3 papers
cs.CL2026
Hi-Q: Hierarchical Evidence-guided Query Refinement for Multi-Hop Question Answering
Jueun Kim, Sungho Park, Wook-Shin Han
A central bottleneck in multi-hop Question Answering (QA) is that the granularity at which a question is expressed often differs from the granularity at which corpus evidence is re…
cs.LG2026
AMUSE: Anytime Muon with Stable Gradient Evaluation
Jueun Kim, Baekrok Shin, Jihun Yun +3
Modern deep learning commonly relies on AdamW with prescribed learning rate schedules, but recent works challenge both components: Schedule-Free optimization removes explicit sched…
cs.CL2026
SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables
Sungho Park, Jueun Kim, Wook-Shin Han
Real-world Table-Text question answering (QA) tasks require models that can reason across long text and source tables, traversing multiple hops and executing complex operations suc…