822 citations · 2.9k across the 43 of their papers we have counts for
6 papers · 1 filter
How does Adversarial Influence Scale in Multi-Agent Systems?
Addison J. Wu, Jasin Cekinmez, Michel Liao +2
Multi-agent deliberation can improve performance, but what happens when some agents do not act in good faith? In practice, an agent may be deceptive and work to subvert the group,…
Cognitive Architectures for Language Agents
Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan +1
Recent efforts have augmented large language models (LLMs) with external resources (e.g., the Internet) or internal control flows (e.g., prompt chaining) for tasks requiring ground…
Reflexion: Language Agents with Verbal Reinforcement Learning
Noah Shinn, Federico Cassano, Edward Berman +3
Large language models (LLMs) have been increasingly used to interact with external environments (e.g., games, compilers, APIs) as goal-driven agents. However, it remains challengin…
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta +7
Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive bi…
Connecting Context-specific Adaptation in Humans to Meta-learning
Rachit Dubey, Erin Grant, Michael Luo +2
Cognitive control, the ability of a system to adapt to the demands of a task, is an integral part of cognition. A widely accepted fact about cognitive control is that it is context…
Learning Rewards from Linguistic Feedback
Theodore R. Sumers, Mark K. Ho, Robert D. Hawkins +2
We explore unconstrained natural language feedback as a learning signal for artificial agents. Humans use rich and varied language to teach, yet most prior work on interactive lear…