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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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arXivSeptember 17, 2026

How Many Labels Does Model Choice Need? Certificates and Budgets for Selective Prediction

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본 연구는 분류기들의 선택적 성능을 비교하기 위해 필요한 최소한의 레이블 개수(인증서 크기)를 정량화했습니다. 일반화된 위험-커버리지 곡선 아래 면적(AUGRC)과 같은 특정 지표를 고려할 때, 정확한 모델 비교를 위해서는 전체 레이블 중 상당한 비율의 데이터가 필요함을 보여줍니다.

model-selection
label-efficiency
augrc
classification
machine-learning

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Focus Shifts to Structured and Verifiable AI Development

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Focus Shifts to Structured and Verifiable AI Development

How Many Labels Does Model Choice Need? Certificates and Budgets for Selective Prediction

Revisiting Distributed Sign-Based Variance Reduction

Learning to Program Adaptive Non-Local Observables for Machine Learning

A Probe Shift Is Not a Fairness Fix: The Limits of Representation Steering in Speech Models

Variational Quantum Transformer Architecture for Synthetic Language Generation

TRIPROBE: Probing Task Separability Beyond Classification for XAI

Weakening Neurons: An Input-Output Functionality in Transformers with Outsize Influence

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