activity
20192026
collaborators

7 papers

cs.CV2026

Task-Aware Structured Memory for Dynamic Multi-modal In-Context Learning

Zhirui Chen, Ziwei Chen, Ling Shao

Multi-modal large language models (MLLMs) depend on in-context learning (ICL) for rapid task adaptation, but their scalability is severely limited by finite context windows and the…

cs.AI2026

Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories

Peiyang Liu, Zhirui Chen, Xi Wang +4

Monte Carlo Tree Search (MCTS) has been widely used for automated reasoning data exploration, but current supervision extraction methods remain inefficient. Standard approaches ret…

cs.CL2026

StructKV: Preserving the Structural Skeleton for Scalable Long-Context Inference

Zhirui Chen, Peiyang Liu, Ling Shao

As Large Language Models (LLMs) scale to support context windows exceeding one million tokens, the linear growth of Key-Value (KV) cache imposes severe memory capacity and bandwidt…

cs.CL2025

Joint Enhancement of Relational Reasoning for Long-Context LLMs

Zhirui Chen, Wei Shen, Jiashui Huang +1

Despite significant progress, large language models (LLMs) still struggle with long contexts due to memory limitations and their inability to tackle complex and long-context tasks.…

cs.CV2025

Words or Vision: Do Vision-Language Models Have Blind Faith in Text?

Ailin Deng, Tri Cao, Zhirui Chen +1

Vision-Language Models (VLMs) excel in integrating visual and textual information for vision-centric tasks, but their handling of inconsistencies between modalities is underexplore…

cs.LG2023

Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness

Ailin Deng, Shen Li, Miao Xiong +2

Trustworthy machine learning is of primary importance to the practical deployment of deep learning models. While state-of-the-art models achieve astonishingly good performance in t…

cs.CV2019

Weakly Supervised Tracklet Person Re-Identification by Deep Feature-wise Mutual Learning

Zhirui Chen, Jianheng Li, Wei-Shi Zheng

The scalability problem caused by the difficulty in annotating Person Re-identification(Re-ID) datasets has become a crucial bottleneck in the development of Re-ID.To address this…