arXiv (All)
open access
UnexpectedParrots: Iterating Behavioral Experiment Design with In-Silico Simulations
Running behavioral experiments is expensive, slow, and prone to design flaws, often detectable after collecting data. We introduce UnexpectedParrots to iteratively refine behavioral study designs before a researcher recruits costly human participants. UnexpectedParrots relies on reasoning traces in
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
- Sequential Efficient Designs#1345432
- Lay Sensitivity to Sampling and Selection Errors in Scientific Studies#1079025
- Data-Driven Simulations to Assess the Impact of Study Imperfections in Time-to-Event Analyses#345329
- ECLAIR: A Causally-Grounded AI Framework for Scientific Discovery in Empirical Software Engineering#424035
- Open-Source Autonomous Driving System Analysis and Multi-Disciplinary Hardware-in-the-Loop Research Paradigm with Reinforcement-Learning Testing and Large Language Models#622609
Record · ID 1400173
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