arXiv (OAI Expanded)
open access
TraceMind: Predicting User Information Uptake from Low-Cost Interaction Traces during Human-LLM Content Co-Generation
In human-LLM content co-generation, AI-generated information can enter final artifacts without being adequately processed by users, creating risks when artifacts are shared or acted upon. We study whether recognition-level uptake of atomic information units can be assessed in open-ended co-generatio
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
- SsgCaps: A controlled dataset for the evaluation of sound scene generation algorithms#1063065
- SONAR: Task-Aware Code Summary Evaluation for LLM Consumers Without References#429435
- On-Device Named-Entity Recognition: A Deployability Study of Accuracy, Cost, Reliability, and Confidence#1154630
- Ceci n'est pas une pipe: AI systems as semantic abstractions#998292
- User Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation#155526
Record · ID 1070089
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