1 citations · 2 across the 9 of their papers we have counts for
9 papers
Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs
Elizabeth Mieczkowski, Alexander Ku, Tiwalayo Eisape +5
Large language models (LLMs) are increasingly deployed in teams, yet existing coordination approaches often occupy two extremes. Highly structured methods rely on fixed roles, pipe…
Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents
Ryan Liu, Dilip Arumugam, Cedegao E. Zhang +3
While contemporary large language models (LLMs) are increasingly capable in isolation, there are still many difficult problems that lie beyond the abilities of a single LLM. For su…
Lossy communication constrains iterated learning
Ben Prystawski, Dilip Arumugam, Noah D. Goodman
Humans' distinctive role in the world can largely be attributed to our capacity for iterated learning, a process by which knowledge is expanded and refined over generations. A rang…
Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL
Mahsa Bastankhah, Grace Liu, Dilip Arumugam +2
In this work, we take a first step toward elucidating the mechanisms behind emergent exploration in unsupervised reinforcement learning. We study Single-Goal Contrastive Reinforcem…
Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems
Jiayi Geng, Howard Chen, Dilip Arumugam +1
Using AI to create autonomous researchers has the potential to accelerate scientific discovery. A prerequisite for this vision is understanding how well an AI model can identify th…
Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions
Jian-Qiao Zhu, Hanbo Xie, Dilip Arumugam +2
A central goal of cognitive modeling is to develop models that not only predict human behavior but also provide insight into the underlying cognitive mechanisms. While neural netwo…
Toward Efficient Exploration by Large Language Model Agents
Dilip Arumugam, Thomas L. Griffiths
A burgeoning area within reinforcement learning (RL) is the design of sequential decision-making agents centered around large language models (LLMs). While autonomous decision-maki…
PERSONA: A Reproducible Testbed for Pluralistic Alignment
Louis Castricato, Nathan Lile, Rafael Rafailov +2
The rapid advancement of language models (LMs) necessitates robust alignment with diverse user values. However, current preference optimization approaches often fail to capture the…
Social Contract AI: Aligning AI Assistants with Implicit Group Norms
Jan-Philipp Fränken, Sam Kwok, Peixuan Ye +6
We explore the idea of aligning an AI assistant by inverting a model of users' (unknown) preferences from observed interactions. To validate our proposal, we run proof-of-concept s…