5 papers
Token-Budget Distillation: Transferring Full-Token Semantics to Compressed Video Vision-Language Models
Xiaoyang Guo, Guoping Luo, Jusheng Zhang +2
Adapting video vision-language models (VLMs) is computationally expensive because video inputs produce a large number of visual tokens, making both fine-tuning and inference costly…
Ave: Guiding Agentic GPU Optimization Using Data-Flow Invariants
Haohui Mai, Xiaoyan Guo, Xiangyun Ding +7
LLM coding agents can generate correct GPU kernels, but their performance still trails expert libraries. Reaching peak throughput requires coordinating low-level optimizations such…
Adaptive-VoCo: Complexity-Aware Visual Token Compression for Vision-Language Models
Xiaoyang Guo, Keze Wang
In recent years, large-scale vision-language models (VLMs) have demonstrated remarkable performance on multimodal understanding and reasoning tasks. However, handling high-dimensio…
MM-CoT:A Benchmark for Probing Visual Chain-of-Thought Reasoning in Multimodal Models
Jusheng Zhang, Kaitong Cai, Xiaoyang Guo +10
The ability to perform Chain-of-Thought (CoT) reasoning marks a major milestone for multimodal models (MMs), enabling them to solve complex visual reasoning problems. Yet a critica…
Hybrid Token Compression for Vision-Language Models
Jusheng Zhang, Xiaoyang Guo, Tongyu Mo +5
Vision-language models (VLMs) rely on hundreds of visual tokens, leading to high computational and memory costs. Existing compression methods face a trade-off: continuous compressi…