arXiv (All)
open access
Knowing What Not to Answer: Selective Non-Compliance in Vision-Language Models
Vision-language models (VLMs) are expected to respond helpfully to appropriate requests while withholding compliance with requests that are incorrect, unsafe, infeasible, or unanswerable. However, existing benchmarks predominantly evaluate non-compliance at the level of the query as a whole, assumin
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
- It Takes Little to Rewrite Perception: Targeted Semantic Substitution in Vision-Language Models at $ε\leq 4/255$#1142899
- Representational alignment yields generalizable safety in language models#926872
- DICS: Exploring Data Intrinsic Consistency for Visual Instruction Selection#822020
- PRISM-VLM: A Multi-Axis Discriminative Benchmark for Compact Vision-Language Models#1063606
- VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-training#1131849
Record · ID 971396
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