Synthetic data

Timeline

1. Oct 8, 2026 · Research paper · 1 source
   Marformer: A Transformer for Predicting Missing Data Distributions
   We present the Marformer, a Transformer trained to directly predict conditional marginals given any set of observed values.
2. Oct 8, 2026 · Research paper · 1 source
   RIFT: Relative Isolation From Trees For Anomaly Detection
   Inspired by the geometric interpretation of this formula, we introduce RIFT (Relative Isolation From Trees), a deterministic anomaly detection method that generates the minimum spanning tree and scores each point by the sum of the apparent sizes of tree edges as viewed from that point.
3. Oct 8, 2026 · Research paper · 1 source
   Is Real-World Training Data Necessary for Generalist Graph Anomaly Detection?
   Generalist graph anomaly detection (GAD) aims to build a foundation model that detects anomalies on arbitrary unseen graphs without retraining or fine-tuning.
4. Oct 8, 2026 · Research paper · 1 source
   Leveraging Human-In-The-Loop Demonstrations in Reinforcement Learning for Digital Twin-Driven Robot Flexibility
   We propose a human-in-the-loop online training framework combining a digital twin (DT), reinforcement learning (RL), and human demonstrations.
5. Oct 8, 2026 · Research paper · 1 source
   Adapting English Quality Classifiers for Multilingual LLM Pretraining Data Selection
   Recent advances in large language model (LLM) pretraining highlight the role of high-quality training data in improving performance.
6. Oct 8, 2026 · Research paper · 1 source
   Embedding-Bias in Conditional Independence Testing
   To test conditional independence of $X$ and $Y$ given a text or an image $Z$, one conditions on an embedding $ψ(Z)$ in place of $Z$.
7. Oct 8, 2026 · Research paper · 1 source
   Conditional Transfer from Controlled Pretraining Mixtures to Code
   Synthetic tasks are increasingly used both as probes of language-model capability and as pretraining data.
8. Oct 8, 2026 · Research paper · 1 source
   Beyond Distributional Fidelity: Causal-Penalized Diffusion for Synthetic Tabular Data
   In this paper, we study whether causal fidelity can be improved directly within a fully generative tabular model.
9. Oct 8, 2026 · Research paper · 1 source
   Adaptive Adversarial Augmentation for Controllable Face Synthesis
   We propose Ensemble Feedback Controllable Synthesis (EFCS), a guided framework that generates diverse and challenging samples while preserving visual realism.
10. Oct 8, 2026 · Research paper · 1 source
    SynCo: Data Synthesis Co-Training for Self-Evolving LLMs via Multi-Agent Reinforcement Learning
    To address this problem, we propose SynCo, an agentic data synthesis co-training framework for self-evolving LLMs based on multi-agent reinforcement learning.
11. Oct 7, 2026 · Research paper · 1 source
    MotherTree: Meta-learning on synthetic data improves decision tree training
    We introduce MotherTree, a tabular transformer that meta-learns decision tree induction: given a training set for a new task, it outputs a hard, axis-aligned decision tree, equivalent in form to classically trained trees, in a single forward pass.
12. Oct 7, 2026 · Research paper · 1 source
    Executing Causal Structure Learning with Linear-Attention Transformers
    We study a standard continuous method that repeatedly updates a candidate causal graph while enforcing acyclicity.
13. Oct 7, 2026 · Research paper · 1 source
    Evaluating Sequence Assembly Strategies for Differentially Private Synthetic Time-Series Forecasting
    We study this post-generation sequence assembly process by systematically varying overlap rates and window-weighting schemes and evaluating the resulting sequences in terms of boundary continuity, statistical and temporal fidelity, and Train-on-Synthetic-Test-on-Real (TSTR) forecasting utility.
14. Oct 7, 2026 · Research paper · 1 source
    HuLiGen: Human LiDAR Generation from Parametric Body Models
    In contrast, we introduce HuLiGen, a generative model that generates human LiDAR point clouds from a parametric body model, using a point transformer trained with a flow-matching objective.
15. Oct 7, 2026 · Research paper · 1 source
    Broadly Applicable Approximate MCMC for Switching Stochastic Differential Equations Using Uniformization and Time-Conditioned Factorized Neural Likelihood Estimation
    Switching stochastic differential equations (SSDEs) describe continuous-time dynamics whose parameters switch according to a latent regime process that follows a continuous-time Markov chain (CTMC).
16. Oct 7, 2026 · Research paper · 1 source
    m-Set Adversarial Bandits with Winner Feedback
    We show upper and lower bounds on the regret of $m$-set adversarial bandits for different utilities (winner reward or sum of rewards) and feedback models (winner index, winner reward, sum of rewards, and their combinations).
17. Oct 7, 2026 · Research paper · 1 source
    Beyond Reward Suppression: Near-Optimal Offline Attacks on Warm-Start Bandits with Bounded Rewards
    Adversarial attacks on bandits aim to mislead a learner toward a target arm while keeping the attack cost small.
18. Oct 7, 2026 · Research paper · 1 source
    Hard, Yet Reducible: Controlled Forward Transfer for Synthetic Degradation Curation
    We propose the controlled Reducible Degradation Gap (cRDG) for regions defined by degradation type and severity.
19. Oct 7, 2026 · Research paper · 1 source
    Concentration, Not Uncertainty: Why Targeted Synthetic Data Doesn't Help Camouflaged Object Detection
    Camouflaged object detection requires pixel-accurate masks, but obtaining such annotations is slow and costly, making synthetic training images an attractive alternative.
20. Oct 7, 2026 · Research paper · 1 source
    AdaPS-LiNGAM: Adaptive Predecessor Selection for Linear Non-Gaussian Acyclic Models under Small-Sample Settings
    This challenge also arises in the linear non-Gaussian acyclic model (LiNGAM), an identifiable framework for causal discovery from observational data.
21. Oct 7, 2026 · Research paper · 1 source
    An Invariant Tangent-Angle Descriptor and a Band U-Net for 2D Fragment Adjacency Prediction
    This paper addresses the prediction of adjacency between pairs of 2D fragments based on their contours.
22. Oct 7, 2026 · Research paper · 1 source
    Finite-Rank Logistic Gaussian Processes with Exact Likelihood for Conditional Density Estimation
    We propose the exact likelihood finite-rank LGP (ExFR-LGP), which writes the log density as the sum of two bivariate functions, one of the response and a location-varying linear index of the covariates, and one of the response and the location.
23. Oct 7, 2026 · Research paper · 1 source
    Self-Consuming Generative Models with Co-Evolving Human Preferences
    We show that when training relies entirely on user-curated synthetic data, iterative curation amplifies initial biases and drives the system toward one of multiple singleton equilibria in which the instance holding an initial advantage eventually dominates.
24. Oct 7, 2026 · Research paper · 1 source
    Stability and Diversity of Networked Self-Consuming Generative Ecosystems
    This paper takes a first step toward understanding networked self-consuming generative models, in which multiple models consume synthetic data generated by one another through complex interaction pathways.
25. Oct 6, 2026 · Research paper · 1 source
    ToolRACER: A Robust Agentic Conversation Emulation Resource for Agent Training and Evaluation
    We present ToolRACER, a synthetic data generation pipeline that coordinates user, assistant and tool emulation models to generate and validated multi-turn interactions between a user and an agent.
26. Oct 6, 2026 · Research paper · 1 source
    World Models Dream of Success: Diagnosing and Repairing Failure Insensitivity in Robot World Models
    Robot world models support policy evaluation, planning, and synthetic data generation, but these applications require predictions that distinguish successful actions from failures.
27. Oct 6, 2026 · Research paper · 1 source
    Learning Transition Kernels of Jump-Diffusion Processes with Conditional Diffusion Models
    We study the problem of learning transition kernels for time-homogeneous jump-diffusion processes using conditional diffusion models, with the goal of generating new sample paths from training data consisting of N independent trajectories observed on a high-frequency discrete time grid.
28. Oct 6, 2026 · Research paper · 1 source
    Algorithmic Scratchpads and Curriculum Staging for Arithmetic Reasoning in Tiny Transformers
    In this paper, we investigate the mechanics of multi-step arithmetic in compact "Tiny" Transformers ( 10.6M non-embedding parameters, 49.3M total) trained on synthetic data across four basic operations (+, -, , /) unrolled as step-by-step scratchpads.
29. Oct 6, 2026 · Research paper · 1 source
    Coverage, Not Difficulty, Sets How Much Synthetic Data an Activation Probe Needs
    Activation probes that monitor deployed language models are trained on synthetic conversations, and how many a probe needs is open.
30. Oct 6, 2026 · Research paper · 1 source
    When Forgetting is not Catastrophic: On the Mechanics of Spurious Forgetting
    Knowledge that a language model appears to forget during finetuning often remains stored and can be recovered, a phenomenon called spurious forgetting.

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