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Survey on Vision-Language-Action Models

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arxiv 2502.06851 v3 pith:LIJUTKM4 submitted 2025-02-07 cs.CL cs.AIcs.CV

classification cs.CLcs.AIcs.CV
keywords literaturemodelsresearchacademicaccuracyai-generatedcontentfuture
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents an AI-generated review of Vision-Language-Action (VLA) models, summarizing key methodologies, findings, and future directions. The content is produced using large language models (LLMs) and is intended only for demonstration purposes. This work does not represent original research, but highlights how AI can help automate literature reviews. As AI-generated content becomes more prevalent, ensuring accuracy, reliability, and proper synthesis remains a challenge. Future research will focus on developing a structured framework for AI-assisted literature reviews, exploring techniques to enhance citation accuracy, source credibility, and contextual understanding. By examining the potential and limitations of LLM in academic writing, this study aims to contribute to the broader discussion of integrating AI into research workflows. This work serves as a preliminary step toward establishing systematic approaches for leveraging AI in literature review generation, making academic knowledge synthesis more efficient and scalable.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Leveraging OS-Level Primitives for Robotic Action Management

    cs.OS 2025-08 conditional novelty 4.0 of 10

    Applying OS-style exception handling, context caching, and replay to robotic action slices raises success rates 7x to 24x and cuts execution steps up to 74% for repetitive manipulation tasks, without retraining the VLA model.

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