7 papers
Latent Thought Flow: Efficient Latent Reasoning in Large Language Models
Xiandong Zou, Jing Huang, Jianshu Li +1
Large Language Models (LLMs) increasingly rely on intermediate reasoning, yet explicit Chain-of-Thought (CoT) suffers from a linguistic space bottleneck: each thought must be decod…
Variational Speculative Decoding: Rethinking Draft Training from Token Likelihood to Sequence Acceptance
Xiandong Zou, Jianshu Li, Jing Huang +1
Speculative decoding accelerates inference for (M)LLMs, yet a training-decoding discrepancy persists: while existing methods optimize single greedy trajectories, decoding involves…
Benchmarking Gaslighting Attacks Against Speech Large Language Models
Jinyang Wu, Bin Zhu, Xiandong Zou +3
As Speech Large Language Models (Speech LLMs) become increasingly integrated into voice-based applications, ensuring their robustness against manipulative or adversarial input beco…
DreamCS: Geometry-Aware Text-to-3D Generation with Unpaired 3D Reward Supervision
Xiandong Zou, Ruihao Xia, Hongsong Wang +1
While text-to-3D generation has attracted growing interest, existing methods often struggle to produce 3D assets that align well with human preferences. Current preference alignmen…
HPS: Hard Preference Sampling for Human Preference Alignment
Xiandong Zou, Wanyu Lin, Yuchen Li +1
Aligning Large Language Model (LLM) responses with human preferences is vital for building safe and controllable AI systems. While preference optimization methods based on Plackett…
IBNorm: Information-Bottleneck Inspired Normalization for Representation Learning
Xiandong Zou, Jia Li, Xiaotong Yuan +1
Normalization is fundamental to deep learning, but existing approaches such as BatchNorm, LayerNorm, and RMSNorm are variance-centric by enforcing zero mean and unit variance, stab…