arXiv:2608. 13096v1 Announce Type: new Abstract: Limit order book (LOB) simulators are most useful to practitioners when they combine realistic market dynamics, computationally efficient sampling, controllable scenario generation, and the ability to generalize beyond the instruments seen during training---properties that existing agent-based and deep generative simulators provide only partially.
By Zhuohan Wang, Andreea Bacalum, Ollie Olby, Carmine Ventre, Namid Stillman
arXiv:2604. 23904v3 Announce Type: replace-cross Abstract: Synthetic tabular data are often evaluated by distributional similarity, privacy distance, or train-on-synthetic-test-on-real predictive performance, but these criteria do not ensure validity for causal inference.
By Yichen Xu
arXiv:2502. 18049v5 Announce Type: replace-cross Abstract: Recent studies identified an intriguing phenomenon in recursive generative model training known as model collapse, where models trained on data generated by previous models exhibit severe performance degradation.
By Hengzhi He, Shirong Xu, Guang Cheng
arXiv:2609.36472v1 Announce Type: new
Abstract: Sequential Model-Based Optimization (SMBO) traditionally relies on Bayesian or ensembling surrogates for uncertainty quantification. While historically...
By Jonas Seng, Bennet Wittelsbach, Kristian Kersting
The paper investigates why certain synthetic relational datasets lead to better relational foundation models. By training four Relational Transformer checkpoints on data from four different generators, the authors trace a measurable property of the data—specifically the predictive necessity of cross‑table information—to the emergence of relational computation in the models. They find that the RelDiff generator yields the largest predictive gain from foreign‑key‑linked parents, and its model uniquely responds to foreign‑key interventions, a dependence that persists across random initializations and grows with corrupted links. Disrupting this mechanism during downstream inference eliminates RelDiff’s advantage on relational tasks while leaving structure‑insensitive models largely unchanged, thereby linking synthetic data properties to learned mechanisms and downstream behavior.
By Shivam Dubey, Mohamed Bouadi, Nassim Bouarour, Varun Kulkarni, Aditya Tanna, Vinay Kumar Sankarapu
arXiv:2607. 08122v1 Announce Type: new Abstract: Workload-based differentially private (DP) synthetic data methods privately measure aggregate queries and post-process the noisy answers into synthetic records.
By Amir Asiaee, Kaveh Aryan
arXiv:2603. 10823v2 Announce Type: replace-cross Abstract: Deep generative models can help with data scarcity and privacy by producing synthetic training data, but they struggle in low-data, imbalanced tabular settings to fully learn the complex data distribution.
By Xiaofeng Lin, Seungbae Kim, Zhuoya Li, Zachary DeSoto, Charles Fleming, Guang Cheng
arXiv:2607. 18310v1 Announce Type: cross Abstract: Synthetic-population tools increasingly run every individual as an independent large language model (LLM) agent.
By Gurkan Ozkan
GENSCRIPT is an inference‑only pipeline that generates synthetic data without training a generative model. It creates a deterministic statistical profile of the source data, uses a language model to infer field semantics and cross‑column constraints, and then compiles these into an executable sampler that works for single‑table, temporal, and relational data. The method builds generators in minutes, samples large datasets quickly, and achieves fidelity comparable to leading methods while preserving key data relationships such as 1‑to‑1 mappings and primary‑foreign key constraints.
By Zilong Zhao, Abdul Raheem, Jiayu Li, Sohei Arisaka, Darius Lim Hong Yi, Milad Abdollahzadeh, Uzair Javaid, Biplab Sikdar
arXiv:2607. 20694v1 Announce Type: new Abstract: Personal and organizational planning systems maintain two records that drift apart: what was planned (a task's effort budget) and what was done (a logged action's duration and description).
By Salavat Ishbulatov
The paper introduces SynPro, a synthetic data generation framework that augments limited organic text for large language model pretraining by applying rephrasing and reformatting operations. SynPro’s generators are optimized with reinforcement learning rewards for quality, faithfulness, and data influence, and are updated continuously as training plateaus. Experiments on 400M, 1.1B, and 2B models show that SynPro can unlock 3.4–5.2× the effective tokens of standard repetition, even outperforming a non‑data‑bound oracle at larger scales.
By Zichun Yu, Chenyan Xiong
arXiv:2607. 09706v1 Announce Type: new Abstract: Language models turn a worded situation into a numeric plan, and the dominant pipelines (NL4Opt, OptiMUS, ORLM, OR-LLM-Agent) commit to a single objective and point-valued coefficients, then solve once.
By Suyash Mishra