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.
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PD-RSAC, a distributionally robust SAC variant with GCN encoder and MILP constraint projection, reports $1.22M net profit on an NYC taxi-based EV simulator while achieving zero feeder violations, outperforming heuristic and other RL baselines.
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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.
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A Distributionally Robust Reinforcement Learning Framework for Constrained Urban EV Dispatch
PD-RSAC, a distributionally robust SAC variant with GCN encoder and MILP constraint projection, reports $1.22M net profit on an NYC taxi-based EV simulator while achieving zero feeder violations, outperforming heuristic and other RL baselines.