arXiv Machine Learning
Jun 2

DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation

arXiv:2606. 00535v1 Announce Type: new Abstract: Speculative decoding (SD) has proven to be an effective technique for accelerating autoregressive generation in large language models (LLMs) however, its application to vision-language models (VLMs) remains relatively unexplored.

By Zining Liu, Yunhai Hu, Tianhua Xia, Bo Bao, Eric Sather, Vithursan Thangarasa, Sai Qian Zhang
Hugging Face Trending Papers
Aug 10

CHORUS: Complementary Experts for High-Coverage Testbench Stimulus Generation

Large language models (LLMs) have advanced code generation, where executable feedback provides a more reliable learning signal than textual imitation alone. Hardware verification is an important application of code generation and accounts for a substantial fraction of modern chip design effort, with high-coverage testbench stimulus generation as a key task.

arXiv AI
Jul 22

Large Language Models Explore by Latent Distilling

arXiv:2604. 24927v2 Announce Type: replace-cross Abstract: Generating diverse responses is crucial for test-time scaling of large language models (LLMs), yet standard stochastic sampling mostly yields surface-level lexical variation, limiting semantic exploration.

By Yuanhao Zeng, Ao Lu, Lufei Li, Zheng Zhang, Yexin Li, Kan Ren
arXiv AI
Jun 3

Exploiting Verification-Generation Gap: Test-Time Reinforcement Learning with Confidence-Conditioned Verification

arXiv:2606. 03608v1 Announce Type: cross Abstract: Test-time reinforcement learning has emerged as a promising paradigm for enhancing the complex reasoning abilities of large language models in a completely label-free manner.

By Jiahui Li, Jianfeng Shan, Wenpei Chen, Shunyu Wu, Jian Lou, Wenjie Feng, Dan Li, See-Kiong Ng