22 citations · 22 across the 9 of their papers we have counts for
3 papers · 1 filter
PROGRESS: Coverage-guided RL to Train Search-augmented LLM Agent
Sudipta Paul, Vijay Srinivasan, Vivek Kulkarni +4
Existing search-augmented LLM agents are trained using Reinforcement Learning to boost its reasoning capabilities. However, these approaches primarily rely on outcome-level rewards…
SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks
Hongcheng Gao, Hailong Qu, Jingyi Tang +18
Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world. However, existing benchmarks predomin…
Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most
Tahreem Yasir, Wenbo Li, Sam Gilson +3
Effective tutoring requires distinguishing optimal, valid but suboptimal, and incorrect student solutions, a distinction central to intelligent tutoring systems (ITS) but untested…