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Synthetic Data357 papers Synthetic Data233 papers Synthetic Data Augmentation155 papers Synthetic Dataset54 papers Synthetic Tabular Data42 papers Synthetic Training Data33 papers

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Papers

Fara-1.5: Scalable Learning Environments for Computer Use Agents
Jun 18, 2026Ahmed Awadallah, Sahil Gupta, Yash Lara +12Computer-Use Agents Data Generation
Hybrid Variational Quantum Circuits for Multivariate Regression and High-Dimensional Data Reconstruction
Sep 15, 2026Koffi Ognandon Ayena, Frédéric Holweck, Serge Iovleff +1Variational Quantum Circuits Hybrid Quantum Neural Networks
When Does Text Inform? Benchmarking Information-Theoretic Metrics for Multimodal Time-Series Forecasting
Sep 12, 2026Emma Andrews, Gianmarco MengaldoSynthetic Time Series Information-Theoretic Limits
Using AI Agents to Automate Black-Box Audits of Personalization Algorithms at Scale
Jun 29, 2026Alessandro Morosini, Sarah H. Cen, Andrew Ilyas +3Algorithm Auditing Personalization
Insurance Pricing Optimization via Off-Policy Evaluation
May 27, 2026Sascha Günther, Dimitri Semenovich, Mario V. WüthrichOff-Policy Evaluation Prices
High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning
May 12, 2026Ricard Montalà, Bernat Font, Oriol Lehmkuhl +2Aerodynamics Iterative Reinforcement Learning
NAMD: Virtual Follow-up Computed Tomography Synthesis via Nodule-Aligned Multimodal Diffusion Models for Early Lung Cancer Diagnosis
Mar 16, 2026James Song, Yifan Wang, Chuan Zhou +1Lung-Nodule Detector Non-Small Cell Lung Cancer
Geometry-Preserving Neural Architectures on Manifolds with Boundary
Feb 3, 2026Karthik Elamvazhuthi, Shiba Biswal, Kian Rosenblum +4Manifolds Representational Geometry
A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets
Jun 25, 2026Santosh Kapuria, AbhishekStructural Health Monitoring Transfer Learning
ONTO: A Token-Efficient Columnar Notation for LLM Input Optimization
Apr 19, 2026Harshavardhanan DeekeswarPer-Token Latency LLM Inference Efficiency
Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies
Jun 29, 2026Youngseok Hwang, Sungho Bae, Dohun Lee +4Anomalous Frame Detection Maritime Navigation
Can Tabular In-Context Learners Generalize to Biomolecular Property Prediction?
Jun 30, 2026Davy Guan, Lu Zhang, Asiri Wijesinghe +7Molecular Property Prediction Tabular Foundation Models
ARC: Augmented-Rank Conformalization for Changepoint Localization --- Finite-Sample Validity and Distribution-Robust Efficiency
Aug 9, 2026Chenchen Peng, Mixia Wu, Qijing Yan +2Change-Point Detection Finite-Sample Coverage Guarantees
Evidence-based Decision Modeling for Synthetic Face Detection with Uncertainty-driven Active Learning
May 11, 2026Qingchao Jiang, Zhenxuan Hou, Zhiying Zhu +3Synthetic Image Detection Detection Performance
Context Window Failures in Relational Foundation Models
Aug 31, 2026Denis Oliveira Correa, Francisco Galuppo AzevedoRelational Tabular Foundation Models
Mitigating The Effect of Class Imbalance in Data with Hierarchical and Dependable Structure
Jul 13, 2026Bipin Chhetri, Deepika Giri, Avishek Kadel +2Extreme Class Imbalance Oversampling
An LMM for Precisely Grounding Elements in Documents
Jun 23, 2026Yijian Lu, Chuangxin Zhao, Kai Sun +3Visual Document Retrieval Visual Grounding
onepot-Bench 0: towards lab-aware in silico chemistry benchmarks
Aug 3, 2026Brandon Wang, Andrei S. Tyrin, Daniil A. BoikoVast Chemical Space
Dataset Distillation by Influence Matching
Jul 18, 2026Haoru Tan, Wang Wang, Sitong Wu +5Chain-Of-Thought Distillation Influence
HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization
Jul 15, 2026Abdullah Shaikh, Zain Naqi, Taha Zahid +2Large Language Model Reasoning Synthetic Training Data
Multiclass Linear Perceptrons with Multiplicative Margins
Aug 30, 2026Dmitri Rachkovskij, Evgeny Osipov, Olexander Volkov +2Multiclass Classification Bias Learning
Shaping Human-AI Interactions to Provide Improvement Pathways and Balance Competing Objectives
Aug 6, 2026Keziah NaggitaHuman-Ai Interaction Objectives
Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting
Jul 24, 2026Wan Zhang, Qinjie Lin, Chan Lee +3Time Series Mixformer
Machine Learning for Charge State Characterization of Isolated Double Quantum Dots
Jul 23, 2026Hyma Vallabhapurapu, Marco Candido, Krishna Choudhary +7Quantum Machine Learning Post-Quantum Cryptography
DynamicMem: A Long-Horizon Memory Benchmark in Real-World Settings
Jun 22, 2026Wenya Xie, Shengming Zhou, Zelin Li +9Long-Horizon Tasks Large Language Model Agents
Crossing the Validation Crisis: Cross-Validation Reduces Benchmarking Variance Surprisingly Well
Jun 10, 2026Célestin Eve, Gaël Varoquaux, Thomas MoreauCross Validation Variance Reduction
Causal Longitudinal Prior-Fitted Networks for Counterfactual Outcome Prediction
Jun 4, 2026Amirhossein Zare, Amirhessam Zare, Herlock Rahimi +2Causal Inferences Counterfactual Reasoning
PitchBench: Measuring Pitch Hearing in Audio-Language Models
May 25, 2026Milan Liessens Dujardin, Song-Ze Yu, Craver Corbyn Thomas-Smith +2Large Audio Language Models Pitch
An accurate nucleic acid-small molecule docking framework via geometric deep learning with large-scale pretraining
May 22, 2026Shi Li, Xujun Zhang, Mingquan Liu +5Structure-Based Drug Design Drug Design
Tippett-minimum Fusion of Representation-space Diffusion Models for Multi-Encoder Out-of-Distribution Detection
May 19, 2026Neelkamal BhuyanOut-Of-Distribution Detection Out-Of-Distribution

Jun 18, 2026 · cs.AIJ/K move · Enter open · S save

Fara-1.5: Scalable Learning Environments for Computer Use Agents

Ahmed Awadallah, Sahil Gupta, Yash Lara, Yadong Lu+11

Collecting computer use data from human demonstrations is expensive and slow, motivating the need for scalable generation strategies. This requires two key ingredients: environments in which agents can act and verifiers that can judge whether their demonstrations succeeded. We introduce FaraGen1.5, a scalable data pipeline for computer use agents composed of three modular components: environments, solvers, and verifiers. FaraGen1.5 uses both live websites and synthetic environments that faithfully simulate domains gated by authentication or that require irreversible actions. It employs a solver harness that can be powered by multiple models, including strong frontier models such as GPT-5.4, and also incorporates a user simulator to enable multi-turn rollouts. Finally, FaraGen1.5 scores the resulting trajectories with three complementary verifiers covering task correctness, efficiency, and critical-point adherence. Using data produced by this pipeline, we train Fara1.5, a family of native computer use agents (CUAs) at three scales built on Qwen3.5 (4B, 9B, and 27B). To train these models, we employ a supervised finetuning (SFT) recipe that carefully balances data from FaraGen1.5 for broad coverage, specific high-value tasks, and target model deficiencies in an iterative approach. Each model sets a new state of the art for its size class on browser-use benchmarks: Fara1.5-9B reaches 63.4% on Online-Mind2Web and 86.6% on WebVoyager, while Fara1.5-27B achieves 72.3% on Online-Mind2Web, which is competitive with much larger proprietary systems.

Computer-Use Agents Data Generation Environment Supervised Fine-Tuning

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