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Zehua Zang

4 accepted papers

2026

M2I2: Learning Efficient Multi-Agent Communication via Masked State Modeling and Intention Inference

AAAI 2026technical

Communication is essential in coordinating the behaviors of multiple agents. However, existing methods primarily emphasize content, timing, and partners for information sharing, often neglecting the critical aspect of integrating shared information. This gap can significantly impact agents

Cited by 0SourcePDFScholar
2026

RS-SSM: Refining Forgotten Specifics in State Space Model for Video Semantic Segmentation

CVPR 2026

Recently, state space models have demonstrated efficient video segmentation through linear-complexity state space compression. However, Video Semantic Segmentation (VSS) requires pixel-level spatiotemporal modeling capabilities to maintain temporal consistency in segmentation of semantic objects. Wh

Cited by 0SourcecodeScholar
2026

Test-Time Perturbation Tuning with Delayed Feedback for Vision-Language-Action Models

CVPR 2026

Vision-Language-Action models (VLAs) achieve remarkable performance in sequential decision-making but remain fragile to subtle environmental shifts, such as small changes in object pose. We attribute this brittleness to trajectory overfitting, where VLAs over-attend to the spurious correlation betwe

Cited by 0SourcecodeScholar
2024

T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration

AAAI 2024technical

Communication stands as a potent mechanism to harmonize the behaviors of multiple agents. However, existing work primarily concentrates on broadcast communication, which not only lacks practicality, but also leads to information redundancy. This surplus, one-fits-all information could adversely impa…

Cited by 10SourcePDFScholar