5 accepted papers
Auditing recommendation systems has attracted growing attention due to increasing concerns over filter bubbles, unfairness, and data misuse. A common approach is sock-puppet auditing, where autonomous agents interact with platforms to reveal risks. However, existing approaches rely on hard-coded age
While LLM-based agents excel at individual tasks, effective collaboration with realistic human partners remains challenging. Most of the existing conversation-level collaborative studies lack grounded interaction and behavioral execution, motivating the need for cooperative game environments that en…
Fairness auditing aims to assess whether a model is fair, playing a critical role in identifying potential risks in deployed AI systems. In practice, due to limited access, third-party auditors often rely on self-collected datasets (e.g., via sock-puppets), which may differ from real-world deploymen
Computerized Adaptive Testing (CAT) measures student ability by iteratively selecting informative questions, with core components being the Cognitive Diagnosis Model (CDM) and selection strategy. Current research focuses on optimizing the selection strategy, assuming relatively accurate CDM results.
Robotic grasping is one of the most fundamental robotic manipulation tasks and has been actively studied. However, how to quickly teach a robot to grasp a novel target object in clutter remains challenging. This paper attempts to tackle the challenge by leveraging object attributes that facilitate r…