arXiv (All)
open access
Dream2Discern: Learning from Hard Latent Variants for Runtime Failure Prediction in Multi-Agent Systems
Reliable deployment of LLM-based multi-agent systems calls for runtime auditors that predict terminal outcomes from observed execution prefixes. These auditors typically learn from a limited corpus of trajectories with verified outcomes. Expanding this corpus requires costly execution and verificati
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
- AgentRx: Diagnosing AI Agent Failures from Execution Trajectories#798756
- AgentRx: Diagnosing AI Agent Failures from Execution Trajectories#797342
- Auditing Latent-Space Monitors for Autonomous Driving#1104756
- Who Guards the Benchmarks? Automated Auditing of LLM Agent Benchmarks#1186455
- Lightweight, Rubric-Guided Trajectory Evaluation for Production AI Agents#1187993
Record · ID 1327661
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