Author

Justin Lovelace

Texas A&M University

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#artificial intelligence Jun 2026

Self-Augmenting Retrieval for Diffusion Language Models

This work shows that intermediate states surface salient entities early in the denoising trajectory, making them a useful lookahead signal for retrieval-augmented generation (RAG), and demonstrates that grounding generation in retrieved evidence sharply reduces inter-token dependence, making RAG particularly well suite...

Paul J. Jünger, Justin Lovelace, Lin-Xi Zhao et al. · arXiv.org · 2 citations · ⚡1

#artificial intelligence Preprint Sep 2026

MoRE: Mixture of Reused Experts

Experiments show that MoRE consistently achieves lower perplexity and stronger downstream performance than standard MoEs and state-of-the-art weight-sharing architectures at matched compute and parameter budgets, with only minimal modifications to existing MoE implementations.

Eric S. Qiu, U. U. Acikalin, Justin Lovelace et al. · 1 citation

#machine learning Preprint Aug 2026

Optimize Your Sampling: Tuned Diffusion Sampling with Bayesian Optimization

Optizing Your Sampling (OYS), which instead treats timestep selection as a black-box optimization problem, optimizing the target metric directly with Bayesian optimization, improves both simple and sophisticated samplers such as Euler and DPM-Solver++.

Travis Zhang, Christian K. Belardi, Justin Lovelace et al. · 0 citations