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28 papers in the last four weeks, up 180% on the four weeks before. 0.3% of all new papers.

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Scalable, Transferable Meta-network for Data Selection Requires a Different Loss (and Why the Obvious Choice is Problematic)
Oct 1, 2026Zilin Du, Bowen Yang, Boyang Albert LiNatural Language Datasets Training Data
College of Computing and Data Science Nanyang Technological University Singapore
SAGE: Similarity-Based Cleaning of Poisoned Training Data from Verified Examples
Oct 1, 2026Chaeeun Han, Soodeh Atefi, Yevgeniy Vorobeychik +1Poisoning Training Data
College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, USA · Department of Computer Science and Engineering, University of Louisville, Louisville, KY, USA · Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA
AutoGUIWorld: Image Generators as Visual World Models for GUI Agent
Oct 1, 2026Cheng Yang, Yifan Wu, Yutao Huang +18Visual Generation Action Generation
Hunyuan AI Data Team
Comparison of techniques for fine-tuning open-weight models for entity extraction from radiology reports
Sep 30, 2026Aawez Mansuri, Kush Mehta, Mohammadreza Chavoshi +10Radiology Report Generation Model Fine-Tuning
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA · Department of Computer Science, Emory University, Atlanta, GA, USA
Less is more: error-distance scaling relation for data-efficient kilometer-scale downscaling of extreme heat
Sep 30, 2026Ahmed Marey, Henry Lu, Abhishek Gaur +6Dynamical Downscaling Climate
Centre for Zero Energy Building Studies, Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, H3G 1M8 Canada · Building and Climate Interface, Construction Research Centre, National Research Council Canada, Ottawa, ON, K1A 0R6, Canada · Department of Architecture, School of Sciences and Engineering, The American University in Cairo, New Cairo 11835, Egypt +3
Source Identification Is Not Fitness Testing: Measuring the Limits of Synthetic-Data Attribution
Sep 30, 2026Joss ArmstrongTraining Data Physical Activities
Ericsson, Athlone, Ireland
Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning
Sep 30, 2026Muzaffer Citir, Hiroki Nishikawa, Sangyoung ParkFederated Learning Training Data
Smart Mobility Systems, Technical University of Berlin, Germany · Graduate School of Information Science and Technology, The University of Osaka, Japan
After a Decade: Bringing Shadow Removal into the Real World with Agentic Training Data
Sep 29, 2026Shilin Hu, Jingyi Xu, Dimitris Samaras +1Diverse Occlusion-And-Revelation Scenarios Training Data
Department of Computer Science Stony Brook University · Department of Computer Science University of North Carolina at Charlotte
Memorize, Adapt, Ignore: Diagnosing Robot Learning Mechanisms under Training Data Variation
Sep 29, 2026Ke Zhang, Danica J. Sutherland, Chao LiuScalable Robot Learning Imitation Learning
PRIME Robotics Lab, Department of Mechanical Engineering, The University of British Columbia · Department of Computer Science, University of British Columbia · Alberta Machine Intelligence Institute
It's All Training: A Fully Synthetic Single-Stage Recipe for LLMs
Sep 29, 2026Pierre-Carl Langlais, Pieter Delobelle, Yannick Detrois +7Synthetic Training Data Synthetic Data
PleIAs · Sorbonne Center for Artificial Intelligence · Sciences Po Médialab +6
How Many Labels Does a Language Need? Annotation Budgets and Cross-Lingual Pooling for African-Language Text Classification
Sep 29, 2026Bhanu Prakash Vangala, Sowmya Guda, Navya VangalaAfrican Languages Large Language Model Classification
University of Missouri · Amar Bio Tech Pvt Ltd
Recursive LLM Degradation in Biomedical Question Answering: A Cross-Generation Study
Sep 28, 2026Bibek Bhandari, Kshitij LingthepBiomedical Text Synthetic Data
Independent Researcher Memphis, Tennessee, USA
Curating Merchant-Matching Training Data with Two Confidence-Gated Local LLM Judges
Sep 27, 2026Donghao Huang, Jinling Pei, Zhaoxia WangJudges Training Data
Research and Development, Mastercard, Arlington, VA, USA · School of Computing and Information Systems, Singapore Management University, Singapore
ShapeLex: Decoupling Local Shape Symbolization and Global Scale Modeling for Text-Controlled Time Series Generation
Sep 21, 2026Subo Wei, Jianqi Gao, Mingyan Fan +3Time-Series Generation Text Generation
School of Computer Engineering and Science, Shanghai University, Shanghai, China · Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China
Alignment Forecasting: Predicting Misalignment From Training Data
Sep 19, 2026Chen Yueh-Han, Bruce W. Lee, Ilia Sucholutsky +1Large Language Model Alignment Misalignment Persona
NYU, MATS · Independent · NYU +1
TouchSight: Bare-Handed Tactile Prediction from Egocentric Video via Generative Visual Augmentation
Sep 17, 2026Danyan Zhou, Jinxuan Lu, Jiawei Lin +3Tactile Egocentric Vision
THU · Xspark AI · HKUST (GZ) +1
QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization
Sep 17, 2026Yujie Li, Zezhi Shao, Chengqing Yu +7Zero-Shot Forecasting Pretraining
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · School of Data Science and Engineering, East China Normal University, Shanghai, China
OPEN-1B: A Fully Auditable Training Run
Sep 15, 2026John Donaghy, Brian Wilcox, Oğuzhan Ersoy +6Reproducibility Training Data
Gensyn
ReMova: Fine-tuning LLMs for English to Belarusian translation
Sep 14, 2026Mikita Pilinka, Aliaksandr Kliujeŭ, David Samuel +1Neural Machine Translation Large Language Model Fine-Tuning
University of Oslo Department of Informatics · Independent Researcher
Failure-Guided Co-Evolution of Prompts and Training Data
Sep 14, 2026Tianyu Yuan, Zhuzhong QianAutomatic Prompt Optimization Training Data
Nanjing University
SynthSentry: Detecting Synthetic Data Contamination in Language Model Training Data
Sep 14, 2026Praveen Kumar Myakala, Ravichandra Namburi, Sowmya Keragodu Jayaramu +1Synthetic Data Training Data
Independent AI Researcher Melissa, USA · Independent Researcher Frisco, USA · Independent Researcher Austin, USA
Combining Synthetic and Real Data for Low-Resource Historical OCR: A Manchu Case Study
Sep 11, 2026Yan Hon Michael Chung, Hanlin WangOptical Character Recognition Historical Manuscripts
Division of Humanities, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR · The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR
Empirical Evaluation of Data Poisoning Attacks in Supervised Learning
Sep 10, 2026Toshif Khan, Muhammad AbusaqerPoisoning Training Data
Minot State University · Department of Math, Data & Technology Minot State University Minot, ND, 58707 · Trustworthy Language Intelligence Lab, Minot State University
Detectable Only Where It Is Confounded: What Verified Duplication Counts Say About Membership Evidence in Language Models
Sep 9, 2026Arman Nik KhahMembership Inference Repetition
The University of Texas at Dallas
ActReview: Rebuttal-Guided Training Data and Rubric Rewards for Actionable Peer Review Generation
Sep 8, 2026Yiling Ma, Yilun Zhao, Sihong Wu +3Peer Review Reviewer
Yale University · University of Chicago · TCS Research
Compensating for Scarce Historical Images in Cross-Domain Cultural Heritage Retrieval Using Synthetic Aging
Sep 8, 2026Marcin Iwanowski, Adam Mazgaj, Ferdynand Gorski +1Synthetic-To-Real Domain Gap Cultural Heritage
Inst.of Engineering and Technology, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, ul.Grudziądzka 5, 87-100 Toruń, POLAND · Clemens, ul.Pawlikowskiego 10/2, 31-127 Krakow, POLAND · cClemens, ul.Pawlikowskiego 10/2, 31-127 Krakow, POLAND +1
zScore-N: A Neural Network for On-Chain Wallet Reputation Scoring
Sep 8, 2026Girish G N, Ashutosh Sahoo, Akshay SP +2Reputation Scores
Zeru AI
Attributing Cohen's d: Training Data Attribution for Disease-Related Effects in Normative Age Biomarkers
Sep 7, 2026Jakob Snel, Marc-Andre SchulzVascular Age Cohort
Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany · Tübingen AI Center, University of Tübingen, Tübingen, Germany · Department of Psychiatry and Neurosciences, Charité – Universitätsmedizin Berlin, Berlin, Germany +1
From Zero to Hero: An Open LLM Ecosystem for Armenian
Sep 3, 2026Erik Arakelyan, Khatun Avetisyan, Meri Davtyan +5Large Language Model Tool Use Text Corpora
1NVIDIA · 2COPA
Tail-Likelihood Reinforcement Learning
Sep 2, 2026Shrinivas Ramasubramanian, Daman Arora, Fahim Tajwar +11Offline Reinforcement Learning Training Data
Carnegie Mellon University · Together AI · University of California, Berkeley +2
Test-Time Logit Prompting for Source-Free Missing Modality Adaptation
Sep 2, 2026Taixi Chen, Nancy GuoVision-Language Model Adaptation Logit Lens
School of Computing State University of New York at Binghamton
Probing Factual Knowledge Transfer with Training Data Interventions
Sep 1, 2026Romina Oji, Marc Braun, Marcel Bollmann +2Multilingual Language Models Large Language Model Pretraining
Department of Computer and Information Science Linköping University
LLM Post-Training as Brownfield Maintenance: An Industrial Perspective on Dataware Engineering
Aug 31, 2026Gopi Krishnan Rajbahadur, Amir M. Ebrahimi, Boyuan Chen +1Post-Training Training Data
Queen’s University, Canada · Centre for Software Excellence, Huawei Technologies, Canada
Estimating Population-Risk Curves Along Nonconvex Gradient Flows from the Training Sample
Aug 31, 2026Mingzhi SongWasserstein Gradient Flows Gradient
Department of Mathematics, The University of Hong Kong
Transformer-Based Flow Shop Scheduling Using MILP-Generated Training Data
Aug 30, 2026Roderich WallrathJob Shop Scheduling Operations Research
University of Twente, Faculty of Science and Technology, Sustainable Process Technology, Process Design and Optimization, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
Learned, Then Lost: A Measured Single-Example Counterfactual in Pre-training
Aug 19, 2026Zachary Speck, Asa ShepardTraining Data Batch
Arizona State University · Williams College
Measuring Task-Agnostic Training Data Influence Across Language Model Pretraining
Aug 13, 2026Yuto Nishida, Hirokazu Kiyomaru, Yusuke Oda +6Large Language Model Pretraining Training Data
Nara Institute of Science and Technology · NII LLMC · Waseda University +3
Agentic Instruction Data Selection: Let DataMaster Interpret Your Intent
Aug 11, 2026Fanqi Zhou, Qiaosheng Chen, Zixian Huang +1Alignment-Aware Selection Data Science Agents
State Key Laboratory of Novel Software Technology, Nanjing University · Shanghai Artificial Intelligence Laboratory
TokenPrint: A Calibrated Token-Space Fingerprint for Language-Model Provenance
Aug 8, 2026Yuqi Wu, Shengming Zhao, Jie ChenLanguage Modeling Fingerprint
College of Biomedical Engineering, Fudan University
Vorch-IR: Long-Form Unified Multimodal Identity Replacement Video Generation
Aug 6, 2026Yaole Wang, Xiaoyu Chen, Xin Ma +5Vision-Language Foundation Models Fidelity
Vorch Team · Fudan University
Meta-Task: Turning Terminal Task Synthesis into a Terminal Task for Scalable Agent Training
Jul 30, 2026Zhihong Pan, Jiyuan He, Kai Zhang +5Synthetic Task Agentic Benchmarks
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China · Meituan
SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement
Jul 21, 2026Arther Tian, Alex Ding, Simon Wu +1Gating Alignment-Aware Selection
DGrid AI
Moving Alphabet: A Controlled Study of Training Data for Text-to-Video Generation
Jul 21, 2026Amber Yijia Zheng, Lu Liu, Raymond A. Yeh +1Text-To-Video Generation Model Video Dataset
Meta Superintelligence Labs · Purdue University · Work done during an internship at Meta Superintelligence Labs
PPL-Factory: Task-Aware and Budget-Aware Data Selection from Language Modeling to Reasoning
Jul 20, 2026Hang Zhang, Warren J. GrossLarge Language Model Fine-Tuning Language Modeling
Department of Electrical and Computer Engineering, McGill University Montreal, QC, Canada
Bridging the Information Gap: Semantic Densification and Hindsight Distillation for Cold-Start Prediction
Jul 19, 2026Hao Duong Le, Yifei Gao, Huan Li +4Cold-Start Dataset Distillation
Tsinghua University Beijing, China · Meituan Beijing, China
Neural spectroscopy of AlphaFold2 reveals encoded protein conformational landscapes
Jul 17, 2026Kaustav MehtaConformational Ensembles Protein
Independent researcher, India
Analytical study of the optimal combination of binary classifiers based on classifiers-induced partitioning of the training set
Jul 16, 2026Jean-Marc Brossier, Olivier LafitteClassifier Empirical Risk Minimization
CNRS, Univ. Grenoble Alpes, Grenoble-INP, GIPSA-lab, Grenoble, France. · Universit´e Sorbonne Paris Nord, LAGA, UMR 7539. IRL CNRS-CRM 3457. Universit´e de Montr´eal. Canada.
Tracing LLM Behavior to the Training Data with Empirical Next-Token Distributions
Jul 15, 2026Zachary IzzoNext-Token Distribution Mechanistic Interpretability
NEC Labs America
Reducing information dependency does not cause training data privacy. Adversarially non-robust features do
Jul 14, 2026Rasmus Torp, Shailen K. Smith, Adam BreuerPrivacy Training Data
Department of Computer Science Dartmouth College · Department of Computer Science & Department of Government Dartmouth College
Data Safety: Synthetic Data Quality Analysis Using CIFAKE Dataset
Jul 13, 2026Kuniko Paxton, Amila Akagić, Koorosh Aslansefat +2Synthetic Image Detection Synthetic Data
School of Digital and Physical Science, University of Hull, Cottingham Road, Hull, HU6 7RX, United Kingdom · Faculty of Electrical Engineering, University of Sarajevo, Zmaja od Bosne bb, Sarajevo, 71000, Bosnia and Herzegovina
Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data
Jul 13, 2026Shikai Qiu, Marc Finzi, Yujia Zheng +2Data Compression Methods Codebooks
New York University · Carnegie Mellon University
What You Train Is What You Get: Gender Bias, Training Composition, and Post-Hoc Mitigation in Audio Deepfake Detection
Jul 10, 2026Aishwarya R. Fursule, Vamshi Nallaguntla, Shruti Kshirsagar +1Audio Deepfake Detection Audio Understanding
School of Computing, Wichita State University, Wichita, KS, USA · Institut national de la recherche scientifique (INRS–EMT), Montreal, QC, Canada · INRS-UQO Mixed Research Unit on Cybersecurity, Gatineau, QC, Canada
FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation
Jul 8, 2026Chongkai Li, Bang Zhang, Wenjian LuoFederated Learning Training Data
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Institute of Cyberspace Security, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
Selective Left-Shift: Turning Test-Time Compute and Difficulty-based Curation into Training Data for Low-Resource Code Generation
Jul 8, 2026Didula Samaraweera, Anjana Supun, Srinath PereraCode Generation Small Large Language Models
WSO2 Santa Clara, CA, USA
TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios
Jul 7, 2026Hong Lyu, Mingru Yang, Qianhua He +3Training Data
School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
A $^{2}$ utoLPBench: An Auto-Generated, Agent-Friendly LP Benchmark via Inverse-KKT Construction
Jul 2, 2026Shuo Ren, Yaohui Han, Yifan Shi +6Agentic Benchmarks Large Language Model Benchmarks
The Chinese University of Hong Kong
WARP: Weight-Space Analysis for Recovering Training Data Portfolios
Jul 2, 2026Tzu-Heng Huang, Aditya Goyal, John Cooper +1Weight-Space Training Data
University of Wisconsin-Madison
UT-AISTimprt submission for ICME 2026 Grand Challenge on Academic Text-to-Music Generation
Jul 2, 2026Shunsuke Yoshida, Yu-Hua Chen, Satoru FukayamaText-To-Music Training Data
University of Tokyo, Japan · National Institute of Advanced Industrial Science and Technology (AIST), Japan
How to Allocate Your Tokens? Scaling Laws with Training Steps and Batch Size
Jul 1, 2026Fabian SchaippScaling Laws Batch
Inria, ´Ecole Normale Sup´erieure, PSL Research University, Paris
Watermarking for Proprietary Dataset Protection
Jul 1, 2026John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura +1Membership Inference Watermarking
University of Maryland · 2Lawrence Livermore National Labs.

Oct 1, 2026, cs.CLJ/K move · Enter open · S save

Scalable, Transferable Meta-network for Data Selection Requires a Different Loss (and Why the Obvious Choice is Problematic)

Zilin Du, Bowen Yang, Boyang Albert Li

College of Computing and Data Science Nanyang Technological University Singapore

Natural Language Datasets Training Data Choice Multivariate Time-Series

Open paper AskPDF arXiv

Data selection is critical for training large language models on massive and heterogeneous corpora. Meta-learning for Training-data Selection offers a principled alternative to heuristic scoring by learning data weights from a target validation objective, but existing methods face a trade-off between fine-grained valuation and transferability to unseen data. A natural solution is to replace per-sample weights with a selection network. However, we find that directly incorporating such a network into existing MTS objectives leads to unstable optimization and poor generalization, caused by weight suppression and persistent reliance on easy-to-learn features. To address these issues, we propose Transferable Example Scoring and Selection (TESS), a scalable data-selection framework built on a Pointwise Value Matching objective (PVM). Experiments on LLM safety and targeted instruction tuning demonstrate strong transfer across datasets, from subsets to full corpora, and from smaller to larger models.

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