most citedWhen AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research

5 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI2025

Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers

Wooseok Seo, Seungju Han, Jaehun Jung +6

Fact verification is essential for ensuring the reliability of LLM applications. In this study, we evaluate 12 pre-trained LLMs and one specialized fact-verifier, including frontie…

cs.CL2025

Persona Dynamics: Unveiling the Impact of Personality Traits on Agents in Text-Based Games

Seungwon Lim, Seungbeen Lee, Dongjun Min +1

Artificial agents are increasingly central to complex interactions and decision-making tasks, yet aligning their behaviors with desired human values remains an open challenge. In t…

cs.CL20255 cited

When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research

Guijin Son, Jiwoo Hong, Honglu Fan +8

Recent advances in large language models (LLMs) have fueled the vision of automated scientific discovery, often called AI Co-Scientists. To date, prior work casts these systems as…

cs.AI2025

VisEscape: A Benchmark for Evaluating Exploration-driven Decision-making in Virtual Escape Rooms

Seungwon Lim, Sungwoong Kim, Jihwan Yu +3

Escape rooms present a unique cognitive challenge that demands exploration-driven planning: with the sole instruction to 'escape the room', players must actively search their envir…

cs.CV2025

MASS: Overcoming Language Bias in Image-Text Matching

Jiwan Chung, Seungwon Lim, Sangkyu Lee +1

Pretrained visual-language models have made significant advancements in multimodal tasks, including image-text retrieval. However, a major challenge in image-text matching lies in…