Author

Yongil Kim

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Preprint Aug 2026

K-EXAONE 2.0 Technical Report

K-EXAONE 2.0 improves over K-EXAONE and remains competitive with open-weight models, showing its largest gains in agentic coding and long-context understanding and its clearest strengths in long-context retrieval and safety.

Eunbi Choi, Kibong Choi, Sehyun Chun et al. · 0 citations