Author

Ya-Dong Wang

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#artificial intelligence Preprint Sep 2026

Before the Token Commits: Trajectory-Level Benchmarking of Visual Hallucinations in Diffusion VLMs

Multimodal diffusion language models generate responses by iteratively unmasking tokens, making each answer the endpoint of a multi-step trajectory rather than an immediate commitment. Hallucination benchmarks built for autoregressive models evaluate only the final output, and therefore cannot determine whether an unsu...

Ya-Dong Wang, Si-Ping Yue, Yu Tian et al. · 0 citations

#artificial intelligence Preprint Feb 2026

Beyond Dense States: Sparse Transcoders as Causally Testable Operators for LLM Latent Reasoning

LSTR (Latent Sparse Transcoder Reasoning), a framework that turns sparse transcoders from post-hoc diagnostic tools into in-loop, intervenable transition components for latent reasoning, and suggests that sparse latent transitions can preserve the compression benefits of latent reasoning while making the resulting traj...

Yadong Wang, Hao-Dong Chen, Yu Tian et al. · 0 citations

Preprint Aug 2026

Knowing but Not Saying: Preventing Factual Access Failures in LLM SFT via Recall-Anchored Distillation

Recall-Anchored Distillation (RAD), a base-anchored self-distillation objective that preserves out-of-distribution generation behavior by aligning the adapted model with the original base model's soft continuation distribution on unlabeled OOD text, is introduced.

Hao-Dong Chen, Yadong Wang, Shengtao Wen et al. · 0 citations

Preprint Aug 2026

Rethinking Normalization Placement for LLMs: Post-Norm under Curriculum Depth Growing

Pre-norm is the standard normalization placement in modern Transformers because it facilitates joint optimization of full-depth models. We ask whether this preference persists when depth is introduced through a curriculum. In curriculum depth growth, each appended block receives the boundary representation produced by...

Sheng Ren, Yadong Wang, Naiqiang Tan et al. · 0 citations