arXiv (All)
open access
Lost in Motion: Vision Language Models Fail the Dynamic Gauges Test
The digital transformation of industrial manufacturing increasingly relies on the ability of autonomous robots to interact with legacy infrastructure, particularly analog gauges. Vision-Language Models (VLMs) have the potential to provide a general solution for gauge reading and have already shown g
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- From Fixed to Free Cameras: Calibration-Free View-Robust Vision-Language-Action Model#343456
- Validating, Not Sampling: Region-Level Robustness of Vision-Language and Vision-Language-Action Models#1036976
- Small yet Assistive: Spatially-Aware Post-Training for Low Vision#1076413
- Detect Before You Leap: Mirage Detection in Vision-Language Models#1012219
- Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments#238651
Record · ID 1118529
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive