Frank Shyu

This paper proposes Learning to Allocate (L2A), an end-to-end framework for resource-adaptive inference in Large Language Models (LLMs) using budget-conditioned and input-aware gating networks.

This paper introduces Bifocal dLLMs (R2LM), a new paradigm for discrete diffusion language models that combines causal and bidirectional attention for improved throughput and generation quality.

This paper proposes Diffusion-GR2, a method to convert an autoregressive reasoning re-ranker into a block-diffusion re-ranker while maintaining accuracy and increasing speed.

This paper proposes SCOReD, a framework for optimizing chain-of-thought (CoT) distillation in the recommendation domain by parsing teacher traces into typed segments, scoring their importance, and dynamically selecting edits based on the student's output distribution.