Author

Di Wang

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Preprint Aug 2026

Mitigating Reasoning-Induced Misalignment via Safety-Direction Penalty

Reasoning-Induced Misalignment, where fine-tuning on reasoning data containing no harmful content, including mathematics, code, and problem-solving with chain-of-thought traces can induce harmful behaviors of LLM, posing a serious challenge to the safety of LLM reasoning. Cross-architecture, cross-scale, and cross-data...

Yipeng Zhao, Qi-Shun Yang, Shenzhe Zhu et al. · 0 citations

#natural language process... Preprint Sep 2026

SFAD: Speculative Factuality-Aware Decoding

SFAD is presented, a speculative decoding framework that enhances contextual faithfulness without inference degradation and substantially improves faithfulness while achieving $2.48\times$ speedup, offering a practical solution for efficient LLMs.

Guan-Qiao Chen, Di Wang, Lijie Hu · 0 citations

#machine learning Jul 2026

Final Checkpoints Are Not Enough: Analyzing Latent Reasoning Faithfulness Along Training Trajectories

These results show that evaluating only a final checkpoint can obscure both when counterfactual responsiveness changes and what the latent states contribute, and show that high task accuracy can coexist with low counterfactual responsiveness.

Heng-Yu Jin, Shu Yang, Di Wang · arXiv.org · 1 citation