Papers by Seokgyu Jang
OMHBench: Benchmarking Balanced and Grounded Omni-Modal Multi-Hop Reasoning (2026.findings-acl)
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Seunghee Kim, Ingyu Bang, Seokgyu Jang, Changhyeon Kim, Sanghwan Bae, Jihun Choi, Richeng Xuan, Taeuk Kim
| Challenge: | Existing evaluation frameworks for multimodal large language models suffer from limitations . modality shortcuts and biased reasoning paths are common in such models . |
| Approach: | a new benchmark evaluates omni-modal multi-hop reasoning using 6,144 questions . authors propose OMHBench to address these limitations by comparing modalities . |
| Outcome: | OMHBench evaluates omni-modal multi-hop reasoning on 6,144 questions with balanced reasoning paths . evaluation of 13 state-of-the-art models shows performance gap exists between MLLMs and open-source models . |
Superficial Success vs. Internal Breakdown: An Empirical Study of Generalization in Adaptive Multi-Agent Systems (2026.findings-acl)
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| Challenge: | Adaptive multi-agent systems (MAS) are increasingly adopted as solutions to complex problems. |
| Approach: | They conduct extensive empirical study on adaptive multi-agent systems . they find they are prone to topological overfitting and exhibit illusory coordination . authors urge prioritization of generalization in MAS development and evaluation . |
| Outcome: | a new study shows adaptive multi-agent systems are prone to overfitting and lack coordination . the findings highlight the need to prioritize generalization in MAS development . |