This article explains the key points of HarnessOpt-Bench: Evaluating LLMs at Harness Optimization. 2026-08-06 (arXiv) Ursekar, Varun, Shanker, Apaar, Maurya, Yash, Yasser, Shehab, Kalmath, Vijay S., Chatrath, Veronica, Xue, Yuan. Scale AI Paper Read this article in Korean Summary HARNESSOPT-BENCH evaluates whether an LLM operating through a coding harness can...

이번 글에서는 HarnessOpt-Bench: Evaluating LLMs at Harness Optimization 논문의 핵심 포인트만 간단히 정리한다. 2026년 8월 6일(Arxiv) Ursekar, Varun, Shanker, Apaar, Maurya, Yash, Yasser, Shehab, Kalmath, Vijay S., Chatrath, Veronica, Xue, Yuan. Scale AI 논문 링크 영문판 보기 요약 HARNESSOPT-BENCH는 코딩 하니스를 통해 작동하는 LLM이 비용이 크고 확률적인 평가 환경에서 대상...

이번 글에서는 PAST-Bench: Benchmarking the Foundations of Recursive Self-Improvement in Personal Agents 논문의 핵심 포인트만 간단히 정리한다. 2026년 8월 4일(Arxiv), arXiv Xue, Shuhan, Ding, Zixin, Shen, Yichen, Wang, Yinjie, Yin, Zhenfei, Wu, Yingcheng, Chen, Yuxin, Wang, Mengdi, Yang, Ling. 논문 링크 Github 요약 PAST-Bench는 개인 AI agent가 세션 간에 보존한...

This article explains the key points of AURORA-LM: Autoencoding Unified Representation for Continuous-Latent Diffusion Language Modeling. 2026-08-03 (arXiv) Liang, Jiajun, Liao, Yucheng, Cao, Yukang, Wei, Jiazhe, Li, Ken, Tan, Wende, Zhang, Jiankun, Cui, ZY, Yang, Jingkang, Guo, Liucheng, et al. PRLab, Nanjing University, S-Lab, Nanyang Technological University, Imperial College London...

이번 글에서는 AURORA-LM: Autoencoding Unified Representation for Continuous-Latent Diffusion Language Modeling 논문의 핵심 포인트만 간단히 정리한다. 2026년 8월 3일(Arxiv) Liang, Jiajun, Liao, Yucheng, Cao, Yukang, Wei, Jiazhe, Li, Ken, Tan, Wende, Zhang, Jiankun, Cui, ZY, Yang, Jingkang, Guo, Liucheng, et al. PRLab, Nanjing University, S-Lab, Nanyang Technological University, Imperial College...