arXiv (All)
open access
When can we trust untrusted monitoring? A safety case sketch across collusion strategies
AIs are increasingly being deployed with greater autonomy and capabilities, which increases the risk that a misaligned AI may be able to cause catastrophic harm. Untrusted monitoring -- using one untrusted model to oversee another -- is one approach to reducing risk. Justifying the safety of an untr
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
- A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance#1052238
- The Entanglement Wall: Activation-Space Probes as Risk Detectors, Not Context Adjudicators#1118651
- Can AI Oversight Be Zero Knowledge?#1160918
- Can AI Oversight Be Zero Knowledge?#1157343
- Improve Trust and Adversarial Robustness of AI models in Remote Sensing#307622
Record · ID 1049757
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