Papers by Sunghwan Steve Cho
MATA: Multi-Agent Framework for Reliable and Flexible Table Question Answering (2026.findings-acl)
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| Challenge: | Recent advances in Large Language Models have significantly improved table understanding tasks . practical deployment of TableQA systems presents several persistent challenges . |
| Approach: | They propose a multi-agent TableQA framework that leverages multiple reasoning paths and tools built with small language models. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy and efficient reasoning while avoiding excessive LLM inference. |
MI-CXR: A Benchmark for Longitudinal Reasoning over Multi-Interval Chest X-rays (2026.findings-acl)
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| Challenge: | Existing medical VQA benchmarks focus on single images or short-horizon image pairs. |
| Approach: | They propose a benchmark for standardized evaluation of longitudinal reasoning over multi-visit sequences. |
| Outcome: | The proposed benchmark shows low overall performance (29.3% accuracy) and is only modestly above random guessing. |