1 citations · 1 across the 7 of their papers we have counts for
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Hidden Decoding at Scale: Latent Computation Scaling for Large Language Models
Aiwei Liu, Cheng Shi, Chuhan Wu +44
Scaling Large Language Models (LLMs) has been driven mainly by enlarging the Transformer backbone, but for an already-strong model this requires another round of costly pretraining…
UniAudio-Token: Empowering Semantic Speech Tokenizers with General Audio Perception
Yuhan Song, Linhao Zhang, Aiwei Liu +6
Semantic speech tokenizers have become a widely used interface for Audio-LLMs, owing to their compact single-codebook design and strong linguistic alignment. However, their focus o…
Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs
Linhao Zhang, Yuhan Song, Aiwei Liu +6
Recent Audio Large Language Models (AudioLLMs) exhibit a striking performance inversion: while excelling at complex reasoning tasks, they consistently underperform on fine-grained…
WeDLM: Reconciling Diffusion Language Models with Standard Causal Attention for Fast Inference
Aiwei Liu, Minghua He, Shaoxun Zeng +7
Autoregressive (AR) generation is the standard decoding paradigm for Large Language Models (LLMs), but its token-by-token nature limits parallelism at inference time. Diffusion Lan…
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
ByteDance Seed, :, Jiaze Chen +267
We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 8…
Efficient Pretraining Length Scaling
Bohong Wu, Shen Yan, Sijun Zhang +4
Recent advances in large language models have demonstrated the effectiveness of length scaling during post-training, yet its potential in pre-training remains underexplored. We pre…
HybridNorm: Towards Stable and Efficient Transformer Training via Hybrid Normalization
Zhijian Zhuo, Yutao Zeng, Ya Wang +5
Transformers have become the de facto architecture for a wide range of machine learning tasks, particularly in large language models (LLMs). Despite their remarkable performance, m…