10 citations · 13 across the 15 of their papers we have counts for
16 papers
RoboTalk: Learning Multi-Robot Communication and Coordination from Multimodal Demonstrations
Dorian Benhamou Goldfajn, Mason Nakamura, Saaduddin Mahmud +3
Multi-robot collaboration could enable more efficient and scalable solutions to complex robotic tasks, but collaboration under partial observability remains challenging. Natural-la…
Targeting World Models to Compromise Robot Learning Pipelines
Ethan Rathbun, Ahmed Agha, Saaduddin Mahmud +3
World models have recently seen a rapid growth in both their popularity and capability as more data efficient tools for generating robot training data or simulating real world envi…
Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems
Mason Nakamura, Abhinav Kumar, Saswat Das +5
Multi-agent systems, where LLM agents communicate through free-form language, enable sophisticated coordination for solving complex cooperative tasks. This surfaces a unique safety…
Verification Required: The Impact of Information Credibility on AI Persuasion
Saaduddin Mahmud, Eugene Bagdasarian, Shlomo Zilberstein
Agents powered by large language models (LLMs) are increasingly deployed in settings where communication shapes high-stakes decisions, making a principled understanding of strategi…
Terrarium: Revisiting the Blackboard for Multi-Agent Safety, Privacy, and Security Studies
Mason Nakamura, Abhinav Kumar, Saaduddin Mahmud +3
A multi-agent system (MAS) powered by large language models (LLMs) can automate tedious user tasks such as meeting scheduling that requires inter-agent collaboration. LLMs enable n…
Aligning LLMs on a Budget: Inference-Time Alignment with Heuristic Reward Models
Mason Nakamura, Saaduddin Mahmud, Kyle H. Wray +2
Aligning LLMs with user preferences is crucial for real-world use but often requires costly fine-tuning or expensive inference, forcing trade-offs between alignment quality and com…