In active object search, a robot must decide where to look and when to stop. Imperfect detection complicates both decisions: a non-detection may indicate occlusion, an out-of-view target, or a false negative, while a detection may be a false positive. This raises the question of which representations a world model needs to support reliable search under imperfect detection. We introduce Bayesian Active Miss-aware Object Search (BAMOS) to investigate how target belief, occupancy belief, and detector errors affect search decisions. Through comprehensive ablations in Isaac Sim and HSSD houses, we examine the roles of these representations and their update mechanisms across different detector configurations. Notably, target belief and its update mechanisms are critical to search performance, with their contribution generally increasing as detection errors become more prevalent. These findings highlight the importance of maintaining and updating target beliefs to support reliable active search under uncertain detector observations, providing insights into the design of world models for embodied agents. Code is available in https://github.com/gokyeongryeol/BAMOS.
The performance of deep neural networks is strongly influenced by the quality of their training data. However, mitigating dataset bias by manually curating challenging edge cases remains a major bottleneck. To address this, we propose an automated pipeline for text-guided edge-case synthesis. Our approach employs a Large Language Model, fine-tuned via preference learning, to rephrase image captions into diverse textual prompts that steer a Text-to-Image model toward generating difficult visual scenarios. Evaluated on the FishEye8K object detection benchmark, our method achieves superior robustness, surpassing both naive augmentation and manually engineered prompts. This work establishes a scalable framework that shifts data curation from manual effort to automated, targeted synthesis, offering a promising direction for developing more reliable and continuously improving AI systems. Code is available at https://github.com/gokyeongryeol/ATES.