most citedQUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL

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cs.AI2026

The End of Reward Engineering: How LLMs Are Redefining Multi-Agent Coordination

Haoran Su, Yandong Sun, Congjia Yu

Reward engineering, the manual specification of reward functions to induce desired agent behavior, remains a fundamental challenge in multi-agent reinforcement learning. This diffi…

cs.AI2025★ 1 cited

QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL

Cong Yu, Valter Uotila, Shilong Deng +5

Designing and optimizing task-specific quantum circuits are crucial to leverage the advantage of quantum computing. Recent large language model (LLM)-based quantum circuit generati…