2 citations · 3 across the 5 of their papers we have counts for
5 papers
See it to Place it: Evolving Macro Placements with Vision-Language Models
Ikechukwu Uchendu, Swati Goel, Karly Hou +5
We propose using Vision-Language Models (VLMs) for macro placement in chip floorplanning, a complex optimization task that has recently shown promising advancements through machine…
Evolving Deeper LLM Thinking
Kuang-Huei Lee, Ian Fischer, Yueh-Hua Wu +4
We explore an evolutionary search strategy for scaling inference time compute in Large Language Models. The proposed approach, Mind Evolution, uses a language model to generate, re…
Training-free Diffusion Model Alignment with Sampling Demons
Po-Hung Yeh, Kuang-Huei Lee, Jun-Cheng Chen
Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable…
Geometric-Averaged Preference Optimization for Soft Preference Labels
Hiroki Furuta, Kuang-Huei Lee, Shixiang Shane Gu +4
Many algorithms for aligning LLMs with human preferences assume that human preferences are binary and deterministic. However, human preferences can vary across individuals, and the…
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
Kuang-Huei Lee, Xinyun Chen, Hiroki Furuta +2
Current Large Language Models (LLMs) are not only limited to some maximum context length, but also are not able to robustly consume long inputs. To address these limitations, we pr…