arXiv (All)
open access
Policy-Verified Self-Training: Learning Agent Compliance from Verifiable Policy Rewards
Runtime policy verifiers can prevent agents from executing disallowed actions, but their decisions are typically used only for enforcement and discarded afterward. We ask whether these decisions can instead become verifiable policy rewards for training the agents they guard. We introduce Policy-Veri
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
- Runtime Compliance Verification for AI Agents#287188
- SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models#1344697
- SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models#1318896
- How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement#370275
- StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents#1247869
Record · ID 1345195
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