6 citations · 7 across the 8 of their papers we have counts for
5 papers · 1 filter
CheatBench: Measuring Reward Gaming in AI Agents
Long Phan, Stephen K. Yang, Jason J. Lim +10
Reinforcement learning has helped AI agents solve increasingly difficult tasks, but high rewards do not always reflect the work users intended. In recent incidents and controlled e…
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,…
Where did the ambiguity go? Examining how multimodal models interpret polysemous words
Jasin Cekinmez, Addison J. Wu, Raja Marjieh +1
Human language is highly polysemous. Many common words (e.g., "bank" or "palm") carry several distinct meanings that shape what humans communicate and imagine. Large language model…
SportD: How do VLMs physically strategize?
Jasin Cekinmez, Addison J. Wu, Haotian Xia +6
Vision-language models (VLMs) can describe a scene, but can they act well within one? We study whether VLMs can make sound strategic decisions, using soccer as an objective testbed…
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…