mlsanityData to 14 Oct 2026

Survey on Vision-Language-Action Models

arXiv 2502.06851 ↗First posted 7 Feb 2025cs.CL cs.AI cs.CVcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Feb 2025.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

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  1. 2025MMReview: A Multidisciplinary and Multimodal Benchmark for LLM-Based Peer Review Automation
  2. 2025Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges
  3. 2026LitReview Arena: Evaluating Literature Review Agents with Battle-Style Peer Review Platform
  4. 2024Building and better understanding vision-language models: insights and future directions
  5. 2025A Survey on Vision-Language-Action Models: An Action Tokenization Perspective
  6. 2024LADEV: A Language-Driven Testing and Evaluation Platform for Vision-Language-Action Models in Robotic Manipulation
  7. 2024System for systematic literature review using multiple AI agents: Concept and an empirical evaluation
  8. 2025DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process