Sung Ju Hwang

Don't Just Trust Your Memory: Evidence-Linked Memory Reduces Memory-Induced Degradation in LLM Agents

Yukyeong Lee, Yumin Choi, Jinheon Baek, et al.

Channel Selection for Mixture-of-Experts Decoding Needs No Calibration Data

Geunhyoung Kim, Sung Ju Hwang

RunningTab: Direct Workspace Interaction with Environment-Side Tabs

Jinheon Baek, Soyeong Jeong, Yumin Choi, et al.

Agent Memory Distillation: Empowering Small LLM Agents with Hierarchical Teacher Memory

Taeil Kim, Kangsan Kim, Sung Ju Hwang

Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents

Kangsan Kim, Minki Kang, Taeil Kim, et al.

MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Model

Youngwan Lee, Soojin Jang, Yoorhim Cho, et al.

This paper introduces MultihopSpatial, a new benchmark to test how well AI vision-language models can understand complex spatial relationships in images, such as 'the object to the left of the object above the red box.' The benchmark includes a new evaluation metric (Acc@50IoU) that checks both whether the model answers correctly and whether it can precisely pinpoint the location in the image, which is crucial for robots performing real-world tasks. Testing 37 state-of-the-art models reveals that spatial reasoning remains challenging, but training models on their new dataset improves both the models' spatial understanding and their ability to perform physical manipulation tasks.