9 papers
HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning
Runze Zhao, Dongruo Zhou, Sumit Kumar Jha +2
Cooperative multi-agent reinforcement learning (MARL) often relies on communication to mitigate partial observability, yet most existing protocols treat messages as flat dense vect…
PreUnlearn: Auditing Collateral Knowledge Damage Before Large Language Model Unlearning
Bo Su, Ankit Shah, Thai Le
Machine unlearning for large language models (LLMs) aims to remove specified knowledge while preserving the rest of the model's capabilities. However, the boundary between knowledg…
Inference Time Optimization with Confidence Dynamics
Yu Wang, Minghao Liu, Jiayun Wang +3
Inference time optimization techniques, such as repeated sampling, have significantly advanced the reasoning capabilities of Large Language Models (LLMs). However, the critical rol…
Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond
Minghao Liu, Zonglin Di, Jiaheng Wei +15
Large-scale data collection is essential for developing personalized training data, mitigating the shortage of training data, and fine-tuning specialized models. However, creating…
Training-Free Agentic AI: Probabilistic Control and Coordination in Multi-Agent LLM Systems
Mohammad Parsa Hosseini, Ankit Shah, Saiyra Qureshi +3
Multi-agent large language model (LLM) systems enable complex, long-horizon reasoning by composing specialized agents, but practical deployment remains hindered by inefficient rout…
ProRefine: Inference-Time Prompt Refinement with Textual Feedback
Deepak Pandita, Tharindu Cyril Weerasooriya, Ankit Parag Shah +3
Agentic workflows, where multiple AI agents collaborate to accomplish complex tasks like reasoning or planning, play a substantial role in many cutting-edge commercial applications…