Pseudocode-Guided Structured Reasoning for Automating Reliable Inference in Vision-Language Models
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. However, their susceptibility to hallucinations introduces critical failures in decision-making, posing significant safety and reliability risks in physical deployments. This challenge is exacerbated by the open-ended nature of real-world tasks, where questions vary vastly in difficulty and modality, demanding robust and adaptable reasoning strategies. To tackle this, we propose the Pseudocode-guided Structured Reasoning framework (PStar), which adaptively selects structured pseudocode reasoning paths...
| Authors | Weicong Ni, Tianbao Jiang, Linlin Wang |
|---|---|
| Primary category | cs.AI |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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