Papers by Jiaxi Hu

2 papers
Native Hybrid Attention for Efficient Sequence Modeling (2026.acl-long)

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Challenge: Experimental results show that NHA surpasses Transformers on recall-intensive tasks.
Approach: They propose a hybrid architecture of linear and full attention that integrates both into a unified layer design.
Outcome: The proposed architecture surpasses Transformers and other hybrid baselines on recall-intensive and commonsense reasoning tasks.
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)

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Challenge: Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent.
Approach: They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs.
Outcome: The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models.

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