SafeCodeRL: Security-Constrained Multi-Agent Reinforcement Learning for Trustworthy LLM-Generated IoT/CPS Software.
- DOI
- 10.3390/s26113502
- Published
- 2026 Jun 2
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.3390/s26113502,
title = {SafeCodeRL: Security-Constrained Multi-Agent Reinforcement Learning for Trustworthy LLM-Generated IoT/CPS Software.},
author = {Wang Z and Chen J and Wei Z and Lin L and Tong G},
year = {2026},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s26113502},
url = {https://doi.org/10.3390/s26113502}
}RIS
TY - JOUR TI - SafeCodeRL: Security-Constrained Multi-Agent Reinforcement Learning for Trustworthy LLM-Generated IoT/CPS Software. AU - Wang Z AU - Chen J AU - Wei Z AU - Lin L AU - Tong G PY - 2026 JO - Sensors (Basel, Switzerland) DO - 10.3390/s26113502 UR - https://doi.org/10.3390/s26113502 ER -
APA
Z, W., J, C., Z, W., L, L., & G, T. (2026). SafeCodeRL: Security-Constrained Multi-Agent Reinforcement Learning for Trustworthy LLM-Generated IoT/CPS Software.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26113502
Source records
- pubmed · retrieved 2026-09-25T10:16:46.348Z