arXiv (OAI Expanded)
open access
Available but Unclaimed: An Empirical Study of Human-AI Synergy
People increasingly reason with large language models (LLMs), yet complementary capabilities do not guarantee outperforming both components. In a between-subjects study, participants (N=535) solved a 40-item battery of matrix reasoning, mental rotation, syllogisms, and letter-string analogies, unaid
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
- Learning What to Retain: Gated-Memory Routing for Efficient Collaboration in Multi-Agent LLM Systems#997536
- Semantic Representational Strength in the Precuneus and Lateral ATL Predicts Successful Factual Learning.#227733
- LLMs are not Artificial intelligence#95494
- Book review D'Agostino et al.: Depth-Bounded Reasoning. Volume I: Classical Propositional Logic: College Publications, 2024, xvii + 225. ISBN 978-1-84890-442-2#111830
- Using memory to accelerate planning#1078977
Record · ID 1122793
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