35 citations · 46 across the 7 of their papers we have counts for
10 papers
Scaling Multi-Agent Intelligence with Substrate-Centric Coordination
Zhenting Qi, Zhuokai Zhao, Rulin Shao +7
Existing large language model (LLM) based multi-agent systems have been shown to scale poorly, and we identify three problems: reading burden that grows with the group, useful info…
Muse Spark Safety & Preparedness Report
Cristina Menghini, Peter Ney, Hamza Kwisaba +117
Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framewo…
HorizonBench: Long-Horizon Personalization with Evolving Preferences
Shuyue Stella Li, Bhargavi Paranjape, Kerem Oktar +9
User preferences evolve across months of interaction, and tracking them requires inferring when a stated preference has been changed by a subsequent life event. We define this prob…
Under the Influence: Evaluating and Improving LLM Vigilance in Agentic Tasks
Sasha Robinson, Katherine M. Collins, Ilia Sucholutsky +1
Large Language Models (LLMs) are becoming increasingly autonomous assistants, capable of accomplishing series of complex tasks by interacting with open world domains such as softwa…
Are Large Language Models Sensitive to the Motives Behind Communication?
Addison J. Wu, Ryan Liu, Kerem Oktar +2
Human communication is motivated: people speak, write, and create content with a particular communicative intent in mind. As a result, information that large language models (LLMs)…
Identifying, Evaluating, and Mitigating Risks of AI Thought Partnerships
Kerem Oktar, Katherine M. Collins, Jose Hernandez-Orallo +4
Artificial Intelligence (AI) systems have historically been used as tools that execute narrowly defined tasks. Yet recent advances in AI have unlocked possibilities for a new class…
When Large Language Models are More PersuasiveThan Incentivized Humans, and Why
Jiacheng Liu, Francesco Salvi, Philipp Schoenegger +37
Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (…
Dimensions of Disagreement: Unpacking Divergence and Misalignment in Cognitive Science and Artificial Intelligence
Kerem Oktar, Ilia Sucholutsky, Tania Lombrozo +1
The increasing prevalence of artificial agents creates a correspondingly increasing need to manage disagreements between humans and artificial agents, as well as between artificial…
Getting aligned on representational alignment
Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller +30
Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we m…
Can Humans Do Less-Than-One-Shot Learning?
Maya Malaviya, Ilia Sucholutsky, Kerem Oktar +1
Being able to learn from small amounts of data is a key characteristic of human intelligence, but exactly {\em how} small? In this paper, we introduce a novel experimental paradigm…