Author

Kai Tang

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Preprint Jul 2026

SPARK: Susceptibility-Guided Profiling and Steering of Latent Reasoning States in Large Language Models

SPARK is introduced, which uses hidden-state response to diagnose whether a model internally enters an effective reasoning state and to guide lightweight test-time steering, and suggests that susceptibility can serve not only as a diagnostic signal for reasoning failures, but also as a practical guide for targeted test-time intervention.

Dongxu Zhang, Yiding Sun, Zihao Guo et al. · 0 citations

Jun 2026

FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models

This work proposes FADE (FFN Attenuation for DEcoding), a training-free method that attenuates FFN outputs to reduce language-prior dominance and shows that FADE effectively mitigates hallucinations while preserving inference efficiency.

Yichen Guo, Kai Tang, Fenglai Lin et al. · arXiv.org · 2 citations

Preprint Jul 2026

SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering

SeeMe is proposed, a training-free framework that introduces the concept of feature engineering from traditional machine learning into LVLMs and restructures visual tokens through a three-stage token engineering process to suppress hallucination sources while preserving informative visual evidence.

Kai Tang, Jinhao You, Bohua Zhang et al. · 2 citations