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Focus Shifts to Structured and Verifiable AI Development

The observed trend moves beyond simple model capability improvements toward building highly reliable systems that function within complex, real-world constraints. This is evidenced by the focus on using composable architecture frameworks or subjecting AI to rigorous validation through high-difficulty benchmarks like the Ramanujan Challenge. Furthermore, securing efficiency and robustness has become a critical concern across diverse domains, from optimizing RAG performance to streamlining embedded system security patches.

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arXivJuly 30, 2026

Parameter-Free Dynamic Regret for Online Convex Optimization under Heavy-Tailed Noise

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중량 꼬리 노이즈(heavy-tailed noise) 환경의 비정상적 상황에서 작동하는 새로운 온라인 볼록 최적화(OCO) 알고리즘 HT-PAder를 제안합니다. 이 알고리즘은 파라미터에 대한 사전 지식 없이도 보편적 동적 후회를 달성하며, 유한 분산 환경에서도 최초의 파라미터 프리 미니맥스 보증을 제공합니다.

online-convex-optimization
heavy-tailed-noise
optimization
adagrad
machine-learning-theory

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Focus Shifts to Structured and Verifiable AI Development

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Focus Shifts to Structured and Verifiable AI Development

Parameter-Free Dynamic Regret for Online Convex Optimization under Heavy-Tailed Noise

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Keep It InMind A benchmark for the implicit-association blind spot in agent memory. It targets the implicit-association blind spot: a memor…