Author

Zeyu Liu

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Preprint Sep 2026

What Makes World Action Models Generalize? An Empirical Study of Test-Time Future Modeling

World action models (WAMs) predict the future alongside actions during training. Due to the heavy computation cost of video denoising, whether the future must still be generated during inference is disputed: Explicit WAMs denoise it into clean frames along with every action chunk, whereas Latent WAMs discard it entirel...

Ren-Ping Zhou, Zan-Lin Ni, Zi-Hao Fan et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Is Human-Readable Text Necessary for Effective LLM Fine-Tuning?

Is human readability necessary for effective fine-tuning of large language models? We investigate whether model-conditioned training representations can preserve or improve adaptation utility without requiring a human-readable textual form. We propose Desired-Update-Aligned Synthetic Data (DASA), which uses activation-...

Jin-Hao Zhang, Ze-Yu Liu, Zi-Cheng Yan et al. · 0 citations

Preprint Aug 2026

Quantifying Depth Sufficiency in Residual Neural Networks: A First-Order Criterion

How can we determine whether a trained neural network is already deep enough? We study this under a fixed function-preserving residual-growth protocol specifying insertion locations, residual families, zero-output initializations, and zero-state first-order updates. We define first-order residual depth saturation as th...

Zeyu Liu, Jinhao Zhang, Yun-Quan Zhang et al. · 0 citations