Author

Shiu-hong Kao

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Preprint Oct 2026

From Reasoning Failures to Composable Video Spatial Intelligence

Spatial reasoning benchmarks evaluate vision-language models across diverse tasks, but task-level scores do not reveal which underlying capabilities account for success or failure. Each task requires recovering spatial evidence, representing geometry, and reasoning over it. We disentangle these capabilities by comparin...

Peng-Zhan Sun, Jun-Bin Xiao, Ramanathan Rajaraman et al. · 0 citations

Preprint Sep 2026

Aligning Thoughts with Answers: Probability Rewards to Tame Thinking Drift

This paper studies \textbf{thinking--answer consistency} in vision-language models. We focus on Visual Intention Grounding, where a model infers a target object based on a human intention query and predicts a bounding box. We reveal that previous IoU-based reinforcement learning (RL) frameworks suffer from ``thinking d...

Peng-Zhan Sun, Shiu-hong Kao, Shi-Jie Li et al. · 1 citation

#artificial intelligence Preprint Oct 2026

Rethinking Probability-Based Reinforcement Learning From Posterior Concentration

Verifier-free reinforcement learning with probability-based rewards offers a promising way to train LLMs on general reasoning tasks where external verifiers are unavailable. Yet the reliability of these rewards, especially in long-horizon reasoning, remains underexplored. This work identifies a length-dependent failure...

Shiu-hong Kao, Yu-Bo Zhao, Zhen Tian et al. · 0 citations