arXiv (OAI Expanded)
open access
CAPTCHAs in the Agentic Era: Solvers That Learn from Every Encounter
Vision-language models (VLMs) can solve visual CAPTCHAs without task-specific training, but the agents built on them approach every challenge from scratch. For such an agent, the hundredth instance of a familiar puzzle costs as much time and compute as the first. Specialized detectors invert the tra
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
- CAPTCHAs in the Agentic Era: Solvers That Learn from Every Encounter#924277
- Invisible in Space, Visible in Time: Motion Vision CAPTCHA against GUI Agents#1063672
- CAPTCHAs in the Agentic Era: Solvers That Learn from Every Encounter#632774
- FailBench: How Reliable are VLMs at Judging Robot Task Success?#926515
- Frontier vision-language models have overtaken young adults at detecting AI-generated portraits -- but not their calibration#799430
Record · ID 920046
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