activity
20222026
collaborators

7 papers

cs.RO2026

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…

cs.MA2026

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…

cs.AI2025

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…

cs.LG2025

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…

cs.CL2025

Inference-Aware Prompt Optimization for Aligning Black-Box Large Language Models

Saaduddin Mahmud, Mason Nakamura, Kyle Hollins Wray +1

Prompt optimization methods have demonstrated significant effectiveness in aligning black-box large language models (LLMs). In parallel, inference scaling strategies such as Best-o…

cs.LG2024

MAPLE: A Framework for Active Preference Learning Guided by Large Language Models

Saaduddin Mahmud, Mason Nakamura, Shlomo Zilberstein

The advent of large language models (LLMs) has sparked significant interest in using natural language for preference learning. However, existing methods often suffer from high comp…

math.CO2022

Graph Coloring as a Measure of Network Vulnerability

Mia Destefano, Grace Mulry, Mason Nakamura +2

We consider new parameters for conditional network vulnerability related to graph coloring. We define a network to be in operation if the chromatic number (or index) is greater tha…