About
I’m Mason Nakamura, a fourth-year Ph.D. student in Computer Science at UMass Amherst, working in the College of Information and Computer Sciences (CICS) as a member of the Resource-Bounded Reasoning Lab, under the guidance of Professor Shlomo Zilberstein. I’m currently a research intern at Microsoft Research working on collaborative multi-agent systems. My research is funded by the NSF Graduate Research Fellowship where I work on multi-agent safety and alignment.
I’m interested in the following research areas:
- Emergent Behaviors of Multi-Agent Systems: What safety-relevant behaviors emerge in long-horizon multi-agent trajectories? How can such behaviors be monitored and mitigated?
- Safety Evaluations and Auditing of Multi-Agent Systems: How can we prevent catastrophic risks of multi-agent systems via online auditing techniques and robust offline evaluations?
- Improving Multi-Agent Coordination and Communication: How can we improve multi-agent coordination capabilities? Can we do so without expensive multi-agent reinforcement learning?
If you are interested in collaboration, let’s have a chat!
Previously, I had internships at the Center for Human-Compatible Artificial Intelligence (CHAI) / Berkeley Artificial Intelligence Research (BAIR) (Summer 2023) on building ethically-compliant autonomous systems using constrained deep reinforcement learning, CHAI/BAIR (Summer-Fall 2022) building a formal framework for computationally bounded agents, Moravian University (Summer 2021 NSF REU) working on extremal and spectral graph theory, and Michigan State University (Summer 2020 NSF REU) working on geometric deep learning.
Selected Papers
* indicates equal contribution
RoboTalk: Learning Multi-Robot Communication and Coordination from Multimodal Demonstrations
Terrarium: Revisiting the Blackboard for Multi-Agent Safety, Privacy, and Security Studies
MAPLE: A Framework for Active Preference Learning Guided by Large Language Models
Inference-Aware Prompt Optimization for Aligning Black-Box Large Language Models
Aligning LLMs on a Budget: Inference-Time Alignment with Heuristic Reward Models
News
2026
September
- Our paper on auditing collusion in cooperative multi-agent systems has been accepted to NeurIPS-2026 as a poster presentation!
- Our new paper, RoboTalk: Learning Multi-Robot Communication and Coordination from Multimodal Demonstrations, is now available on arXiv!
- I will continue working with Microsoft Research through the fall semester!
June
- I will be a research intern at Microsoft Research for the summer in Redmond on the Office of Applied Research team!
2025
November
- Our paper on inference-aware prompt optimization for black-box LLMs has been accepted to AAAI-2026 as an oral presentation!
June
- I was awarded an NSF Graduate Research Fellowship.
2024
December
- Our paper on accelerating reward modeling using LLM guidance and abstract concepts has been accepted to AAAI-2025!
- Our paper on employing critical states in experience selection strategies for continual reinforcemnt learning has been accepted to AAAI-2025 Workshop on generalization in planning (GenPlan)!
2023
June
- Our paper on formalizing an intelligent system as a composition of contract algorithms has been accepted to IROS-2023!
May
- I graduated summa cum laude from Marist College, majoring in applied mathematics and data science with a minor in computer science.
March
- I accepted a computer science MS/PhD offer at UMass Amherst in the Resource-Bounded Reasoning Lab directed by Shlomo Zilberstein.
2022
May-November
- Visiting scholar at the Center for Human-Compatible AI (CHAI) at UC Berkeley on building well-founded AI using metareasoning.
March
- Our paper on using combinatorics to solve a combinatorial geometry problem has been accepted to MAA Mathematics Magazine!
2021
May-August
- Visiting scholar at Moravian University as part of an NSF REU on computational methods in discrete mathematics working on spectral and extremal graph theory.
2020
May-August
- Visiting scholar at Michigan State University as part of an NSF REU on experimental mathematics working on geometric deep learning.