LATS-RCA applies multi-agent Language Agent Tree Search to automate root cause analysis in microservices, reporting high accuracy on a small open-source Java system but lower accuracy in a complex production environment.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A robust semi-Markov RL agent with MILP feasibility projection and Wasserstein ambiguity set achieves $1.22M net profit on an NYC EV simulator with zero feeder violations, outperforming heuristic and other RL baselines.
citing papers explorer
-
Multi-Agent Systems for Root Cause Analysis in Microservices
LATS-RCA applies multi-agent Language Agent Tree Search to automate root cause analysis in microservices, reporting high accuracy on a small open-source Java system but lower accuracy in a complex production environment.
-
Semi-Markov Reinforcement Learning for City-Scale EV Ride-Hailing with Feasibility-Guaranteed Actions
A robust semi-Markov RL agent with MILP feasibility projection and Wasserstein ambiguity set achieves $1.22M net profit on an NYC EV simulator with zero feeder violations, outperforming heuristic and other RL baselines.