6 citations · 11 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
Enhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling
Marc Walden, Jason Liu, Shaashwath Sivakumar +2
We investigate multi-agent deep reinforcement learning and propose two enhancements to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. First, we introduce a…
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…
WaterVideoQA: ASV-Centric Perception and Rule-Compliant Reasoning via Multi-Modal Agents
Runwei Guan, Shaofeng Liang, Ningwei Ouyang +9
While autonomous navigation has achieved remarkable success in passive perception (e.g., object detection and segmentation), it remains fundamentally constrained by a void in knowl…
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…
Modeling Accessibility-Constrained Networks with Time-Weighted Graphs
Marc Walden, Jason Liu, Ryan Liu +1
Accessibility for the physically disabled is a prevalent issue on university campuses, where stairs and steep slopes make navigating campus arduous. Our work proposes a pipeline to…