3 papers
cs.CL2026
LoopCD: Loop-wise Contrastive Decoding for Improving Reasoning in Looped Language Models
Byeongho Yu, Junhyuk So, Eunhyeok Park
Looped Language Models (LoopLMs) perform "latent reasoning" by recursively refining internal latent representations with shared weights, offering a more effective alternative to ex…
cs.NE2025
Stabilizing Direct Training of Spiking Neural Networks: Membrane Potential Initialization and Threshold-robust Surrogate Gradient
Hyunho Kook, Byeongho Yu, Jeong Min Oh +1
Recent advancements in the direct training of Spiking Neural Networks (SNNs) have demonstrated high-quality outputs even at early timesteps, paving the way for novel energy-efficie…
cs.CL2025
PruneCD: Contrasting Pruned Self Model to Improve Decoding Factuality
Byeongho Yu, Changhun Lee, Jungyu Jin +1
To mitigate the hallucination problem in large language models, DoLa exploits early exit logits from the same model as a contrastive prior. However, we found that these early exit…