id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ba9f4bd2fe3f7f3521e3f5347c068ef8d41531fef70030d3cd04c9e9791e56ce | [
"arxiv",
"semantic_scholar"
] | Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data | Large language models (LLMs) are increasingly used as conditional generators for structured data, relying on in-context learning (ICL) to adapt to new distributions without parameter updates. We investigate the limits of ICL for structured generation under distribution mismatch, using high-cardinality tabular data as a... | [
"Antonio Pelusi",
"Stefano Braghin",
"Alberto Trombetta"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-06-10T00:00:00 | https://arxiv.org/abs/2606.11961 | https://arxiv.org/pdf/2606.11961v1 | 2606.11961 | null | 0 | 0 | false | null | null | 0 |
4468b28148dbdb0015d75ed2fda3269df71d399ff9dbae28a81f01037160a553 | [
"arxiv",
"semantic_scholar"
] | Fixed-Parameter Tractability of Private Synthetic Data Generation | We study the problem of generating synthetic data under differential privacy. We establish fixed-parameter tractability (FPT) for this problem where the parameter is the treewidth of the query family's incidence graph. Our algorithms attain optimal error rates across all regimes and are realized by two different approa... | [
"Badih Ghazi",
"CristΓ³bal GuzmΓ‘n",
"Pritish Kamath",
"Alexander Knop",
"Ravi Kumar",
"Pasin Manurangsi"
] | [
"cs.DS",
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2026-06-09T00:00:00 | https://arxiv.org/abs/2606.11283 | https://arxiv.org/pdf/2606.11283v1 | 2606.11283 | null | 0 | 0 | false | null | null | 0 |
6fcb8750efc3ce0a2815966bdc391e18544d0f9565b2e02cf1d21fea42cae6f5 | [
"arxiv",
"semantic_scholar"
] | Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm | Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e.g., images) can guide a (smaller) student model building on another type of data (e.g., text/audio). Existing CMKD methods often require paired multi-modal data with aligned semantics, but obtaining such paired ... | [
"Trong Khiem Tran",
"Anh Duc Chu",
"Quang Hung Pham",
"Phi Le Nguyen",
"Trong Nghia Hoang"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-06-09T00:00:00 | https://arxiv.org/abs/2606.10504 | https://arxiv.org/pdf/2606.10504v1 | 2606.10504 | null | 0 | 0 | false | null | null | 0 |
da98b269aa44709d5e736c5a89ae287bdd942ec2a60938258b89f90c82b1ceda | [
"arxiv",
"semantic_scholar"
] | Non-covalent Interactions at cm$^{-1}$ Accuracy: Data Efficient Physics-Informed Distillation for Machine Learning Interatomic Potentials | Foundation models in atomistic machine learning encode interaction physics across diverse atomic environments, but whether that structure can be transferred when building specialist potentials at quantum-chemical accuracy remains open. Here we show that knowledge distillation from a pretrained universal machine-learnin... | [
"Yulin Shen",
"Shahzad Akram",
"Louis Primeau",
"Gen Zu",
"Konstantinos D. Vogiatzis",
"Yang Zhang",
"Adrian Del Maestro"
] | [
"physics.chem-ph"
] | [
"Physics"
] | 2026-06-03T00:00:00 | https://arxiv.org/abs/2606.05127 | https://arxiv.org/pdf/2606.05127v1 | 2606.05127 | null | 0 | 0 | true | https://github.com/DelMaestroGroup/papers-code-mlip-distillation-sapt | null | 0 |
c82a462f67c0410d7f4ecde4b69f966745a35460e6ba791b31b5e2f6b717c62a | [
"arxiv",
"semantic_scholar"
] | State Machine Guided Multi-Relational Synthetic Data from Logs for Anomaly Detection | Software systems generate massive unstructured logs that record execution behavior, failures, and interactions across components, yet existing log anomaly detection methods treat these logs primarily as flat sequences of templates, overlooking the relational execution structure that governs how events co-occur and evol... | [
"Aja Khanal",
"Apurva Narayan"
] | [
"cs.MA"
] | [
"Computer Science"
] | 2026-05-30T00:00:00 | https://arxiv.org/abs/2606.00531 | https://arxiv.org/pdf/2606.00531v1 | 2606.00531 | 10.1145/3770855.3818134 | 0 | 0 | false | null | Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD 2026) | 0 |
931e08dd31d97511721aa0df16883280d2cf5c0dc6c8b1a7378c44ad86ec49ce | [
"arxiv",
"semantic_scholar"
] | SilentRetrieval: Hijacking Retrieval-Augmented Generation via Semantically-Preserving Adversarial Data Poisoning | Retrieval-Augmented Generation (RAG) mitigates LLM hallucinations but introduces a critical vulnerability: corpus integrity. We present SilentRetrieval, a two-stage data poisoning attack that hijacks RAG systems through adversarially crafted yet fluent documents. Stage 1 uses Coordinated Beam Search, a multi-token join... | [
"Jiachen Qian"
] | [
"cs.CR",
"cs.CL",
"cs.IR"
] | [
"Computer Science"
] | 2026-05-27T00:00:00 | https://arxiv.org/abs/2605.28074 | https://arxiv.org/pdf/2605.28074v1 | 2605.28074 | 10.1145/3770855.3818186 | 0 | 0 | false | null | Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD '26), August 09--13, 2026, Jeju Island, Republic of Korea | 0 |
24a1583360e38bf4298c904d4413dc4c4335a8475f7e579570e5e73499f360bb | [
"arxiv",
"semantic_scholar"
] | Knowledge Graphs as the Missing Data Layer for LLM-Based Industrial Asset Operations | LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores. AssetOpsBench (KDD 2026) establishes that GPT-4 agents achieve 65% on 139 industrial maintenance scenarios, and compares LLM orchestration paradigms (Agent-As-Tool vs. Plan-Execute) on a fixed data layer. We... | [
"Madhulatha Mandarapu",
"Sandeep Kunkunuru"
] | [
"cs.DB",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-26T00:00:00 | https://arxiv.org/abs/2605.26874 | https://arxiv.org/pdf/2605.26874v2 | 2605.26874 | null | 0 | 0 | true | null | null | 0 |
b1d42184dcb41fae9648a55dd958fc9a48abb82e54f100acf3bf788edc729388 | [
"arxiv",
"semantic_scholar"
] | Generating Logically Consistent Synthetic Supply Chain Data with LLM-Driven Knowledge Graph Reasoning | Synthetic data offers a promising solution to two persistent barriers in supply chain analytics: data scarcity and data privacy. However, for synthetic data to support operational simulation and decision-making, it must do more than reproduce the statistical distributions of real records, and also preserve the \emph{op... | [
"Yunbo Long",
"Ge Zheng",
"Liming Xu",
"Alexandra Brintrup"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-26T00:00:00 | https://arxiv.org/abs/2605.26823 | https://arxiv.org/pdf/2605.26823v1 | 2605.26823 | null | 0 | 0 | false | null | null | 0 |
45247d95e2be1a8c2ea3875bd787dda61845ed8b880c7ec8b3a8410efe105917 | [
"arxiv",
"semantic_scholar"
] | Re-defining Humor Data Objects for AI Humor Research | In most existing AI humor research, humor was treated as either "present" or "not present." We explore the concept of humor as a social interaction with context and explanations. During this project, we defined a humor reasoning data object and developed a way to prompt LLMs to generate an explanation of humor effectiv... | [
"Anna Arnett",
"Bang Nguyen",
"Meng Jiang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-24T00:00:00 | https://arxiv.org/abs/2605.25171 | https://arxiv.org/pdf/2605.25171v2 | 2605.25171 | null | 0 | 0 | true | https://github.com/anna-arnett/ai-humor/ | null | 0 |
7670b3ceb9be16b6bc4a3afb2bccb6aeb65925999ad9cf1356a3245f1b69c691 | [
"arxiv",
"semantic_scholar"
] | Muon Nuclear Data Development Project | Negative muon-induced nuclear reactions play a critical role in a wide range of scientific and technological applications; however, comprehensive nuclear data for these processes remain unavailable. To address this gap, we have launched the Muon Nuclear Data (muND) Development Project in Japan, aiming to construct a de... | [
"Yukinobu Watanabe",
"Megumi Niikura",
"Shinichiro Abe",
"Sayani Biswas",
"Hiroki Iwamoto",
"Adrian Hillier",
"Naritoshi Kawamura",
"Shoichiro Kawase",
"Teiichiro Matsuzaki",
"Futoshi Minato",
"Rurie Mizuno",
"Dai Tomono",
"Yuji Yamaguchi"
] | [
"nucl-ex",
"nucl-th"
] | [
"Physics"
] | 2026-05-15T00:00:00 | https://arxiv.org/abs/2605.15539 | https://arxiv.org/pdf/2605.15539v1 | 2605.15539 | null | 0 | 0 | false | null | null | 0 |
259cdbba7e1ef534abbe5f8f7c58b3160748be9378fa1b1acbbd5ff37d09e222 | [
"arxiv",
"semantic_scholar"
] | The Nova Synthetic Data Base: A Principal Component/AI Analysis of Novae Synoptic Spectra | The Nova Synthetic Data Base (NSDB) is presented as the first publicly available database of synthetic spectra for classical nova shells, spanning an unprecedented range of physical parameters (e.g., ejecta mass, chemical composition, temperature, and luminosity of the white dwarf) at several post-eruption ages. Genera... | [
"Bruno C. Santos",
"Marcos P. Diaz",
"Larissa Takeda"
] | [
"astro-ph.SR",
"astro-ph.IM"
] | [
"Physics"
] | 2026-05-14T00:00:00 | https://arxiv.org/abs/2605.15432 | https://arxiv.org/pdf/2605.15432v1 | 2605.15432 | 10.3847/1538-4365/ae5641 | 0 | 0 | false | null | Astrophys. J. Suppl. Ser. 284, 24 (2026) | 0 |
f6333e4806e8d00584543ee7a209a25700f49fa15333991ffd185a9b6ce59266 | [
"arxiv",
"semantic_scholar"
] | A Toolbox to Understand the Physics of Quantum Data Management | The application of quantum computing to data management has attracted growing interest, yet remains constrained by a limited understanding of how the physical behaviour of quantum devices relates to the structure and difficulty of database problems. In particular, evaluating quantum annealing approaches for combinatori... | [
"Wolfgang Mauerer",
"Manuel SchΓΆnberger"
] | [
"quant-ph",
"cs.DB"
] | [
"Physics",
"Computer Science"
] | 2026-05-14T00:00:00 | https://arxiv.org/abs/2605.14719 | https://arxiv.org/pdf/2605.14719v1 | 2605.14719 | null | 1 | 0 | false | null | null | 0.0753 |
0d9c902e7642ec06ef0ae3b512f58b6d8afe48168f22643dab6367b0d3f55cbb | [
"arxiv",
"semantic_scholar"
] | Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection | Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of annotated anomalies and abundant unlabeled samples. A major challenge is to learn a meaningful latent feature representation that reduces i... | [
"Yingjie Zhou",
"Yuqin Xie",
"Fanxing Liu",
"Dongjin Song",
"Ce Zhu",
"Lingqiao Liu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.11749 | https://arxiv.org/pdf/2605.11749v1 | 2605.11749 | 10.1109/TKDE.2026.3656821 | 0 | 0 | true | https://github.com/yj-zhou/SAWGAD | IEEE Transactions on Knowledge and Data Engineering | 0 |
01d9f31e1dbf08cc0141b84af2aab52d981400138216b7f767da4ec50f5e135d | [
"arxiv",
"semantic_scholar"
] | Automated Big Data Quality Assessment using Knowledge Graph Embeddings | Automated data quality assessment is crucial for managing big data, but existing solutions face challenges in achieving accurate context-aware assessment. This paper presents a novel knowledge-based approach to enhance automated data quality assessment. Our approach utilizes knowledge graph embeddings to predict missin... | [
"Hadi Fadlallah",
"Rima Kilany",
"Mitri Haber",
"Ali Jaber"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.18833 | https://arxiv.org/pdf/2605.18833v1 | 2605.18833 | 10.1504/IJDMMM.2025.150987 | 1 | 0 | false | null | International Journal of Data Mining Modelling and Management | 0.0753 |
1cdd15cbecb299f7f994abea1bc9c190ce9962fdedeb2c3735c7a46f69207b9c | [
"arxiv",
"semantic_scholar"
] | Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation | The Room Acoustics and Speaker Distance Estimation (SDE) Challenge at ICASSP 2025 explores the effectiveness of augmented room impulse response (RIR) data for improving SDE model performance. This challenge at GenDARA involves generating RIRs to supplement sparse datasets and fine-tuning SDE models with the augmented d... | [
"Anton Ratnarajah",
"Mehmet Ergezer",
"Arun Nair",
"Mrudula Athi"
] | [
"cs.SD",
"cs.AI",
"eess.AS",
"eess.SP"
] | [
"Computer Science",
"Engineering"
] | 2026-05-01T00:00:00 | https://arxiv.org/abs/2605.00721 | https://arxiv.org/pdf/2605.00721v1 | 2605.00721 | null | 0 | 0 | true | null | Generative Data Augmentation for Real-World Signal Processing Applications (GenDA 2025). An ICASSP 2025 Satellite Workshop and IEEE Data Science and Learning Workshop | 0 |
31c533c54c4f33e49192d3c8c20859a3df47b03cf5ea17fbeef00d1e5f2fb917 | [
"arxiv",
"semantic_scholar"
] | Multimodal Data Curation Through Ranked Retrieval | Shared embedding spaces are widely used for multimodal search and data curation. In practice, two problems often limit how well this works. First, embeddings can reflect modality more than meaning, so examples cluster by input type even when the underlying content matches. Second, the paired supervision used to train t... | [
"Pratyush Muthukumar",
"Harshil Kotamreddy",
"Sarah Amiraslani",
"Tomo Kanazawa",
"Ramani Akkati",
"Shaan Jain",
"Andrew Mathau"
] | [
"cs.IR",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-01T00:00:00 | https://arxiv.org/abs/2605.01163 | https://arxiv.org/pdf/2605.01163v1 | 2605.01163 | null | 0 | 0 | false | null | null | 0 |
e2b7c068efaba712c4070107b3de420ea1861bb585a7376e9bc6e26db010d777 | [
"arxiv",
"semantic_scholar"
] | The Solar System Notification Alert Processing System (SNAPS): Public access to SNAPS data and products | The Solar System Notification Alert Processing System, SNAPS, is a downstream broker that ingests moving object data from ZTF and LSST and serves these data and derived properties to the public. This document describes how users can access our SNAPS data and products. This is intended to be a living document that will ... | [
"David E. Trilling",
"Michael Gowanlock",
"Revanth Munugala",
"Daniel R. Kramer",
"Maria Chernyavskaya",
"Erin Clark",
"Graceson Mule",
"Savannah Chappus"
] | [
"astro-ph.EP",
"astro-ph.IM"
] | [
"Physics"
] | 2026-04-30T00:00:00 | https://arxiv.org/abs/2604.27420 | https://arxiv.org/pdf/2604.27420v1 | 2604.27420 | null | 0 | 0 | false | null | null | 0 |
f3816b51589742c7f15bc625ba37732627c2ebaa94badc721d8a20d123864c87 | [
"arxiv",
"semantic_scholar"
] | Diverse Image Priors for Black-box Data-free Knowledge Distillation | Knowledge distillation (KD) represents a vital mechanism to transfer expertise from complex teacher networks to efficient student models. However, in decentralized or secure AI ecosystems, privacy regulations and proprietary interests often restrict access to the teacher's interface and original datasets. These constra... | [
"Tri-Nhan Vo",
"Dang Nguyen",
"Trung Le",
"Kien Do",
"Sunil Gupta"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2026-04-28T00:00:00 | https://arxiv.org/abs/2604.25794 | https://arxiv.org/pdf/2604.25794v1 | 2604.25794 | null | 0 | 0 | false | null | null | 0 |
772160fcefbfed49c0c7c0aad1871e234ec5a8bd058ca9008ef8e141cc16d4c2 | [
"arxiv",
"semantic_scholar"
] | Cross-Domain Data Selection and Augmentation for Automatic Compliance Detection | Automating the detection of regulatory compliance remains a challenging task due to the complexity and variability of legal texts. Models trained on one regulation often fail to generalise to others. This limitation underscores the need for principled methods to improve cross-domain transfer. We study data selection as... | [
"Fariz Ikhwantri",
"Dusica Marijan"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2026-04-23T00:00:00 | https://arxiv.org/abs/2604.21469 | https://arxiv.org/pdf/2604.21469v1 | 2604.21469 | 10.1109/BigData66926.2025.11401435 | 0 | 0 | false | null | BigData Congress [Services Society] | 0 |
03e2fb5bf339d69f9b6038ac057380d0044af7c7ce07ad7f83278a6ba1a0ec85 | [
"arxiv",
"semantic_scholar"
] | Synthetic Flight Data Generation Using Generative Models | The increasing adoption of synthetic data in aviation research offers a promising solution to data scarcity and confidentiality challenges. This study investigates the potential of generative models to produce realistic synthetic flight data and evaluates their quality through a comprehensive four-stage assessment fram... | [
"Karim Aly",
"Alexei Sharpanskykh"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-04-22T00:00:00 | https://arxiv.org/abs/2604.20293 | https://arxiv.org/pdf/2604.20293v1 | 2604.20293 | 10.1109/ICNS65417.2025.10976960 | 1 | 0 | false | null | International Conference on Networking and Services | 0.0753 |
1929b528bc78fa06d97d0561f18ebc9181070c1e529042b57280c37eec779b88 | [
"arxiv",
"semantic_scholar"
] | Toward Cross-Lingual Quality Classifiers for Multilingual Pretraining Data Selection | As Large Language Models (LLMs) scale, data curation has shifted from maximizing volume to optimizing the signal-to-noise ratio by performing quality filtering. However, for many languages, native high quality data is insufficient to train robust quality classifiers. This work investigates the idea that quality markers... | [
"Yassine Turki",
"Vinko SabolΔec",
"Bettina Messmer",
"Martin Jaggi"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-22T00:00:00 | https://arxiv.org/abs/2604.20549 | https://arxiv.org/pdf/2604.20549v1 | 2604.20549 | null | 0 | 0 | false | null | null | 0 |
cf2ca4ce0aa4f0df2db5f2f82271c09e1cd6f2aa8d43ec45a7375bf0eb0eea2b | [
"arxiv",
"semantic_scholar"
] | Adversarial Arena: Crowdsourcing Data Generation through Interactive Competition | Post-training Large Language Models requires diverse, high-quality data which is rare and costly to obtain, especially in low resource domains and for multi-turn conversations. Common solutions are crowdsourcing or synthetic generation, but both often yield low-quality or low-diversity data. We introduce Adversarial Ar... | [
"Prasoon Goyal",
"Sattvik Sahai",
"Michael Johnston",
"Hangjie Shi",
"Yao Lu",
"Shaohua Liu",
"Anna Rumshisky",
"Rahul Gupta",
"Anna Gottardi",
"Desheng Zhang",
"Lavina Vaz",
"Leslie Ball",
"Lucy Hu",
"Luke Dai",
"Samyuth Sagi",
"Maureen Murray",
"Sankaranarayanan Ananthakrishnan"
] | [
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-04-20T00:00:00 | https://arxiv.org/abs/2604.17803 | https://arxiv.org/pdf/2604.17803v1 | 2604.17803 | null | 0 | 0 | true | null | null | 0 |
a676b4b6b4ea554ec42f2950b89dc5724fec7a9ef66a5b0dab98ba916da9c066 | [
"arxiv",
"semantic_scholar"
] | Reverse Constitutional AI: A Framework for Controllable Toxic Data Generation via Probability-Clamped RLAIF | Ensuring the safety of large language models (LLMs) requires robust red teaming, yet the systematic synthesis of high-quality toxic data remains under-explored. We propose Reverse Constitutional AI (R-CAI), a framework for automated and controllable adversarial data generation that moves beyond isolated jailbreak promp... | [
"Yuan Fang",
"Yiming Luo",
"Aimin Zhou",
"Fei Tan"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-20T00:00:00 | https://arxiv.org/abs/2604.17769 | https://arxiv.org/pdf/2604.17769v1 | 2604.17769 | null | 0 | 0 | true | https://github.com/ZeroLoss-Lab/R-CAI | null | 0 |
29791699448af97d6314ddde2cc6a1e332f8b9dad65cba22b8a5d805e0f2e8f8 | [
"arxiv",
"semantic_scholar"
] | A Complexity Agnostic Clustering Engine for Time Projection Chambers and its Implementation in FPGA | A clustering functional block implemented in field-programable-gate-array (FPGA) for time projection chambers (TPC) operating with predictable time regardless the complexity of the event is described in this paper. The clustering functional block reorganizes input data and the hits data belonging to the same clusters a... | [
"Jinyuan Wu",
"Michael Wang",
"Datao Gong"
] | [
"physics.ins-det"
] | [
"Physics"
] | 2026-04-17T00:00:00 | https://arxiv.org/abs/2604.16253 | https://arxiv.org/pdf/2604.16253v1 | 2604.16253 | null | 0 | 0 | false | null | null | 0 |
608423cb3d036d22d0f2445d13c6d8793526e766c8848e2355fb6a3395877219 | [
"arxiv",
"semantic_scholar"
] | Synthetic Tabular Generators Fail to Preserve Behavioral Fraud Patterns: A Benchmark on Temporal, Velocity, and Multi-Account Signals | We introduce behavioral fidelity -- a third evaluation dimension for synthetic tabular data that measures whether generated data preserves the temporal, sequential, and structural behavioral patterns that distinguish real-world entity activity. Existing frameworks evaluate statistical fidelity (marginal distributions a... | [
"Bhavana Sajja"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-04-13T00:00:00 | https://arxiv.org/abs/2604.13125 | https://arxiv.org/pdf/2604.13125v1 | 2604.13125 | 10.5281/zenodo.19545114 | 0 | 0 | true | https://github.com/bhavana3/synthetic-data-experiments | null | 0 |
23ba24289a7f42386fab4894d40e37a41551da1c40e934681ec1287f6ce0901e | [
"arxiv",
"semantic_scholar"
] | Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning | The rapid growth of generative artificial intelligence (AI) has introduced unprecedented computational demands, driving significant increases in the energy footprint of data centers. However, existing power consumption data is largely proprietary and reported at varying resolutions, creating challenges for estimating w... | [
"Roberto Vercellino",
"Jared Willard",
"Gustavo Campos",
"Weslley da Silva Pereira",
"Olivia Hull",
"Matthew Selensky",
"Juliane Mueller"
] | [
"eess.SY",
"cs.DC",
"cs.LG"
] | [
"Engineering",
"Computer Science"
] | 2026-04-08T00:00:00 | https://arxiv.org/abs/2604.07345 | https://arxiv.org/pdf/2604.07345v1 | 2604.07345 | null | 2 | 0 | false | null | null | 0.1193 |
71460b735a6560680e233d9e30db2bd5dd90f88099920406d1fb1a7f0649c1ab | [
"arxiv",
"semantic_scholar"
] | Stop Fixating on Prompts: Reasoning Hijacking and Constraint Tightening for Red-Teaming LLM Agents | With the widespread application of LLM-based agents across various domains, their complexity has introduced new security threats. Existing red-team methods mostly rely on modifying user prompts, which lack adaptability to new data and may impact the agent's performance. To address the challenge, this paper proposes the... | [
"Yanxu Mao",
"Peipei Liu",
"Tiehan Cui",
"Congying Liu",
"Mingzhe Xing",
"Datao You"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-04-07T00:00:00 | https://arxiv.org/abs/2604.05549 | https://arxiv.org/pdf/2604.05549v2 | 2604.05549 | null | 0 | 0 | false | null | null | 0 |
7226f11ff1ae9ab933b7bb22fecc1fd85550470db3356f93d1bb4d0322f51bab | [
"arxiv",
"semantic_scholar"
] | A Synthetic Eye Movement Dataset for Script Reading Detection: Real Trajectory Replay on a 3D Simulator | Large vision-language models have achieved remarkable capabilities by training on massive internet-scale data, yet a fundamental asymmetry persists: while LLMs can leverage self-supervised pretraining on abundant text and image data, the same is not true for many behavioral modalities. Video-based behavioral data -- ge... | [
"Kidus Zewde",
"Yuchen Zhou",
"Dennis Ng",
"Neo Tiangratanakul",
"Tommy Duong",
"Ankit Raj",
"Yuxin Zhang",
"Xingyu Shen",
"Simiao Ren"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-04-07T00:00:00 | https://arxiv.org/abs/2604.05475 | https://arxiv.org/pdf/2604.05475v1 | 2604.05475 | null | 0 | 0 | false | null | null | 0 |
bb0c0b1b8916fdee6107a2d676210ecb1f73aab25cb1cd9fd36980488bf9723f | [
"arxiv",
"semantic_scholar"
] | Stable and Privacy-Preserving Synthetic Educational Data with Empirical Marginals: A Copula-Based Approach | To advance Educational Data Mining (EDM) within strict privacy-protecting regulatory frameworks, researchers must develop methods that enable data-driven analysis while protecting sensitive student information. Synthetic data generation is one such approach, enabling the release of statistically generated samples inste... | [
"Gabriel Diaz Ramos",
"Lorenzo Luzi",
"Debshila Basu Mallick",
"Richard Baraniuk"
] | [
"cs.LG",
"cs.CY"
] | [
"Computer Science"
] | 2026-04-05T00:00:00 | https://arxiv.org/abs/2604.04195 | https://arxiv.org/pdf/2604.04195v1 | 2604.04195 | null | 0 | 0 | false | null | null | 0 |
9e6d1c1647ec7547fc139aa03b739e1b9dfdc33a17417af179a2f7cb0327e262 | [
"arxiv",
"semantic_scholar"
] | The IAEA Fusion Data Lake Project -- Accelerating AI and Big Data Applications through Open Science and FAIR Data | AI applications in fusion is a maturing field, playing a key role as surrogate models and digital twins to overcome computational expense limitations and insufficiently characterised phenomena, and expanding the horizon for real-time applications. The IAEA is supporting this activity through the AI for Fusion Coordinat... | [
"Daljeet Singh Gahle",
"Matteo Barbarino"
] | [
"physics.plasm-ph",
"physics.app-ph"
] | [
"Physics"
] | 2026-04-02T00:00:00 | https://arxiv.org/abs/2604.01797 | https://arxiv.org/pdf/2604.01797v1 | 2604.01797 | null | 0 | 0 | false | null | null | 0 |
932602d25b3ecf66a3676c4ca72aa2c5dbc0018ad8f5d208b0f98a8020aa5164 | [
"arxiv",
"semantic_scholar"
] | KMTNet Synoptic Survey of Southern Sky III: The First Data Release | We present the first public data release (DR1) of the KMTNet Synoptic Survey of Southern Sky (KS4). This deep, wide-field imaging survey covers a southern footprint of -85$^{\circ}$ < Decl. < -28.8$^{\circ}$ in the $B$, $V$, $R$, and $I$ bands using a network of three 1.6-m telescopes. Although primarily designed to se... | [
"Seo-Won Chang",
"Myungshin Im",
"Mankeun Jeong",
"Joonho Kim",
"Bomi Park",
"Jaewon Lee",
"David A. H. Buckley",
"Jeff Cooke",
"Sungho Jung",
"Dong-Jin Kim",
"Ji Hoon Kim",
"Yongjung Kim",
"Chung-Uk Lee",
"Seong-Kook Lee",
"Gregory S. H. Paek",
"Jiseop Shin"
] | [
"astro-ph.IM",
"astro-ph.GA"
] | [
"Physics"
] | 2026-03-30T00:00:00 | https://arxiv.org/abs/2603.28089 | https://arxiv.org/pdf/2603.28089v1 | 2603.28089 | null | 0 | 0 | false | null | null | 0 |
2724bbc5ce66cc9d196fc1beac09c89a773859389ad2e6ea9138f85519ab6f8c | [
"arxiv",
"semantic_scholar"
] | Mapping data literacy trajectories in K-12 education | Data literacy skills are fundamental in computer science education. However, understanding how data-driven systems work represents a paradigm shift from traditional rule-based programming. We conducted a systematic literature review of 84 studies to understand K-12 learners' engagement with data across disciplines and ... | [
"Robert Whyte",
"Manni Cheung",
"Katharine Childs",
"Jane Waite",
"Sue Sentance"
] | [
"cs.CY",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-30T00:00:00 | https://arxiv.org/abs/2603.28317 | https://arxiv.org/pdf/2603.28317v1 | 2603.28317 | null | 0 | 0 | false | null | null | 0 |
a6bb067eaec23056cd6d6be54052544ed5d6779aff21ac76d3150b444199c7ee | [
"arxiv",
"semantic_scholar"
] | Text Data Integration | Data comes in many forms. From a shallow perspective, they can be viewed as being either in structured (e.g., as a relation, as key-value pairs) or unstructured (e.g., text, image) formats. So far, machines have been fairly good at processing and reasoning over structured data that follows a precise schema. However, th... | [
"Md Ataur Rahman",
"Dimitris Sacharidis",
"Oscar Romero",
"Sergi Nadal"
] | [
"cs.CL",
"cs.IR"
] | [
"Computer Science"
] | 2026-03-28T00:00:00 | https://arxiv.org/abs/2603.27055 | https://arxiv.org/pdf/2603.27055v1 | 2603.27055 | null | 0 | 0 | false | null | null | 0 |
9d2b8caa681bcc51265d2af7e81c21061edc253ec3c7a036a777ae73017b29c5 | [
"arxiv",
"semantic_scholar"
] | KI-Adventskalender: An Informal Learning Intervention for Data & AI Literacy | Secondary school students increasingly encounter AI systems whose outputs depend on data quality, evaluation choices and modeling assumptions. To provide accessible entry points to these interconnected concepts, we developed KI-Adventskalender, a free web-based extracurricular initiative with 24 didactically curated, s... | [
"Rahul Sharma",
"Lars Henrich",
"Larisa Ivanova",
"Arsalan Karimzadmotallebiazar",
"Annette Bieniusa",
"Leo Van Waveren",
"Sebastian Vollmer"
] | [
"cs.HC"
] | [
"Computer Science"
] | 2026-03-27T00:00:00 | https://arxiv.org/abs/2603.26906 | https://arxiv.org/pdf/2603.26906v2 | 2603.26906 | null | 0 | 0 | false | null | null | 0 |
9a0d7b6cc2c0a6a564241d1a0ebf75136fbae7b7d6c027e80530c6533a1af822 | [
"arxiv",
"semantic_scholar"
] | Data Gravity and the Energy Limits of Computation | Unlike the von Neumann architecture, which separates computation from memory, the brain tightly integrates them, an organization that large language models increasingly resemble. The crucial difference lies in the ratio of energy spent on computation versus data access: in the brain, most energy fuels compute, while in... | [
"Wonsuk Lee",
"Jehoshua Bruck"
] | [
"cs.AR"
] | [
"Computer Science"
] | 2026-03-27T00:00:00 | https://arxiv.org/abs/2603.26053 | https://arxiv.org/pdf/2603.26053v1 | 2603.26053 | null | 0 | 0 | false | null | null | 0 |
cb21c0e491970e7b3dbdaab622bd18621aa83d2dcfe0eff6c51aca334d1b099d | [
"arxiv",
"semantic_scholar"
] | DFLOP: A Data-driven Framework for Multimodal LLM Training Pipeline Optimization | Multimodal Large Language Models (MLLMs) have achieved remarkable advances by integrating text, image, and audio understanding within a unified architecture. However, existing distributed training frameworks remain fundamentally data-blind: they parallelize computation without accounting for variations in input data ch... | [
"Hyeonjun An",
"Sihyun Kim",
"Chaerim Lim",
"Hyunjoon Kim",
"Rathijit Sen",
"Sangmin Jung",
"Hyeonsoo Lee",
"Dongwook Kim",
"Takki Yu",
"Jinkyu Jeong",
"Youngsok Kim",
"Kwanghyun Park"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2026-03-26T00:00:00 | https://arxiv.org/abs/2603.25120 | https://arxiv.org/pdf/2603.25120v1 | 2603.25120 | 10.1145/3802037 | 0 | 0 | false | null | Proc. ACM Manag. Data 4, 3, Article 160 (June 2026), 29 pages | 0 |
6df5cb857d195ab32a84179c0511644b03b9048bd27dcb8082a4ec6bdf3a137a | [
"arxiv",
"semantic_scholar"
] | Comparing Natural and Synthetic Structured Data: A Study of the Passive Verb Alternation in French and Italian | This study compares the impact of natural and synthetic data on training and evaluating large language models (LLMs), using the case of passive verb alternation in French and Italian. We use Blackbird Language Matrices (BLMs), structured datasets designed to probe linguistic knowledge of underlying patterns across sent... | [
"Giuseppe Samo",
"Paola Merlo"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-03-26T00:00:00 | https://arxiv.org/abs/2603.25227 | https://arxiv.org/pdf/2603.25227v1 | 2603.25227 | null | 0 | 0 | false | null | null | 0 |
e637766eeee16df1e15d7b36e1ed2f34abcb17c56462e9510c52748d9ba9cc71 | [
"arxiv",
"semantic_scholar"
] | Knowledge-Guided Retrieval-Augmented Generation for Zero-Shot Psychiatric Data: Privacy Preserving Synthetic Data Generation | AI systems in healthcare research have shown potential to increase patient throughput and assist clinicians, yet progress is constrained by limited access to real patient data. To address this issue, we present a zero-shot, knowledge-guided framework for psychiatric tabular data in which large language models (LLMs) ar... | [
"Adam Jakobsen",
"Sushant Gautam",
"Hugo Lewi Hammer",
"Susanne Olofsdotter",
"Miriam S Johanson",
"PΓ₯l Halvorsen",
"Vajira Thambawita"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-26T00:00:00 | https://arxiv.org/abs/2603.25186 | https://arxiv.org/pdf/2603.25186v1 | 2603.25186 | null | 0 | 0 | false | null | null | 0 |
75179b0045f788fe725dc6ecb9272aff660e8df526b2224a9d1e8960cb0273a1 | [
"arxiv",
"semantic_scholar"
] | Attack Assessment and Augmented Identity Recognition for Human Skeleton Data | Machine learning models trained on small data sets for security applications are especially vulnerable to adversarial attacks. Person identification from LiDAR based skeleton data requires time consuming and expensive data acquisition for each subject identity. Recently, Assessment and Augmented Identity Recognition fo... | [
"Joseph G. Zalameda",
"Megan A. Witherow",
"Alexander M. Glandon",
"Jose Aguilera",
"Khan M. Iftekharuddin"
] | [
"cs.LG",
"cs.CR",
"cs.CV"
] | [
"Computer Science"
] | 2026-03-25T00:00:00 | https://arxiv.org/abs/2603.24232 | https://arxiv.org/pdf/2603.24232v1 | 2603.24232 | 10.1109/IJCNN54540.2023.10191835 | 1 | 0 | false | null | IEEE International Joint Conference on Neural Network | 0.0753 |
0853a12b5820406fae704bce050fdd2424476f020b5b6850fe9e3b4fc8643b9c | [
"arxiv",
"semantic_scholar"
] | Data Mixing for Large Language Models Pretraining: A Survey and Outlook | Large language models (LLMs) rely on pretraining on massive and heterogeneous corpora, where training data composition has a decisive impact on training efficiency and downstream generalization under realistic compute and data budget constraints. Unlike sample-level data selection, data mixing optimizes domain-level sa... | [
"Zhuo Chen",
"Yuxuan Miao",
" Supryadi",
"Deyi Xiong"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-25T00:00:00 | https://arxiv.org/abs/2604.16380 | https://arxiv.org/pdf/2604.16380v1 | 2604.16380 | 10.3724/2096-7004.di.2026.0055 | 0 | 0 | false | null | Data Intelligence | 0 |
ca5baaf7a190773b27113ff79e48c3681371723308769a7e007675b92a7a0a13 | [
"arxiv",
"semantic_scholar"
] | Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs? | Self-distillation has emerged as an effective post-training paradigm for LLMs, often improving performance while shortening reasoning traces. However, in mathematical reasoning, we find that it can reduce response length while degrading performance. We trace this degradation to the suppression of epistemic verbalizatio... | [
"Jeonghye Kim",
"Xufang Luo",
"Minbeom Kim",
"Sangmook Lee",
"Dohyung Kim",
"Jiwon Jeon",
"Dongsheng Li",
"Yuqing Yang"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-25T00:00:00 | https://arxiv.org/abs/2603.24472 | https://arxiv.org/pdf/2603.24472v3 | 2603.24472 | null | 42 | 7 | true | https://github.com/beanie00/self-distillation-analysis | null | 0.4084 |
2b3306ad51b8ec0ffae1b8f3a005c13e7327834e4e9ba5cdbc396df0a0c1fee5 | [
"arxiv",
"semantic_scholar"
] | Spatial Analysis on Value-Based Quadtrees of Rasterized Vector Data | Mobility data science offers insights into the complex interconnections of spatial data of moving objects and their surroundings, often based on a combination of vector and raster data. For example, mobility traces are usually in vector format, weather data are often in raster format. Yet, available spatial analysis to... | [
"Diana Baumann",
"Nils Japke",
"Tim C. Rese",
"David Bermbach"
] | [
"cs.DB"
] | [
"Computer Science"
] | 2026-03-24T00:00:00 | https://arxiv.org/abs/2603.23105 | https://arxiv.org/pdf/2603.23105v4 | 2603.23105 | null | 0 | 0 | false | null | null | 0 |
d7802482b9934d7e2a406a7f991574f9733bc95bc43fa46d664a4bc76a054093 | [
"arxiv",
"semantic_scholar"
] | Assessing Data Literacy in K-12 Education: Challenges and Opportunities | Data literacy has become a key learning objective in K-12 education, but it remains an ambiguous concept as teachers interpret it differently. When creating assessments, teachers turn broad ideas about "working with data" into concrete decisions about what materials to include. Since working with data visualizations is... | [
"Annabel Goldman",
"Yuan Cui",
"Matthew Kay"
] | [
"cs.HC"
] | [
"Computer Science"
] | 2026-03-22T00:00:00 | https://arxiv.org/abs/2603.21382 | https://arxiv.org/pdf/2603.21382v2 | 2603.21382 | null | 0 | 0 | false | null | null | 0 |
79162a472af0a620006cec89b7bb9a71f631487192a6e39b62ecd723e5b17a1d | [
"arxiv",
"semantic_scholar"
] | R&D: Balancing Reliability and Diversity in Synthetic Data Augmentation for Semantic Segmentation | Collecting and annotating datasets for pixel-level semantic segmentation tasks are highly labor-intensive. Data augmentation provides a viable solution by enhancing model generalization without additional real-world data collection. Traditional augmentation techniques, such as translation, scaling, and color transforma... | [
"Huy Che",
"Dinh-Duy Phan",
"Duc-Khai Lam"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-19T00:00:00 | https://arxiv.org/abs/2603.18427 | https://arxiv.org/pdf/2603.18427v1 | 2603.18427 | 10.1007/978-3-032-09321-9_30 | 0 | 0 | true | https://github.com/chequanghuy/Enhanced-Generative-Data-Augmentation-for-Semantic-Segmentation-via-Stronger-Guidance}{https://github.com/chequanghuy/Enhanced-Generative-Data-Augmentation-for-Semantic-Segmentation-via-Stronger-Guidance} | International Conference on Computational Collective Intelligence | 0 |
0d2c47403c4d43f5d5555a0c1ed3335f3abce0913ba5a4e92af8c5fc99b01f5d | [
"arxiv",
"semantic_scholar"
] | Synthetic Data, Information, and Prior Knowledge: Why Synthetic Data Augmentation to Boost Sample Doesn't Work for Statistical Inference | The use of synthetic data to deidentify data and to improve predictive models is well-attested to. The augmentation of datasets using synthetically generated data is an alluring proposition: in the best case, it generates realistic data \textit{in silico} at a fraction of the cost of authentic data which may be found \... | [
"Reid Dale",
"Jordan Rodu",
"Mike Baiocchi"
] | [
"stat.ME"
] | [
"Mathematics"
] | 2026-03-18T00:00:00 | https://arxiv.org/abs/2603.18345 | https://arxiv.org/pdf/2603.18345v1 | 2603.18345 | null | 1 | 0 | false | null | null | 0.0753 |
c2a924e1cb0a1f8d78f1c9005e5d314102cc55772bec73746b5b8c02f666614d | [
"arxiv",
"semantic_scholar"
] | Knowledge Distillation for Large Language Models | We propose a resource-efficient framework for compressing large language models through knowledge distillation, combined with guided chain-of-thought reinforcement learning. Using Qwen 3B as the teacher and Qwen 0.5B as the student, we apply knowledge distillation across English Dolly-15k, Spanish Dolly-15k, and code B... | [
"Alejandro Paredes La Torre",
"Barbara Flores",
"Diego Rodriguez"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-14T00:00:00 | https://arxiv.org/abs/2603.13765 | https://arxiv.org/pdf/2603.13765v1 | 2603.13765 | null | 8 | 2 | true | https://github.com/AlejandroParedesLT/knowledge_distillLLM | null | 0.2386 |
25966d3d72fc71b385dc432c50057dd063605d9371b5bfe5ea4a640d2d4ff7db | [
"arxiv",
"semantic_scholar"
] | Greedy Information Projection for LLM Data Selection | We present \emph{Greedy Information Projection} (\textsc{GIP}), a principled framework for choosing training examples for large language model fine-tuning. \textsc{GIP} casts selection as maximizing mutual information between a subset of examples and task-specific query signals, which may originate from LLM quality jud... | [
"Victor Ye Dong",
"Kuan-Yun Lee",
"Jiamei Shuai",
"Shengfei Liu",
"Yi Liu",
"Jian Jiao"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-03-14T00:00:00 | https://arxiv.org/abs/2603.13790 | https://arxiv.org/pdf/2603.13790v1 | 2603.13790 | null | 1 | 0 | false | null | null | 0.0753 |
7ac96d80eabc6195405b55e9c394859da6db6906979806be0c3872e7be032baf | [
"arxiv",
"semantic_scholar"
] | Grounding Synthetic Data Generation With Vision and Language Models | Deep learning models benefit from increasing data diversity and volume, motivating synthetic data augmentation to improve existing datasets. However, existing evaluation metrics for synthetic data typically calculate latent feature similarity, which is difficult to interpret and does not always correlate with the contr... | [
"Γmit Mert ΓaΔlar",
"Alptekin Temizel"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-10T00:00:00 | https://arxiv.org/abs/2603.09625 | https://arxiv.org/pdf/2603.09625v2 | 2603.09625 | null | 1 | 0 | true | null | null | 0.0753 |
2ee3fd21d15b910cedaf6ee3f131ec68cf3a65167a6237a01259aefba6acbe75 | [
"arxiv",
"semantic_scholar"
] | Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure | Synthetic tabular data generation addresses data scarcity and privacy constraints in a variety of domains. Tabular Prior-Data Fitted Network (TabPFN), a recent foundation model for tabular data, has been shown capable of generating high-quality synthetic tabular data. However, TabPFN is autoregressive: features are gen... | [
"Davide Tugnoli",
"Andrea De Lorenzo",
"Marco Virgolin",
"Giovanni CinΓ "
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-10T00:00:00 | https://arxiv.org/abs/2603.10254 | https://arxiv.org/pdf/2603.10254v1 | 2603.10254 | null | 1 | 0 | true | https://github.com/DavideTugnoli/tabpfn-causal-synthetic | null | 0.0753 |
0dbfca4e111be9e745db13f8b56201e4e33d0f3b6898b4b27f3cd057ed411daf | [
"arxiv",
"semantic_scholar"
] | MAcPNN: Mutual Assisted Learning on Data Streams with Temporal Dependence | Internet of Things (IoT) Analytics often involves applying machine learning (ML) models on data streams. In such scenarios, traditional ML paradigms face obstacles related to continuous learning while dealing with concept drifts, temporal dependence, and avoiding forgetting. Moreover, in IoT, different edge devices bui... | [
"Federico Giannini",
"Emanuele Della Valle"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-09T00:00:00 | https://arxiv.org/abs/2603.08972 | https://arxiv.org/pdf/2603.08972v1 | 2603.08972 | 10.1109/BigData62323.2024.10825150 | 0 | 0 | false | null | BigData Congress [Services Society] | 0 |
8db0d2aeadabb2f4263aff65e7c6f92bc3513abdd335a1e128541c888407f7a2 | [
"arxiv",
"semantic_scholar"
] | Query-Guided Analysis and Mitigation of Data Verification Errors (Extended Version) | Data verification, the process of labeling data items as correct or incorrect, is a preprocessing step that may critically affect the quality of results in data-driven pipelines. Despite recent advances, verification can still produce erroneous labels that propagate to downstream query results in complex ways. We prese... | [
"Ran Schreiber",
"Yael Amsterdamer"
] | [
"cs.DB"
] | [
"Computer Science"
] | 2026-03-09T00:00:00 | https://arxiv.org/abs/2603.08612 | https://arxiv.org/pdf/2603.08612v1 | 2603.08612 | null | 0 | 0 | false | null | null | 0 |
141e02aa64830e2ccdf25286769ddaeb065edd93387335ff40b0a3622603c7b9 | [
"arxiv",
"semantic_scholar"
] | Visualization Retrieval for Data Literacy: Position Paper | Current resources for data literacy education, such as visualization galleries and datasets, provide useful examples but lack mechanisms for learners to query, compare, and navigate the visualization design space efficiently. This position paper advocates for visualization retrieval as essential infrastructure for data... | [
"Huyen N. Nguyen",
"Nils Gehlenborg"
] | [
"cs.HC"
] | [
"Computer Science"
] | 2026-03-06T00:00:00 | https://arxiv.org/abs/2604.09598 | https://arxiv.org/pdf/2604.09598v1 | 2604.09598 | 10.5281/zenodo.19240985 | 1 | 0 | false | null | null | 0.0753 |
21319c3f20387e68a1225a2fbb05f1b53dd556137934cc72c26f00fe18f3c702 | [
"arxiv",
"semantic_scholar"
] | FairFinGAN: Fairness-aware Synthetic Financial Data Generation | Financial datasets often suffer from bias that can lead to unfair decision-making in automated systems. In this work, we propose FairFinGAN, a WGAN-based framework designed to generate synthetic financial data while mitigating bias with respect to the protected attribute. Our approach incorporates fairness constraints ... | [
"Tai Le Quy",
"Dung Nguyen Tuan",
"Trung Nguyen Thanh",
"Duy Tran Cong",
"Huyen Giang Thi Thu",
"Frank Hopfgartner"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-05T00:00:00 | https://arxiv.org/abs/2603.05327 | https://arxiv.org/pdf/2603.05327v1 | 2603.05327 | null | 0 | 0 | false | null | null | 0 |
cd4cf9f5e4f178b89aa0a95dc6272374a695575a3fc87b56d0ac6c9c5e28d599 | [
"arxiv",
"semantic_scholar"
] | A Late-Fusion Multimodal AI Framework for Privacy-Preserving Deduplication in National Healthcare Data Environments | Duplicate records pose significant challenges in customer relationship management (CRM)and healthcare, often leading to inaccuracies in analytics, impaired user experiences, and compliance risks. Traditional deduplication methods rely heavily on direct identifiers such as names, emails, or Social Security Numbers (SSNs... | [
"Mohammed Omer Shakeel Ahmed"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-04T00:00:00 | https://arxiv.org/abs/2603.04595 | https://arxiv.org/pdf/2603.04595v1 | 2603.04595 | 10.1109/FMLDS67896.2025.00021 | 0 | 0 | false | null | 2025 IEEE International Conference on Future Machine Learning and Data Science (FMLDS) | 0 |
59fdc3d3e3aec027e41bca47d2a5b0a2893c01a1a13cbc59581a306082dd01ff | [
"arxiv",
"semantic_scholar"
] | SEAnet: A Deep Learning Architecture for Data Series Similarity Search | A key operation for massive data series collection analysis is similarity search. According to recent studies, SAX-based indexes offer state-of-the-art performance for similarity search tasks. However, their performance lags under high-frequency, weakly correlated, excessively noisy, or other dataset-specific propertie... | [
"Qitong Wang",
"Themis Palpanas"
] | [
"cs.DB",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-02T00:00:00 | https://arxiv.org/abs/2603.01448 | https://arxiv.org/pdf/2603.01448v2 | 2603.01448 | 10.1109/TKDE.2023.3270264 | 12 | 0 | false | null | IEEE Transactions on Knowledge and Data Engineering | 0.2785 |
20b6a4ae5ef453c0cb3d573ae624418cf74014e964190d29adb77ab16d4da022 | [
"arxiv",
"semantic_scholar"
] | Synthetic Data in MR Spectroscopy: Current Practices, Applications, and Considerations | The use of synthetic data has emerged as an essential tool in Magnetic Resonance Spectroscopy (MRS) research and applications, providing advantages for optimization of acquisition, software validation, deep learning applications, and enhanced reproducibility. Importantly, synthetic data addresses challenges of limited ... | [
"John T. LaMaster",
"Aaron T. Gudmundson",
"Alireza Abaei",
"Seyma Alcicek",
"Arturo Alvarado",
"Ovidiu Andronesi",
"Tiffany K. Bell",
"Wolfgang Bogner",
"Hanna Bugler",
"Alexander R Craven",
"Cristina Cudalbu",
"Alma Davidson",
"Christopher W. Davies-Jenkins",
"Dinesh Deelchand",
"Richa... | [
"physics.med-ph"
] | [
"Physics"
] | 2026-02-26T00:00:00 | https://arxiv.org/abs/2602.23463 | https://arxiv.org/pdf/2602.23463v2 | 2602.23463 | null | 0 | 0 | false | null | null | 0 |
f2e901a57c6ceea018e18c7819f1ca18d344d4f062d697206e92269913c3e8af | [
"arxiv",
"semantic_scholar"
] | Forecasting Antimicrobial Resistance Trends Using Machine Learning on WHO GLASS Surveillance Data: A Retrieval-Augmented Generation Approach for Policy Decision Support | Antimicrobial resistance (AMR) is a growing global crisis projected to cause 10 million deaths per year by 2050. While the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) provides standardized surveillance data across 44 countries, few studies have applied machine learning to forecast population... | [
"Md Tanvir Hasan Turja"
] | [
"cs.LG",
"q-bio.QM"
] | [
"Computer Science",
"Biology"
] | 2026-02-26T00:00:00 | https://arxiv.org/abs/2602.22673 | https://arxiv.org/pdf/2602.22673v1 | 2602.22673 | 10.48550/arXiv.2602.22673 | 0 | 0 | true | https://github.com/TanvirTurja | arXiv.org | 0 |
4f6d795c8995a6ad9aa2313992b0d42eefbfe701252942e1430d4aa8c0f845b3 | [
"arxiv",
"semantic_scholar"
] | Workload-Aware Incremental Reclustering in Cloud Data Warehouses | Modern cloud data warehouses store data in micro-partitions and rely on metadata (e.g., zonemaps) for efficient data pruning during query processing. Maintaining data clustering in a large-scale table is crucial for effective data pruning. Existing automatic clustering approaches lack the flexibility required in dynami... | [
"Yipeng Liu",
"Renfei Zhou",
"Jiaqi Yan",
"Huanchen Zhang"
] | [
"cs.DB"
] | [
"Computer Science"
] | 2026-02-26T00:00:00 | https://arxiv.org/abs/2602.23289 | https://arxiv.org/pdf/2602.23289v2 | 2602.23289 | 10.1145/3802127 | 0 | 0 | false | null | null | 0 |
8704a8eb0c1960934b5dbaa47900db39bf4df329ec8dc6818e0afdfb6fff407e | [
"arxiv",
"semantic_scholar"
] | Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis | Generative Adversarial Networks (GAN) have been used in many studies to synthesise mixed tabular data. Conditional tabular GAN (CTGAN) have been the most popular variant but struggle to effectively navigate the risk-utility trade-off. Bayesian GAN have received less attention for tabular data, but have been explored wi... | [
"Bahrul Ilmi Nasution",
"Mark Elliot",
"Richard Allmendinger"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2026-02-25T00:00:00 | https://arxiv.org/abs/2602.21948 | https://arxiv.org/pdf/2602.21948v1 | 2602.21948 | 10.48550/arXiv.2602.21948 | 1 | 0 | false | null | arXiv.org | 0.0753 |
58bd721d0fd273d93eb3a6b78aed5c2713689238f56e5973df7234530d3a5777 | [
"arxiv",
"semantic_scholar"
] | Seasoning Data Modeling Education with GARLIC: A Participatory Co-Design Framework | Entity-Relationship (ER) modeling is commonly taught as a primarily technical activity, despite its central role in shaping how data systems represent people, processes, and institutions. Prior research in participatory design demonstrates that involving diverse stakeholders in modeling can surface tacit knowledge, cha... | [
"Viktoriia Makovska",
"Ihor Michurin",
"Mariia Tokhtamysh",
"George Fletcher",
"Julia Stoyanovich"
] | [
"cs.DB"
] | [
"Computer Science"
] | 2026-02-20T00:00:00 | https://arxiv.org/abs/2602.18274 | https://arxiv.org/pdf/2602.18274v1 | 2602.18274 | 10.48550/arXiv.2602.18274 | 0 | 0 | false | null | arXiv.org | 0 |
ffd4c7f8a6af305c960bcd8fb055bb357281c2546865e7a3e06c5fe258f73f4e | [
"arxiv",
"semantic_scholar"
] | DeepFusion: Accelerating MoE Training via Federated Knowledge Distillation from Heterogeneous Edge Devices | Recent Mixture-of-Experts (MoE)-based large language models (LLMs) such as Qwen-MoE and DeepSeek-MoE are transforming generative AI in natural language processing. However, these models require vast and diverse training data. Federated learning (FL) addresses this challenge by leveraging private data from heterogeneous... | [
"Songyuan Li",
"Jia Hu",
"Ahmed M. Abdelmoniem",
"Geyong Min",
"Haojun Huang",
"Jiwei Huang"
] | [
"cs.LG",
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2026-02-15T00:00:00 | https://arxiv.org/abs/2602.14301 | https://arxiv.org/pdf/2602.14301v1 | 2602.14301 | 10.48550/arXiv.2602.14301 | 0 | 0 | false | null | arXiv.org | 0 |
0a4763bd80f526d169372342ef7383ee64033651fafb06a68020fbde8fd77537 | [
"arxiv",
"semantic_scholar"
] | DistillLens: Symmetric Knowledge Distillation Through Logit Lens | Standard Knowledge Distillation (KD) compresses Large Language Models (LLMs) by optimizing final outputs, yet it typically treats the teacher's intermediate layer's thought process as a black box. While feature-based distillation attempts to bridge this gap, existing methods (e.g., MSE and asymmetric KL divergence) ign... | [
"Manish Dhakal",
"Uthman Jinadu",
"Anjila Budathoki",
"Rajshekhar Sunderraman",
"Yi Ding"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-02-14T00:00:00 | https://arxiv.org/abs/2602.13567 | https://arxiv.org/pdf/2602.13567v1 | 2602.13567 | 10.48550/arXiv.2602.13567 | 0 | 0 | true | https://github.com/manishdhakal/DistillLens | arXiv.org | 0 |
15e488d3e25f7f7133294d4f1ae81fd945454c3c976abd29fca90dc80019bc37 | [
"arxiv",
"semantic_scholar"
] | Pedagogically-Inspired Data Synthesis for Language Model Knowledge Distillation | Knowledge distillation from Large Language Models (LLMs) to smaller models has emerged as a critical technique for deploying efficient AI systems. However, current methods for distillation via synthetic data lack pedagogical awareness, treating knowledge transfer as a one-off data synthesis and training task rather tha... | [
"Bowei He",
"Yankai Chen",
"Xiaokun Zhang",
"Linghe Kong",
"Philip S. Yu",
"Xue Liu",
"Chen Ma"
] | [
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-02-12T00:00:00 | https://arxiv.org/abs/2602.12172 | https://arxiv.org/pdf/2602.12172v1 | 2602.12172 | 10.48550/arXiv.2602.12172 | 2 | 0 | false | null | arXiv.org | 0.1193 |
475cae0c0fab2f03e362e52f58ae624ac4c070da0274b3156e7e22fb324c298f | [
"arxiv",
"semantic_scholar"
] | Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation | Retrieval-Augmented Generation (RAG) has become a cornerstone of knowledge-intensive applications, including enterprise chatbots, healthcare assistants, and agentic memory management. However, recent studies show that knowledge-extraction attacks can recover sensitive knowledge-base content through maliciously crafted ... | [
"Zhisheng Qi",
"Utkarsh Sahu",
"Li Ma",
"Haoyu Han",
"Ryan Rossi",
"Franck Dernoncourt",
"Mahantesh Halappanavar",
"Nesreen Ahmed",
"Yushun Dong",
"Yue Zhao",
"Yu Zhang",
"Yu Wang"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2026-02-10T00:00:00 | https://arxiv.org/abs/2602.09319 | https://arxiv.org/pdf/2602.09319v3 | 2602.09319 | 10.1145/3770855.3817524 | 2 | 0 | false | null | arXiv.org | 0.1193 |
57afa3aa573e0a7c289d6ce59fa19c924cad0bbf81a33c629f36b47812d64254 | [
"arxiv",
"semantic_scholar"
] | COMBOOD: A Semiparametric Approach for Detecting Out-of-distribution Data for Image Classification | Identifying out-of-distribution (OOD) data at inference time is crucial for many machine learning applications, especially for automation. We present a novel unsupervised semi-parametric framework COMBOOD for OOD detection with respect to image recognition. Our framework combines signals from two distance metrics, near... | [
"Magesh Rajasekaran",
"Md Saiful Islam Sajol",
"Frej Berglind",
"Supratik Mukhopadhyay",
"Kamalika Das"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-02-04T00:00:00 | https://arxiv.org/abs/2602.07042 | https://arxiv.org/pdf/2602.07042v1 | 2602.07042 | 10.1137/1.9781611978032.74 | 10 | 1 | false | null | SDM | 0.2603 |
7c4f1f4d7af8edcb3c1f7c3d5bd459ca1b831b41cb006b745fd579985d3ef397 | [
"arxiv",
"semantic_scholar"
] | Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentation | Recent advances in generative modelling have led many to see synthetic data as the go-to solution for a range of problems around data access, scarcity, and under-representation. In this paper, we study three prominent use cases: (1) Sharing synthetic data as a proxy for proprietary datasets to enable statistical analys... | [
"Bogdan Kulynych",
"Theresa Stadler",
"Jean Louis Raisaro",
"Carmela Troncoso"
] | [
"cs.LG",
"cs.CY"
] | [
"Computer Science"
] | 2026-02-03T00:00:00 | https://arxiv.org/abs/2602.03791 | https://arxiv.org/pdf/2602.03791v1 | 2602.03791 | 10.48550/arXiv.2602.03791 | 0 | 0 | false | null | arXiv.org | 0 |
accddd0e575b15fd4cdb0817a95c3d54046f8fe1d42639fc58fc12d373a49fe8 | [
"arxiv",
"semantic_scholar"
] | Synthetic Data Augmentation for Medical Audio Classification: A Preliminary Evaluation | Medical audio classification remains challenging due to low signal-to-noise ratios, subtle discriminative features, and substantial intra-class variability, often compounded by class imbalance and limited training data. Synthetic data augmentation has been proposed as a potential strategy to mitigate these constraints;... | [
"David McShannon",
"Anthony Mella",
"Nicholas Dietrich"
] | [
"cs.SD",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-02-03T00:00:00 | https://arxiv.org/abs/2602.02955 | https://arxiv.org/pdf/2602.02955v1 | 2602.02955 | 10.48550/arXiv.2602.02955 | 1 | 0 | false | null | arXiv.org | 0.0753 |
8091a0db71b1f0448ec6fffe77277bee1bb544f6dcae129f3b724afcfc32f039 | [
"arxiv",
"semantic_scholar"
] | Privacy Amplification Persists under Unlimited Synthetic Data Release | We study privacy amplification by synthetic data release, a phenomenon in which differential privacy guarantees are improved by releasing only synthetic data rather than the private generative model itself. Recent work by Pierquin et al. (2025) established the first formal amplification guarantees for a linear generato... | [
"ClΓ©ment Pierquin",
"AurΓ©lien Bellet",
"Marc Tommasi",
"Matthieu Boussard"
] | [
"cs.CR",
"cs.DS",
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2026-02-03T00:00:00 | https://arxiv.org/abs/2602.04895 | https://arxiv.org/pdf/2602.04895v1 | 2602.04895 | 10.48550/arXiv.2602.04895 | 0 | 0 | false | null | arXiv.org | 0 |
a949099a0abe09527d25c5f84b4b0bf3e6b7a0ae7e7928eafbaa0be10dd2c13f | [
"arxiv",
"semantic_scholar"
] | A hybrid approach for building fuzzy numbers based on data and expert knowledge | This paper presents a hybrid socio-technical methodology for constructing fuzzy numbers from numerical data while incorporating expert knowledge through an interactive Deck of Cards (DoC) process. The approach extends the existing DoC membership function construction framework by introducing a data-driven pipeline base... | [
"Diego GarcΓa-Zamora",
"JosΓ© Rui Figueira",
"Miguel Couceiro"
] | [
"math.GM"
] | [
"Mathematics"
] | 2026-02-01T00:00:00 | https://arxiv.org/abs/2602.01192 | https://arxiv.org/pdf/2602.01192v1 | 2602.01192 | 10.1016/j.fss.2026.110000 | 0 | 0 | false | null | Diego GarcΓa-Zamora, JosΓ© Rui Figueira, Miguel Couceiro, A hybrid approach for building fuzzy numbers based on data and expert knowledge, Fuzzy Sets and Systems, Volume 542, 2026, 110000, ISSN 0165-0114 | 0 |
5d6220129167ade8ea1b85dd25bde67236011fbd813c32bdf457e7ece45408d4 | [
"arxiv",
"semantic_scholar"
] | Learning from Synthetic Data: Limitations of ERM | The prevalence and low cost of LLMs have led to a rise of synthetic content. From review sites to court documents, "natural" content has been contaminated by data points that appear similar to natural data, but are in fact LLM-generated. In this work we revisit fundamental learning theory questions in this, now ubiquit... | [
"Kareem Amin",
"Alex Bie",
"Weiwei Kong",
"Umar Syed",
"Sergei Vassilvitskii"
] | [
"cs.LG",
"cs.DS",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2026-01-21T00:00:00 | https://arxiv.org/abs/2601.15468 | https://arxiv.org/pdf/2601.15468v2 | 2601.15468 | 10.48550/arXiv.2601.15468 | 1 | 0 | false | null | arXiv.org | 0.0753 |
1f05a0ea84eb9971fc2149c17378489b7aa48bd267e4437f077c3acfd8013452 | [
"arxiv",
"semantic_scholar"
] | Synthetic Data Augmentation for Multi-Task Chinese Porcelain Classification: A Stable Diffusion Approach | The scarcity of training data presents a fundamental challenge in applying deep learning to archaeological artifact classification, particularly for the rare types of Chinese porcelain. This study investigates whether synthetic images generated through Stable Diffusion with Low-Rank Adaptation (LoRA) can effectively au... | [
"Ziyao Ling",
"Silvia Mirri",
"Paola Salomoni",
"Giovanni Delnevo"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2026-01-21T00:00:00 | https://arxiv.org/abs/2601.14791 | https://arxiv.org/pdf/2601.14791v1 | 2601.14791 | 10.48550/arXiv.2601.14791 | 0 | 0 | false | null | arXiv.org | 0 |
4797b14cc83b097f1285a45b822103b576f20716c6cce425a35372f9157609ec | [
"arxiv",
"semantic_scholar"
] | Derivative free data-driven stabilization of continuous-time linear systems from input-output data | This letter presents a data-driven framework for the design of stabilizing controllers from input-output data in the continuous-time, linear, and time-invariant domain. Rather than relying on measurements or reliable estimates of input and output time derivatives, the proposed approach uses filters to derive a paramete... | [
"Corrado Possieri"
] | [
"math.OC",
"math.DS"
] | [
"Mathematics",
"Computer Science"
] | 2026-01-20T00:00:00 | https://arxiv.org/abs/2601.13848 | https://arxiv.org/pdf/2601.13848v2 | 2601.13848 | 10.1109/LCSYS.2026.3658297 | 1 | 0 | false | null | IEEE Control Systems Letters | 0.0753 |
a65b00beb2e0a5aad333f5d6f12827d9856b9e30c15bf99373010126c672c7ad | [
"arxiv",
"semantic_scholar"
] | Approximating splits for decision trees quickly in sparse data streams | Decision trees are one of the most popular classifiers in the machine learning literature. While the most common decision tree learning algorithms treat data as a batch, numerous algorithms have been proposed to construct decision trees from a data stream. A standard training strategy involves augmenting the current tr... | [
"Nikolaj Tatti"
] | [
"cs.LG",
"cs.DS"
] | [
"Computer Science"
] | 2026-01-18T00:00:00 | https://arxiv.org/abs/2601.12525 | https://arxiv.org/pdf/2601.12525v1 | 2601.12525 | 10.1137/1.9781611978520.69 | 1 | 0 | false | null | SDM | 0.0753 |
a75863447534c699c5b55e4408a4103b7e5d05b51d4fcfd0900bb0b26e0ed80a | [
"arxiv",
"semantic_scholar"
] | Big Data Workload Profiling for Energy-Aware Cloud Resource Management | Cloud data centers face increasing pressure to reduce operational energy consumption as big data workloads continue to grow in scale and complexity. This paper presents a workload aware and energy efficient scheduling framework that profiles CPU utilization, memory demand, and storage IO behavior to guide virtual machi... | [
"Milan Parikh",
"Aniket Abhishek Soni",
"Sneja Mitinbhai Shah",
"Ayush Raj Jha"
] | [
"cs.DC",
"cs.AI",
"cs.SE"
] | [
"Computer Science"
] | 2026-01-17T00:00:00 | https://arxiv.org/abs/2601.11935 | https://arxiv.org/pdf/2601.11935v1 | 2601.11935 | 10.48550/arXiv.2601.11935 | 3 | 1 | false | null | arXiv.org | 0.1505 |
87ad79c902e8816a181dd8fbdb4a6a906429cf2e5193e2c2719478bf3cf10cc0 | [
"arxiv",
"semantic_scholar"
] | Translating database mathematical schemes into relational database software applications with MatBase | We present a pseudocode algorithm for translating our (Elementary) Mathematical Data Model schemes into relational ones and associated sets of non-relational constraints, used by MatBase, our intelligent data and knowledge base management system prototype. We prove that this algorithm is very fast, solid, complete, and... | [
"Christian Mancas",
"Diana Christina Mancas"
] | [
"cs.DB"
] | [
"Computer Science"
] | 2026-01-15T00:00:00 | https://arxiv.org/abs/2601.10604 | https://arxiv.org/pdf/2601.10604v4 | 2601.10604 | 10.54364/cybersecurityjournal.2026.3124 | 0 | 0 | false | null | Advances in Knowledge-Based Systems, Data Science, and Cybersecurity 2026, 3(1): 497-517 | 0 |
3551a47333765357895f55e0d6a3c21eb277fe9ff191e6ae71f98fe615a08e7c | [
"arxiv",
"semantic_scholar"
] | FilDeep: Learning Large Deformations of Elastic-Plastic Solids with Multi-Fidelity Data | The scientific computation of large deformations in elastic-plastic solids is crucial in various manufacturing applications. Traditional numerical methods exhibit several inherent limitations, prompting Deep Learning (DL) as a promising alternative. The effectiveness of current DL techniques typically depends on the av... | [
"Jianheng Tang",
"Shilong Tao",
"Zhe Feng",
"Haonan Sun",
"Menglu Wang",
"Zhanxing Zhu",
"Yunhuai Liu"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-01-15T00:00:00 | https://arxiv.org/abs/2601.10031 | https://arxiv.org/pdf/2601.10031v1 | 2601.10031 | 10.1145/3770854.3783959 | 0 | 0 | false | null | arXiv.org | 0 |
d620f22c230e98dbd8d8a68c9694f5c3ee6e07bb0b5de9adf42c68c4b72ad077 | [
"arxiv",
"semantic_scholar"
] | Radiation Resistance of Ge-doped Multi-Mode Fiber for Optical Links in Collider Experiments | The applications of optical links in collider experiments provide the advantage of high-speed data transmission with low mass fibers over distances of a few hundred meters. Ge-doped multi-mode fibers are evaluated for radiation tolerance in ionizing doses of Co-60 gamma rays. The Radiation-Induced Attenuation (RIA) var... | [
"Datao Gong",
"Suen Hou",
"Bo-Jing Juang",
"Chonghan Liu",
"Tiankuan Liu",
"Ming Qi",
"Jingbo Ye",
"Lei Zhang",
"Li Zhang"
] | [
"hep-ex"
] | [
"Physics"
] | 2026-01-11T00:00:00 | https://arxiv.org/abs/2601.06822 | https://arxiv.org/pdf/2601.06822v2 | 2601.06822 | 10.1016/j.nima.2026.171699 | 0 | 0 | false | null | Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment | 0 |
a5887c0d073eb4b88b0840bb6d1fabaa309cc5f7085e9ff6ac9d4305a11d7c5d | [
"arxiv",
"semantic_scholar"
] | Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models | Fine-tuning tabular foundation models (TFMs) under data scarcity is challenging, as early stopping on even scarcer validation data often fails to capture true generalization performance. We propose CausalMixFT, a method that enhances fine-tuning robustness and downstream performance by generating structurally consisten... | [
"Magnus BΓΌhler",
"Lennart Purucker",
"Frank Hutter"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-01-07T00:00:00 | https://arxiv.org/abs/2601.04110 | https://arxiv.org/pdf/2601.04110v2 | 2601.04110 | 10.48550/arXiv.2601.04110 | 2 | 0 | false | null | arXiv.org | 0.1193 |
1d489b88b9487b84692966ccad37f63bfe42f64744bea28ac3d91de5452e31a2 | [
"arxiv",
"semantic_scholar"
] | Towards Compositional Generalization of LLMs via Skill Taxonomy Guided Data Synthesis | Large Language Models (LLMs) and agent-based systems often struggle with compositional generalization due to a data bottleneck in which complex skill combinations follow a long-tailed, power-law distribution, limiting both instruction-following performance and generalization in agent-centric tasks. To address this chal... | [
"Yifan Wei",
"Li Du",
"Xiaoyan Yu",
"Yang Feng",
"Angsheng Li"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-07T00:00:00 | https://arxiv.org/abs/2601.03676 | https://arxiv.org/pdf/2601.03676v1 | 2601.03676 | 10.48550/arXiv.2601.03676 | 0 | 0 | true | https://github.com/weiyifan1023/STEPS | arXiv.org | 0 |
663a830d4e987480f0b6e13a00d9108fa65762116ff30fda55d25986ea7f1b0c | [
"arxiv",
"semantic_scholar"
] | AIS-CycleGen: A CycleGAN-Based Framework for High-Fidelity Synthetic AIS Data Generation and Augmentation | Automatic Identification System (AIS) data are vital for maritime domain awareness, yet they often suffer from domain shifts, data sparsity, and class imbalance, which hinder the performance of predictive models. In this paper, we propose a robust data augmentation method, AISCycleGen, based on Cycle-Consistent Generat... | [
"SM Ashfaq uz Zaman",
"Faizan Qamar",
"Masnizah Mohd",
"Nur Hanis Sabrina Suhaimi",
"Amith Khandakar"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-04T00:00:00 | https://arxiv.org/abs/2601.06127 | https://arxiv.org/pdf/2601.06127v1 | 2601.06127 | 10.48550/arXiv.2601.06127 | 0 | 0 | false | null | arXiv.org | 0 |
be9284933f3b32bae7eb1768b56422ff717008f5d271beb71847080b5b4d8d97 | [
"arxiv",
"semantic_scholar"
] | Exploring the Heterogeneity of Tabular Data: A Diversity-aware Data Generator via LLMs | Tabular data generation has become increasingly essential for enabling robust machine learning applications, which require large-scale, high-quality data. Existing solutions leverage generative models to learn original data distributions. However, real-world data are naturally heterogeneous with diverse distributions, ... | [
"Yafeng Tang",
"Xiaoou Ding",
"Jianzhuo Du",
"Zishuo Yan",
"Zhuang Ma",
"Zheng Liang",
"Zekai Qian",
"Hongzhi Wang"
] | [
"cs.LG",
"cs.DB"
] | [
"Computer Science"
] | 2025-12-26T00:00:00 | https://arxiv.org/abs/2512.21915 | https://arxiv.org/pdf/2512.21915v1 | 2512.21915 | 10.48550/arXiv.2512.21915 | 0 | 0 | true | https://github.com/windblow32/DATE | arXiv.org | 0 |
fd32c417ab649a9da7980ced39cead11c6a7dad28fe9bd1345a0b204f7227532 | [
"arxiv",
"semantic_scholar"
] | Data relativistic uncertainty framework for low-illumination anime scenery image enhancement | By contrast with the prevailing works of low-light enhancement in natural images and videos, this study copes with the low-illumination quality degradation in anime scenery images to bridge the domain gap. For such an underexplored enhancement task, we first curate images from various sources and construct an unpaired ... | [
"Yiquan Gao",
"John See"
] | [
"cs.CV",
"cs.LG",
"cs.MM"
] | [
"Computer Science"
] | 2025-12-26T00:00:00 | https://arxiv.org/abs/2512.21944 | https://arxiv.org/pdf/2512.21944v3 | 2512.21944 | 10.48550/arXiv.2512.21944 | 0 | 0 | true | null | arXiv.org | 0 |
d909f2ba13cc906b98e738f398b1a8a6bd3f503c97143f5d0188ee7682ea48db | [
"arxiv",
"semantic_scholar"
] | Synthetic Financial Data Generation for Enhanced Financial Modelling | Data scarcity and confidentiality in finance often impede model development and robust testing. This paper presents a unified multi-criteria evaluation framework for synthetic financial data and applies it to three representative generative paradigms: the statistical ARIMA-GARCH baseline, Variational Autoencoders (VAEs... | [
"Christophe D. Hounwanou",
"Yae Ulrich Gaba",
"Pierre Ntakirutimana"
] | [
"cs.LG",
"q-fin.CP"
] | [
"Computer Science",
"Economics"
] | 2025-12-25T00:00:00 | https://arxiv.org/abs/2512.21791 | https://arxiv.org/pdf/2512.21791v1 | 2512.21791 | 10.48550/arXiv.2512.21791 | 0 | 0 | false | null | arXiv.org | 0 |
6ec908c4f5973cf68452cff992d73cec5364457c75b06e46e42cba5f88e54d26 | [
"arxiv",
"semantic_scholar"
] | Deep Generative Models for Synthetic Financial Data: Applications to Portfolio and Risk Modeling | Synthetic financial data provides a practical solution to the privacy, accessibility, and reproducibility challenges that often constrain empirical research in quantitative finance. This paper investigates the use of deep generative models, specifically Time-series Generative Adversarial Networks (TimeGAN) and Variatio... | [
"Christophe D. Hounwanou",
"Yae Ulrich Gaba"
] | [
"q-fin.ST",
"cs.AI"
] | [
"Computer Science",
"Economics"
] | 2025-12-25T00:00:00 | https://arxiv.org/abs/2512.21798 | https://arxiv.org/pdf/2512.21798v2 | 2512.21798 | 10.48550/arXiv.2512.21798 | 0 | 0 | false | null | arXiv.org | 0 |
0d35c83b6a241c398a574308cc8e50f3eece198e82581cef63354bdde00b4d74 | [
"arxiv",
"semantic_scholar"
] | Generative Spatiotemporal Data Augmentation | We explore spatiotemporal data augmentation using video foundation models to diversify both camera viewpoints and scene dynamics. Unlike existing approaches based on simple geometric transforms or appearance perturbations, our method leverages off-the-shelf video diffusion models to generate realistic 3D spatial and te... | [
"Jinfan Zhou",
"Lixin Luo",
"Sungmin Eum",
"Heesung Kwon",
"Jeong Joon Park"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-12-14T00:00:00 | https://arxiv.org/abs/2512.12508 | https://arxiv.org/pdf/2512.12508v1 | 2512.12508 | 10.48550/arXiv.2512.12508 | 1 | 0 | false | null | arXiv.org | 0.0753 |
b1b97843d559f8da40570f457fe225c23225e32e198a248e47818df093221729 | [
"arxiv",
"semantic_scholar"
] | Improving Translation Quality by Selecting Better Data for LLM Fine-Tuning: A Comparative Analysis | We investigated the impact of data selection on machine translation fine-tuning for open LLMs. Using Japanese-English corpora, we compare five selectors: TF-IDF, COMET Kiwi, QuRate, FD-Score, and random selection, under controlled training conditions. We observed that semantic selectors consistently outperform lexical ... | [
"Felipe Ribeiro Fujita de Mello",
"Hideyuki Takada"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-12-12T00:00:00 | https://arxiv.org/abs/2512.11388 | https://arxiv.org/pdf/2512.11388v1 | 2512.11388 | 10.1109/BigData66926.2025.11402145 | 0 | 0 | false | null | BigData Congress [Services Society] | 0 |
d2be2167e4f89152383af2c4a58a9563713f8485fe7bc5d21fe73862d9c18f6f | [
"arxiv",
"semantic_scholar"
] | A Conditional Generative Framework for Synthetic Data Augmentation in Segmenting Thin and Elongated Structures in Biological Images | Thin and elongated filamentous structures, such as microtubules and actin filaments, often play important roles in biological systems. Segmenting these filaments in biological images is a fundamental step for quantitative analysis. Recent advances in deep learning have significantly improved the performance of filament... | [
"Yi Liu",
"Yichi Zhang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-12-11T00:00:00 | https://arxiv.org/abs/2512.10334 | https://arxiv.org/pdf/2512.10334v3 | 2512.10334 | 10.1109/ACDSA67686.2026.11467901 | 0 | 0 | false | null | null | 0 |
edced3f56bdd8c0ee1b787a0f65a7142123c1c12557adc2e583aa85eb7438a27 | [
"arxiv",
"semantic_scholar"
] | Geometric Data Science | This book introduces the new research area of Geometric Data Science, where data can represent any real objects through geometric measurements. The first part of the book focuses on finite point sets. The most important result is a complete and continuous classification of all finite clouds of unordered points under ri... | [
"Olga D Anosova",
"Vitaliy A Kurlin"
] | [
"math.MG",
"cond-mat.mtrl-sci",
"cs.CG"
] | [
"Mathematics",
"Physics",
"Computer Science"
] | 2025-12-04T00:00:00 | https://arxiv.org/abs/2512.05040 | https://arxiv.org/pdf/2512.05040v1 | 2512.05040 | 10.48550/arXiv.2512.05040 | 0 | 0 | false | null | arXiv.org | 0 |
5e1745291926c4aa4265b37c593a8649e6d340e5fe278290067df8fa49c22fc7 | [
"arxiv",
"semantic_scholar"
] | MechDetect: Detecting Data-Dependent Errors | Data quality monitoring is a core challenge in modern information processing systems. While many approaches to detect data errors or shifts have been proposed, few studies investigate the mechanisms governing error generation. We argue that knowing how errors were generated can be key to tracing and fixing them. In thi... | [
"Philipp Jung",
"Nicholas Chandler",
"Sebastian JΓ€ger",
"Felix Biessmann"
] | [
"cs.LG",
"cs.DB",
"cs.IR"
] | [
"Computer Science"
] | 2025-12-03T00:00:00 | https://arxiv.org/abs/2512.04138 | https://arxiv.org/pdf/2512.04138v1 | 2512.04138 | 10.1109/DSIS67228.2025.11390600 | 0 | 0 | false | null | null | 0 |
076eb10c9b44e8d3f571c13281929d29efecb57f25b088538eda6c796a8beae9 | [
"arxiv",
"semantic_scholar"
] | Robust Tabular Foundation Models | The development of tabular foundation models (TFMs) has accelerated in recent years, showing strong potential to outperform traditional ML methods for structured data. A key finding is that TFMs can be pretrained entirely on synthetic datasets, opening opportunities to design data generators that encourage desirable mo... | [
"Matthew Peroni",
"Franck Le",
"Vadim Sheinin"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-02T00:00:00 | https://arxiv.org/abs/2512.03307 | https://arxiv.org/pdf/2512.03307v1 | 2512.03307 | 10.48550/arXiv.2512.03307 | 1 | 0 | false | null | arXiv.org | 0.0753 |
dd1acc7a5efb4a19021e0fc44397ed8275b7d5aebae0d64784c8cf56b3821ae7 | [
"arxiv",
"semantic_scholar"
] | Fast Gaussian Process Approximations for Autocorrelated Data | This paper is concerned with the problem of how to speed up computation for Gaussian process models trained on autocorrelated data. The Gaussian process model is a powerful tool commonly used in nonlinear regression applications. Standard regression modeling assumes random samples and an independently, identically dist... | [
"Ahmadreza Chokhachian",
"Matthias Katzfuss",
"Yu Ding"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-12-02T00:00:00 | https://arxiv.org/abs/2512.02925 | https://arxiv.org/pdf/2512.02925v1 | 2512.02925 | 10.1287/ijds.2025.0087 | 1 | 0 | false | null | INFORMS Journal on Data Science | 0.0753 |
259aefb8dbfcdd572c54998f8d322270d63ee4630c2347272f5f5478933844a5 | [
"arxiv",
"semantic_scholar"
] | Challenges of Heterogeneity in Big Data: A Comparative Study of Classification in Large-Scale Structured and Unstructured Domains | This study analyzes the impact of heterogeneity ("Variety") in Big Data by comparing classification strategies across structured (Epsilon) and unstructured (Rest-Mex, IMDB) domains. A dual methodology was implemented: evolutionary and Bayesian hyperparameter optimization (Genetic Algorithms, Optuna) in Python for numer... | [
"GonzΓ‘lez Trigueros JesΓΊs Eduardo",
"Alonso SΓ‘nchez Alejandro",
"MuΓ±oz Rivera Emilio",
"PeΓ±arΓ‘n Prieto Mariana Jaqueline",
"Mendoza GonzΓ‘lez Camila Natalia"
] | [
"cs.LG",
"cs.CL",
"cs.DC"
] | [
"Computer Science"
] | 2025-11-29T00:00:00 | https://arxiv.org/abs/2512.00298 | https://arxiv.org/pdf/2512.00298v1 | 2512.00298 | 10.48550/arXiv.2512.00298 | 0 | 0 | false | null | arXiv.org | 0 |
4b9e89c25ae90f37cf0a6c013a4e7b0fa267ea762e71e5b3f4e257f4bddbdab2 | [
"arxiv",
"semantic_scholar"
] | Robust Spectral Watermark for Synthetic Tabular Data | The rise of generative AI has enabled the production of high-fidelity synthetic tabular data across fields such as healthcare, finance, and public policy, raising growing concerns about data provenance and misuse. Watermarking offers a promising solution to address these concerns by ensuring the traceability of synthet... | [
"Yizhou Zhao",
"Xiang Li",
"Peter Song",
"Qi Long",
"Weijie Su"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2025-11-26T00:00:00 | https://arxiv.org/abs/2511.21600 | https://arxiv.org/pdf/2511.21600v2 | 2511.21600 | null | 0 | 0 | false | null | null | 0 |
a220fbc00a09cdb67dba3511070e87191da6a21781f9b9e2bc7247f84d2065a1 | [
"arxiv",
"semantic_scholar"
] | Data-Driven Assessment of Concrete Slab Integrity via Impact-Echo Signals and Neural Networks | Subsurface defects such as delamination, voids, and honeycombing critically affect the durability of concrete bridge decks but are difficult to detect reliably using visual inspection or manual sounding. This paper presents a machine learning based Impact Echo (IE) framework that automates both defect localization and ... | [
"Yeswanth Ravichandran",
"Duoduo Liao",
"Charan Teja Kurakula"
] | [
"eess.SP",
"cs.AI",
"cs.LG"
] | [
"Computer Science",
"Engineering"
] | 2025-11-26T00:00:00 | https://arxiv.org/abs/2511.21080 | https://arxiv.org/pdf/2511.21080v1 | 2511.21080 | 10.1109/BigData66926.2025.11402462 | 1 | 0 | false | null | BigData Congress [Services Society] | 0.0753 |
53c9efdf73f8fb6917076b9089aa1583aaa33520ca3b050cb967cd8e997ae3f3 | [
"arxiv"
] | A review on data fusion in multimodal learning analytics and educational data mining | The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and... | [
"Wilson Chango",
"Juan A. Lara",
"Rebeca Cerezo",
"CristΓ³bal Romero"
] | [
"cs.CY",
"cs.LG"
] | [] | 2025-11-25T00:00:00 | https://arxiv.org/abs/2511.20871 | https://arxiv.org/pdf/2511.20871v1 | 2511.20871 | null | 0 | 0 | false | null | WIREs Data Mining and Knowledge Discovery, 12(4), e1458 (2022) | 0 |
42e8e740a42ca8fb0d0fb0703ef6c2ab07d6fcb8f28bc4efd854ecd0dddb484b | [
"arxiv",
"semantic_scholar"
] | Post-Pruning Accuracy Recovery via Data-Free Knowledge Distillation | Model pruning is a widely adopted technique to reduce the computational complexity and memory footprint of Deep Neural Networks (DNNs). However, global unstructured pruning often leads to significant degradation in accuracy, typically necessitating fine-tuning on the original training dataset to recover performance. In... | [
"Chinmay Tripurwar",
"Utkarsh Maurya",
" Dishant"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-24T00:00:00 | https://arxiv.org/abs/2511.20702 | https://arxiv.org/pdf/2511.20702v1 | 2511.20702 | 10.48550/arXiv.2511.20702 | 0 | 0 | false | null | arXiv.org | 0 |
58359336632406d7ed0d19935b76739e35f182764b04a32d8146d2ecdf2b466b | [
"arxiv",
"semantic_scholar"
] | An Interpretability-Guided Framework for Responsible Synthetic Data Generation in Emotional Text | Emotion recognition from social media is critical for understanding public sentiment, but accessing training data has become prohibitively expensive due to escalating API costs and platform restrictions. We introduce an interpretability-guided framework where Shapley Additive Explanations (SHAP) provide principled guid... | [
"Paula Joy B. Martinez",
"Jose Marie Antonio MiΓ±oza",
"Sebastian C. IbaΓ±ez"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-11-20T00:00:00 | https://arxiv.org/abs/2511.16132 | https://arxiv.org/pdf/2511.16132v1 | 2511.16132 | 10.48550/arXiv.2511.16132 | 0 | 0 | false | null | arXiv.org | 0 |
8ab06786b6866d7ac54912fc3d7ba3f8064447e79a6f49a5f036f5813d3b2b4d | [
"arxiv",
"semantic_scholar"
] | Oversampling techniques for predicting COVID-19 patient length of stay | COVID-19 is a respiratory disease that caused a global pandemic in 2019. It is highly infectious and has the following symptoms: fever or chills, cough, shortness of breath, fatigue, muscle or body aches, headache, the new loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, and diarrhea. ... | [
"Zachariah Farahany",
"Jiawei Wu",
"K M Sajjadul Islam",
"Praveen Madiraju"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-11-19T00:00:00 | https://arxiv.org/abs/2511.15048 | https://arxiv.org/pdf/2511.15048v1 | 2511.15048 | 10.1109/BigData55660.2022.10020253 | 4 | 0 | false | null | 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 17-20 December 2022 | 0.1747 |
End of preview. Expand in Data Studio
Synthetic Data Papers β FineSet
A research-paper dataset on Synthetic Data Papers, assembled, deduplicated, and quality-scored by FineSet from arXiv and Semantic Scholar.
πΈ This is a dated snapshot β generated 2026-06-12. It is not auto-updated. Research on Synthetic Data Papers moves fast β new papers land on arXiv every week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. β
Why this dataset
- Quality-scored:
quality_scorefloat (0β1), citation-normalized β filter out the noise - Papers with code: 151 flagged via
has_codeβ find reproducible work fast - Deduplicated: arXiv + Semantic Scholar cross-referenced, duplicate records merged
- Clean JSONL: 738 records, one per line, normalized fields β no encoding garbage
Dataset details
- Records: 738
- Date range: 2022β2026
- Snapshot date: 2026-06-12 (frozen β see note above)
- Sources: arXiv, Semantic Scholar (cross-referenced, duplicates merged)
- arXiv categories: cs.LG, cs.CL
- Quality scoring: citation-normalized, 0β1 (p50=0.15, p90=0.369)
- Format: JSONL, one record per line
Fields
| Field | Type | Description |
|---|---|---|
| id | string | Deterministic SHA256 record id |
| sources | list | Which sources contributed (arxiv, semantic_scholar) |
| title | string | Paper title |
| abstract | string | Full abstract |
| authors | list | Author names |
| categories | list | arXiv category codes |
| fields_of_study | list | Semantic Scholar field tags |
| published_date | string | ISO 8601 date |
| url | string | arXiv abstract URL |
| pdf_url | string|null | Open-access PDF if available |
| arxiv_id | string|null | arXiv identifier |
| doi | string|null | DOI if available |
| citation_count | int | Citation count (Semantic Scholar) |
| influential_citation_count | int | Influential citations (Semantic Scholar) |
| has_code | bool | Code repo detected in the arXiv comment |
| code_url | string|null | GitHub URL if detected |
| venue | string|null | Publication venue |
| quality_score | float | 0β1, citation-normalized |
Quality score methodology
quality_score = min(1.0, log10(citation_count + 1) / 4)
A citation-normalized heuristic: 0 for uncited papers, ~0.5 at 100 citations, ~0.75 at 1,000, 1.0 at 10,000+. Useful for filtering training data by impact.
π Want this on YOUR topic, updated daily?
This snapshot is frozen at 2026-06-12. The live FineSet pipeline keeps a dataset like this refreshed every day on whatever topic you describe β new papers in, dedup and quality scoring automatic, export as JSONL/Parquet or push straight to the Hub.
Tell me the topic you'd want and I'll run the pipeline on it β open a discussion on this dataset, it's free and it's how I decide what to build next.
β fineset.io β describe what you want to train on, get a dataset. Early-access waitlist open (referral skip available).
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