Pu (Perry) Wang — accepted AI-conference papers

2026

Indoor Multi-View Radar Object Detection via 3D Bounding Box Diffusion

Ryoma Yataka, Pu (Perry) Wang, Petros Boufounos, Ryuhei Takahashi

AAAI 2026technical

Multi-view indoor radar perception has drawn attention due to its cost-effectiveness and low privacy risks. Existing methods often rely on implicit cross-view radar feature association, such as proposal pairing in RFMask or query-to-feature cross-attention in RETR, which can lead to ambiguous featur

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2026

LatentLLM: Activation-Aware Transform to Multi-Head Latent Attention

Toshiaki Koike-Akino, Xiangyu Chen, Jing Liu, Ye Wang, Pu (Perry) Wang, Matthew Brand

AAAI 2026technical

Modern foundation models such as large language models (LLMs) require a massive amount of computational and memory resources. We propose a new framework to convert such LLMs into a reduced-dimension latent structure. Our method extends a local activation-aware tensor decomposition to a global attent

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