activity
20242026
most citedChronological Thinking in Full-Duplex Spoken Dialogue Language Models

1 citations · 1 across the 1 of their papers we have counts for

collaborators

12 papers

cs.CL20261 cited

Chronological Thinking in Full-Duplex Spoken Dialogue Language Models

Donghang Wu, Haoyang Zhang, Chen Chen +8

Recent advances in spoken dialogue language models (SDLMs) reflect growing interest in shifting from turn-based to full-duplex systems, where the models continuously perceive user…

cs.RO2026

ReViP: Mitigating False Completion in Vision-Language-Action Models with Vision-Proprioception Rebalance

Zhuohao Li, Yinghao Li, Jian-Jian Jiang +6

Vision-Language-Action (VLA) models have advanced robotic manipulation by combining vision, language, and proprioception to predict actions. However, previous methods fuse proprioc…

cs.CL2026

OPUS: Towards Efficient and Principled Data Selection in Large Language Model Pre-training in Every Iteration

Shaobo Wang, Xuan Ouyang, Tianyi Xu +9

As high-quality public text approaches exhaustion, a phenomenon known as the Data Wall, pre-training is shifting from more tokens to better tokens. However, existing methods either…

cs.LG2026

Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space

Xingwei Qu, Shaowen Wang, Zihao Huang +16

Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity…

cs.LG2025

Majority of the Bests: Improving Best-of-N via Bootstrapping

Amin Rakhsha, Kanika Madan, Tianyu Zhang +2

Sampling multiple outputs from a Large Language Model (LLM) and selecting the most frequent (Self-consistency) or highest-scoring (Best-of-N) candidate is a popular approach to ach…

cs.CV2025

AirCopBench: A Benchmark for Multi-drone Collaborative Embodied Perception and Reasoning

Jirong Zha, Yuxuan Fan, Tianyu Zhang +4

Multimodal Large Language Models (MLLMs) have shown promise in single-agent vision tasks, yet benchmarks for evaluating multi-agent collaborative perception remain scarce. This gap…