Suo","doi":"10.1007/s11263-026-03057-x","DOIUrl":"https://doi.org/10.1007/s11263-026-03057-x","url":null,"abstract":"","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"15 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149221439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards understanding and monitorability of reasoning LLMs","authors":"","doi":"10.1038/s42256-026-01333-6","DOIUrl":"10.1038/s42256-026-01333-6","url":null,"abstract":"Frontier LLMs are trained to solve problems in a step-by-step way, generating reasoning text in human-understandable language. However, these reasoning tokens are not accurate explanations of LLMs’ internal computations, raising challenges for monitorability.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"8 10","pages":"1507-1507"},"PeriodicalIF":29.8,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s42256-026-01333-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149219602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Explainable AI with Humans: Trust, Reliance, Understanding, and Performance","authors":"Jonathan Frez Zachary, Nelson Baloian, Luciano Ahumada, Ashot Harutyunyan","doi":"10.1145/3856819","DOIUrl":"https://doi.org/10.1145/3856819","url":null,"abstract":"Explainable AI (XAI) is often evaluated through claims about trust, reliance, understanding, and performance, yet studies measure these constructs differently and frequently overlook confounding factors. This PRISMA-guided systematic review with narrative synthesis examines human-centered XAI evaluation across these four constructs. The review covers publications from 2015 to 2025. A merged search combined an OpenAlex-informed DOI/title catalog, prior surveys, citation chasing, and a Scopus coverage-completion stage identifying 9,026 records. A targeted revision update added two eligible publications, yielding 119 validated full-text publications. The synthesis highlights three patterns: trust and reliance can diverge; perceived understanding can exceed objective understanding; and interface salience or perceived accuracy can confound explanation effects. Aggregate advice acceptance, for example, cannot distinguish beneficial reliance from automation bias. We therefore recommend measuring acceptance separately for correct and incorrect AI outputs, testing understanding beyond self-report, documenting study context and interface conditions, and evaluating explanations under uncertainty or model failure. The review contributes construct definitions, a synthesis across designs and domains, an analysis of evaluation measures, and practical reporting guidelines. It also examines generated explanations and citation-based interfaces, supporting more reproducible and interpretable human-centered XAI evaluations.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"5 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149218727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}