E2E-REME outperforms nine LLMs in accuracy and efficiency for end-to-end microservice remediation by using experience-simulation reinforcement fine-tuning on a new benchmark called MicroRemed.
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TREASURE is a transformer model for payment transactions that boosts abnormal behavior detection performance by 111% over production systems and improves recommendation models by 104% when used as an embedding provider.
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E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning
E2E-REME outperforms nine LLMs in accuracy and efficiency for end-to-end microservice remediation by using experience-simulation reinforcement fine-tuning on a new benchmark called MicroRemed.
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TREASURE: The Visa Payment Foundation Model for High-Volume Transaction Understanding
TREASURE is a transformer model for payment transactions that boosts abnormal behavior detection performance by 111% over production systems and improves recommendation models by 104% when used as an embedding provider.