9 papers
From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent
Mingyu Huang, Weiqing Min, Ying Jin +2
Personalized glucose regulation remains a central yet unresolved challenge in precision nutrition, as postprandial glucose response varies substantially across individuals. Existin…
Markovian Generation Chains in Large Language Models
Mingmeng Geng, Amr Mohamed, Guokan Shang +2
The widespread use of large language models (LLMs) raises an important question: how do texts evolve when they are repeatedly processed by LLMs? In this paper, we define this itera…
Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning
Yang Zhang, Amr Mohamed, Hadi Abdine +2
Curriculum learning-organizing training data from easy to hard-has improved efficiency across machine learning domains, yet remains underexplored for language model pretraining. We…
Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR
Abdelaziz Bounhar, Hadi Abdine, Evan Dufraisse +5
Large language models (LLMs) trained for step-by-step reasoning often become excessively verbose, raising inference cost. Standard Reinforcement Learning with Verifiable Rewards (R…
Fast-Decoding Diffusion Language Models via Progress-Aware Confidence Schedules
Amr Mohamed, Yang Zhang, Michalis Vazirgiannis +1
Diffusion large language models (dLLMs) offer a promising alternative to autoregressive models, but their practical utility is severely hampered by slow, iterative sampling. We pre…
LLM as a Broken Telephone: Iterative Generation Distorts Information
Amr Mohamed, Mingmeng Geng, Michalis Vazirgiannis +1
As large language models are increasingly responsible for online content, concerns arise about the impact of repeatedly processing their own outputs. Inspired by the "broken teleph…