6 papers
COOP: Defining, Observing, and Repairing Cooperation in LLM Multi-Agent Systems
Hanqing Yang, Narjes Nourzad, Shiyu Chen +3
Many complex tasks require extended effort, diverse capabilities, or coordinated actions beyond what a single agent can provide. However, simply adding more agents does not guarant…
Memory-Based Advantage Shaping for LLM-Guided Reinforcement Learning
Narjes Nourzad, Carlee Joe-Wong
In environments with sparse or delayed rewards, reinforcement learning (RL) incurs high sample complexity due to the large number of interactions needed for learning. This limitati…
MIRA: Memory-Integrated Reinforcement Learning Agent with Limited LLM Guidance
Narjes Nourzad, Carlee Joe-Wong
Reinforcement learning (RL) agents often suffer from high sample complexity in sparse or delayed reward settings due to limited prior structure. Large language models (LLMs) can pr…
DR. WELL: Dynamic Reasoning and Learning with Symbolic World Model for Embodied LLM-Based Multi-Agent Collaboration
Narjes Nourzad, Hanqing Yang, Shiyu Chen +1
Cooperative multi-agent planning requires agents to make joint decisions with partial information and limited communication. Coordination at the trajectory level often fails, as sm…
AURA: Adaptive Unified Reasoning and Automation with LLM-Guided MARL for NextG Cellular Networks
Narjes Nourzad, Mingyu Zong, Bhaskar Krishnamachari
Next-generation (NextG) cellular networks are expected to manage dynamic traffic while sustaining high performance. Large language models (LLMs) provide strategic reasoning for 6G…
Smart Routing with Precise Link Estimation: DSEE-Based Anypath Routing for Reliable Wireless Networking
Narjes Nourzad, Bhaskar Krishnamachari
In dynamic and resource-constrained environments, such as multi-hop wireless mesh networks, traditional routing protocols often falter by relying on predetermined paths that prove…