6 citations · 7 across the 8 of their papers we have counts for
11 papers
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…
Query Timing Produces Opposite Positional Biases Between LLMs and Humans
Jasin Cekinmez, Addison J. Wu, Thomas L. Griffiths
Positional biases such as recency and primacy effects have been documented in large language models (LLMs), yet the underlying mechanism by which these models make their evaluation…
Quantifying Empirical Compute-Supervision Tradeoffs in RLVR
Ryo Mitsuhashi, Patrick Chen, Isabelle Tseng +2
Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training language models, but in practice, verifiers are rarely perfect. Recent theore…
Guess the Unified Model: How Much Can We Recover from Generated Images?
Jasin Cekinmez, Ryo Mitsuhashi, Addison J. Wu +1
With unified model-generated images now widespread online, attributing their model of origin offers a path toward transparency and deeper insight into the characteristic behaviors…
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…
Large Language Models Develop Novel Social Biases Through Adaptive Exploration
Addison J. Wu, Ryan Liu, Xuechunzi Bai +1
As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important to ensure that they are unbiased. In t…
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)…
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
Ryan Liu, Jiayi Geng, Addison J. Wu +3
Chain-of-thought (CoT) prompting has become a widely used strategy for improving large language and multimodal model performance. However, it is still an open question under which…