1 citations · 1 across the 3 of their papers we have counts for
5 papers
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…
ZX-DB: A Graph Database for Quantum Circuit Simplification and Rewriting via the ZX-Calculus
Valter Uotila, Cong Yu, Bo Zhao
Quantum computing is an emerging computational paradigm with the potential to outperform classical computers in solving a variety of problems. To achieve this, quantum programs are…
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…
SHARP: Shared State Reduction for Efficient Matching of Sequential Patterns
Cong Yu, Tuo Shi, Matthias Weidlich +1
The detection of sequential patterns in data is a basic functionality of modern data processing systems for complex event processing (CEP), OLAP, and retrieval-augmented generation…
Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization
Linus Jern, Valter Uotila, Cong Yu +1
Large language models (LLMs) have achieved remarkable outcomes in complex problems, including math, coding, and analyzing large amounts of scientific reports. Yet, few works have e…