28 citations · 113 across the 33 of their papers we have counts for
11 papers · 1 filter
From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMs
Kunal Jha, Max Kleiman-Weiner, Natasha Jaques
Large language models (LLMs) can now improve themselves by revising the instructions they follow, and LLM agents are increasingly orchestrated to work together on complex problems.…
CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition
Lance Ying, Jinzhou Wu, Yingshan Susan Wang +53
Understanding and modeling human intelligence are parallel goals shared by artificial intelligence (AI) and cognitive science. As AI systems grow increasingly capable, in what ways…
Tapes Together Strong: The Co-evolution of Computation and Cooperation
Kunal Jha, Francesco Cicala, Blaise Agüera y Arcas +4
How does cooperation evolve in complex agentic systems? Prior work in evolutionary game theory studies why individuals are incentivized to cooperate by isolating social interaction…
Evaluating Rational Contracting in Natural Language
Bhavyesh Sajja, Max Kleiman-Weiner, Roger Zimmermann +1
The emergence of language-based AI agents promises to transform the scope of machine economic activity. Instead of just proposing bids or following hard-coded protocols, such agent…
Commitment To Cooperation With Self-Negotiated Contracts
Tim Wyse, Kaitlin Bustos, Yulia Volkova +1
As AI agents operate with increasing autonomy in a multi-agent world, they will need to learn to cooperate with other agents and with humans to generate mutual benefits. However, c…
AI Assistants Overassist
Verona Teo, Raghav Jain, Tobias Gerstenberg +1
Large language models (LLMs) are increasingly used as tutors and thought partners, helping users reason through problems. While guidance from AI assistants can scaffold thinking an…
A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing
Jared Moore, Noah Goodman, Nick Haber +1
Large language models can shift human beliefs across high-stakes domains, but most persuasion studies rely on pre/post belief change. These endpoint measures identify whether persu…
Task diversity produces systematic transfer but inhibits continual reinforcement learning
Purab Seth, Neil Shah, Ishaan Sinha +4
Continual reinforcement learning (RL) aims to produce agents that never stop adapting to new tasks. A key question is how this interacts with the diversity of tasks an agent experi…
Boundedly Rational Meta-Learning in Sequential Consumer Choice
Mehrzad Khosravi, Max Kleiman-Weiner, Hema Yoganarasimhan
Many consumer decisions involve repeated choices under uncertainty, where experience in one context may inform decisions in another. For example, experience with a brand in one mar…
How LLMs Distort Our Written Language
Marwa Abdulhai, Isadora White, Yanming Wan +4
Large language models (LLMs) are used by over a billion people globally, most often to assist with writing. In this work, we demonstrate that LLMs not only alter the voice and tone…
Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians
Kartik Chandra, Max Kleiman-Weiner, Jonathan Ragan-Kelley +1
"AI psychosis" or "delusional spiraling" is an emerging phenomenon where AI chatbot users find themselves dangerously confident in outlandish beliefs after extended chatbot convers…