1 citations · 1 across the 15 of their papers we have counts for
16 papers
A Systematic Post-Train Framework for Video Generation
Zeyue Xue, Siming Fu, Jie Huang +9
While large-scale video diffusion models have demonstrated impressive capabilities in generating high-resolution and semantically rich content, a significant gap remains between th…
EchoKV: Efficient KV Cache Compression via Similarity-Based Reconstruction
Shiyu Ji, Yixuan Wang, Yijun Liu +2
The increasing memory demand of the Key-Value (KV) cache poses a significant bottleneck for Large Language Models (LLMs) in long-context applications. Existing low-rank KV compress…
Beyond Static Cropping: Layer-Adaptive Visual Localization and Decoding Enhancement
Zipeng Zhu, Zhanghao Hu, Qinglin Zhu +5
Large Vision-Language Models (LVLMs) have advanced rapidly by aligning visual patches with the text embedding space, but a fixed visual-token budget forces images to be resized to…
Seer Self-Consistency: Advance Budget Estimation for Adaptive Test-Time Scaling
Shiyu Ji, Yixuan Wang, Yijun Liu +2
Test-time scaling improves the inference performance of Large Language Models (LLMs) but also incurs substantial computational costs. Although recent studies have reduced token con…
TridentServe: A Stage-level Serving System for Diffusion Pipelines
Yifei Xia, Fangcheng Fu, Hao Yuan +6
Diffusion pipelines, renowned for their powerful visual generation capabilities, have seen widespread adoption in generative vision tasks (e.g., text-to-image/video). These pipelin…
Judge Q: Trainable Queries for Optimized Information Retention in KV Cache Eviction
Yijun Liu, Yixuan Wang, Yuzhuang Xu +4
Large language models (LLMs) utilize key-value (KV) cache to store historical information during sequence processing. The size of KV cache grows linearly as the length of the seque…
CAMERA: Multi-Matrix Joint Compression for MoE Models via Micro-Expert Redundancy Analysis
Yuzhuang Xu, Xu Han, Yuanchi Zhang +5
Large Language Models (LLMs) with Mixture-of-Experts (MoE) architectures are distinguished by their strong performance scaling with increasing parameters across a wide range of tas…
Think Before You Accept: Semantic Reflective Verification for Faster Speculative Decoding
Yixuan Wang, Yijun Liu, Shiyu ji +4
Large language models (LLMs) suffer from high inference latency due to the auto-regressive decoding process. Speculative decoding accelerates inference by generating multiple draft…
Lookahead Q-Cache: Achieving More Consistent KV Cache Eviction via Pseudo Query
Yixuan Wang, Shiyu Ji, Yijun Liu +4
Large language models (LLMs) rely on key-value cache (KV cache) to accelerate decoding by reducing redundant computations. However, the KV cache memory usage grows substantially wi…
sEEG-based Encoding for Sentence Retrieval: A Contrastive Learning Approach to Brain-Language Alignment
Yijun Liu
Interpreting neural activity through meaningful latent representations remains a complex and evolving challenge at the intersection of neuroscience and artificial intelligence. We…
HSTU-BLaIR: Lightweight Contrastive Text Embedding for Generative Recommender
Yijun Liu
Recent advances in recommender systems have underscored the complementary strengths of generative modeling and pretrained language models. We propose HSTU-BLaIR, a hybrid framework…
A Survey on Transformer Context Extension: Approaches and Evaluation
Yijun Liu, Jinzheng Yu, Yang Xu +2
Large language models (LLMs) based on Transformer have been widely applied in the filed of natural language processing (NLP), demonstrating strong performance, particularly in hand…
CVLUE: A New Benchmark Dataset for Chinese Vision-Language Understanding Evaluation
Yuxuan Wang, Yijun Liu, Fei Yu +4
Despite the rapid development of Chinese vision-language models (VLMs), most existing Chinese vision-language (VL) datasets are constructed on Western-centric images from existing…
Improving Grammatical Error Correction via Contextual Data Augmentation
Yixuan Wang, Baoxin Wang, Yijun Liu +3
Nowadays, data augmentation through synthetic data has been widely used in the field of Grammatical Error Correction (GEC) to alleviate the problem of data scarcity. However, these…
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training
Yixuan Wang, Xianzhen Luo, Fuxuan Wei +6
Existing speculative decoding methods typically require additional model structure and training processes to assist the model for draft token generation. This makes the migration o…
LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error Correction
Yixuan Wang, Baoxin Wang, Yijun Liu +2
Over-correction is a critical problem in Chinese grammatical error correction (CGEC) task. Recent work using model ensemble methods based on voting can effectively mitigate over-co…