arXiv (All)
open access
Active Causal Discovery Benchmark: Evaluating LLM Agents Under Budgeted Interventions
We introduce the Active Causal Discovery Benchmark (ACDB), an SCM-grounded environment for evaluating whether LLM agents recover causal graph structure from observations and budget-constrained hard interventions. ACDB pairs a linear-Gaussian world generator with a fixed observe-intervene-submit API
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
- Metro-WM: Long-Horizon Latent Planning with Realisable Sub-Goals#1115824
- OnlineWM: Causality-Aware Active Online Learning for Effective World Modeling#1049488
- Toward a Causal Data Management Ecosystem for Decision Making and Agentic AI#440435
- Tags for DAGs: Graph Refinement with Meta-Informed Relations#1118399
- Unsupervised Skill Discovery for Agentic Data Analysis#259468
Record · ID 1115631
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