1 citations · 2 across the 8 of their papers we have counts for
8 papers
RoboWits: Unexpected Challenges for Robotic Creative Problem Solving
Chunru Lin, Hongxin Zhang, Fenghao Yu +5
The ability to reason, adapt, and creatively solve problems under unexpected challenges is essential for robots operating in real-world environments. However, current robotic bench…
Process Matters more than Output for Distinguishing Humans from Machines
Milena Rmus, Mathew D. Hardy, Thomas L. Griffiths +1
Reliable human-machine discrimination is becoming increasingly important as Large Language Models and autonomous agents are deployed in online settings. Existing approaches evaluat…
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…
Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest
Addison J. Wu, Ryan Liu, Shuyue Stella Li +2
Large language models (LLMs) are trained to align with user preferences through methods like reinforcement learning. Yet models are beginning to be deployed not solely to satisfy u…
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…
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…
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…