CAMA applies a three-step feature engineering procedure to LLM agents and reports 55 to 92 percent accuracy on ML monitoring report questions, outperforming six baselines.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Feature Engineering for Agents: An Adaptive Cognitive Architecture for Interpretable ML Monitoring
CAMA applies a three-step feature engineering procedure to LLM agents and reports 55 to 92 percent accuracy on ML monitoring report questions, outperforming six baselines.