Pseudocode-Guided Structured Reasoning for Automating Reliable Inference in Vision-Language Models
The paper presents a new framework called Pseudocode-guided Structured Reasoning (PStar) aimed at improving the reliability of Vision-Language Models (VLMs). PStar utilizes structured pseudocode reasoning paths to enhance decision-making and reduce hallucination rates in VLMs. The framework has shown significant improvements in performance, achieving state-of-the-art scores in various benchmarks.
- ▪Vision-Language Models are crucial for robotic automation but are prone to hallucinations that can lead to critical failures.
- ▪The PStar framework introduces a Difficulty Feature Vector to assess question complexity and select appropriate reasoning strategies.
- ▪Experimental results indicate that PStar reduces hallucination rates and outperforms existing models, including GPT-4V.
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Computer Science > Artificial Intelligence arXiv:2605.19663 (cs) [Submitted on 19 May 2026] Title:Pseudocode-Guided Structured Reasoning for Automating Reliable Inference in Vision-Language Models Authors:Weicong Ni, Tianbao Jiang, Linlin Wang View a PDF of the paper titled Pseudocode-Guided Structured Reasoning for Automating Reliable Inference in Vision-Language Models, by Weicong Ni and 2 other authors View PDF HTML (experimental) Abstract:Vision-Language Models (VLMs) are becoming the cornerstone of high-level reasoning for robotic automation, enabling robots to parse natural language commands and perceive their environments.
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