Cemri, Mert

Why Do Multi-Agent LLM Systems Fail?

Cemri, M., Pan, M. Z., Yang, S., Agrawal, L. A., Chopra, B., Tiwari, R., Keutzer, K., Parameswaran, A., Klein, D., Ramchandran, K., et al. (2025) · arXiv

Multi-Agent Systems and NegotiationNatural Language Processing Techniques

Cited by 630

DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning

Bai, H., Zhou, Y., Cemri, M., Pan, J., Suhr, A., Levine, S., & Kumar, A. (2024) · arXiv

Reinforcement Learning in RoboticsIoT and Edge/Fog Computing

Cited by 20

DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning

Bai, H., Zhou, Y., Cemri, M., Pan, J., Suhr, A., Levine, S., & Kumar, A. (2024) · Advances in Neural Information Processing Systems 37

Reinforcement Learning in RoboticsIoT and Edge/Fog Computing

Cited by 20

AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization

Cemri, M., Agrawal, S., Gupta, A., Liu, S., Cheng, A., Mang, Q., Naren, A., Erdogan, L. E., Sen, K., Zaharia, M., et al. (2026) · arXiv

Evolutionary Algorithms and ApplicationsMetaheuristic Optimization Algorithms Research

Cited by 2

EvoX: Meta-Evolution for Automated Discovery

Liu, S., Agarwal, S., Maheswaran, M., Cemri, M., Li, Z., Mang, Q., Naren, A., Boneh, E., Cheng, A., Pan, M. Z., et al. (2026) · arXiv

Evolutionary Algorithms and ApplicationsMetaheuristic Optimization Algorithms Research

Cited by 1