arXiv (All)
open access
DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Vision
Emerging decision models such as JEV provide typed interfaces that map task instructions and observations directly to choices, judgments, and scores. As these models are increasingly used for automated decision-making, understanding their reliability under adversarial manipulation becomes critical.
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
- Beyond Blind Compliance: Benchmarking Task Verification in OCR Reasoning#819692
- Beyond Blind Compliance: Benchmarking Task Verification in OCR Reasoning#809696
- Chinese-Jev: Bringing System One Model to Chinese-Language Tasks#1132166
- SteerProbe: Learning to Bypass Safety Steering in Vision-Language Models#1144084
- SteerProbe: Learning to Bypass Safety Steering in Vision-Language Models#1136777
Record · ID 1344941
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