4 papers
Ladders of Thought: A Self-Evolving Curriculum of Progressively Simplified Reasoning Traces
Minghui Liu, Thomas Magelinski, Dehao Yuan +2
Large language models (LLMs) excel at reasoning when scaled to hundreds of billions of parameters, but small- and mid-scale models remain brittle reasoners even with knowledge dist…
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…
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…
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…