3 papers
cs.LG2026
Evaluating LLM Simulators as Differentially Private Data Generators
Nassima M. Bouzid, Dehao Yuan, Nam H. Nguyen +1
LLM-based simulators offer a promising path for generating complex synthetic data where traditional differentially private (DP) methods struggle with high-dimensional user profiles…
cs.LG2026
Dynamic Tokenization via Reinforcement Patching: End-to-end Training and Zero-shot Transfer
Yulun Wu, Sravan Kumar Ankireddy, Samuel Sharpe +4
Efficiently aggregating spatial or temporal horizons to acquire compact representations has become a unifying principle in modern deep learning models, yet learning data-adaptive r…
cs.LG2026
PersonaLedger: Generating Realistic Financial Transactions with Persona Conditioned LLMs and Rule Grounded Feedback
Dehao Yuan, Tyler Farnan, Stefan Tesliuc +8
Strict privacy regulations limit access to real transaction data, slowing open research in financial AI. Synthetic data can bridge this gap, but existing generators do not jointly…