10 papers
No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval
Lixuan Guo, Yifei Wang, Tiansheng Wen +3
Multi-vector retrieval (MVR) models, exemplified by ColBERT, have established new benchmarks in retrieval accuracy by preserving fine-grained token-level interactions. However, thi…
Optimus: Elastic Decoding for Efficient Diffusion LLM Serving
Chiyue Wei, Cong Guo, Bowen Duan +7
Large language model (LLM) serving is fundamentally limited by inefficient hardware utilization. Autoregressive (AR) decoding underutilizes GPUs due to its strictly sequential exec…
SSL4RL: Revisiting Self-supervised Learning as Intrinsic Reward for Visual-Language Reasoning
Xiaojun Guo, Runyu Zhou, Yifei Wang +8
Vision-language models (VLMs) have shown remarkable abilities by integrating large language models with visual inputs. However, they often fail to utilize visual evidence adequatel…
Scaling Attention via Feature Sparsity
Yan Xie, Tiansheng Wen, Tangda Huang +4
Scaling Transformers to ultra-long contexts is bottlenecked by the cost of self-attention. Existing methods reduce this cost along the sequence axis through local window…
StreamingEval: A Unified Evaluation Protocol towards Realistic Streaming Video Understanding
Guowei Tang, Tianwen Qian, Huanran Zheng +2
Real-time, continuous understanding of visual signals is essential for real-world interactive AI applications, and poses a fundamental system-level challenge. Existing research on…
LANPO: Bootstrapping Language and Numerical Feedback for Reinforcement Learning in LLMs
Ang Li, Yifei Wang, Zhihang Yuan +2
Reinforcement learning in large language models (LLMs) often relies on scalar rewards, a practice that discards valuable textual rationale buried in the rollouts, forcing the model…