most citedContextual Attention Modulation: Towards Efficient Multi-Task Adaptation in Large Language Models

2 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2026

VISTA-Bench: Do Vision-Language Models Really Understand Visualized Text as Well as Pure Text?

Qing'an Liu, Juntong Feng, Yuhao Wang +6

Vision-Language Models (VLMs) have achieved impressive performance in cross-modal understanding across textual and visual inputs, yet existing benchmarks predominantly focus on pur…

cs.CL2026

MVSS: A Unified Framework for Multi-View Structured Survey Generation

Yinqi Liu, Yueqi Zhu, Yongkang Zhang +7

Scientific surveys require not only summarizing large bodies of literature, but also organizing them into clear and coherent conceptual structures. However, existing automatic surv…

cs.DB2025

Category-Aware Semantic Caching for Heterogeneous LLM Workloads

Chen Wang, Xunzhuo Liu, Yue Zhu +3

LLM serving systems process heterogeneous query workloads where different categories exhibit different characteristics. Code queries cluster densely in embedding space while conver…

cs.AI20252 cited

Contextual Attention Modulation: Towards Efficient Multi-Task Adaptation in Large Language Models

Dayan Pan, Zhaoyang Fu, Jingyuan Wang +3

Large Language Models (LLMs) possess remarkable generalization capabilities but struggle with multi-task adaptation, particularly in balancing knowledge retention with task-specifi…

cs.ET20251 cited

When to Reason: Semantic Router for vLLM

Chen Wang, Xunzhuo Liu, Yuhan Liu +4

Large Language Models (LLMs) demonstrate substantial accuracy gains when augmented with reasoning modes such as chain-of-thought and inference-time scaling. However, reasoning also…