arXiv AI June 19, 2026 By Gina Wong, Drew Prinster, Suchi Saria, Rama Chellappa, Anqi Liu

Toward Calibrated Mixture-of-Experts Under Distribution Shift

arXiv:2606. 20544v1 Announce Type: new Abstract: Calibration aligns a model's predictive uncertainty with the frequencies of its empirical outcomes and is important for understanding and trusting reported probabilities.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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arXiv AI

Jun 30

When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning

arXiv:2601. 07965v2 Announce Type: replace Abstract: When a model knows when it does not know, many possibilities emerge.

By Chenjie Hao, Weyl Lu, Yuko Ishiwaka, Zengyi Li, Weier Wan, Yubei Chen

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arXiv Machine Learning

Jul 15

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs

arXiv:2602. 19938v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts (SMoE) architectures are increasingly used to scale large language models efficiently, delivering strong accuracy under fixed compute budgets.

By Zijie Liu, Jie Peng, Jinhao Duan, Zirui Liu, Kaixiong Zhou, Mingfu Liang, Luke Simon, Xi Liu, Zhaozhuo Xu, Tianlong Chen

llms efficiency benchmarks

arXiv AI

Jun 2

ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts

arXiv:2606. 01509v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models scale by activating only a small subset of experts per token.

By Heng Zhao, Zilei Shao, Guy Van den Broeck, Zhe Zeng

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arXiv Machine Learning

Aug 6

Elbow-Based MoE Routing: A Training-Free Inference Time Plugin for Expert Selection

arXiv:2608. 04401v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models enable model scaling while maintaining low inference-time compute by activating only a subset of experts per token.

By Robin Pan, Raymond Liu, Daniel Fang, Adelina Andrei, Rosa Wu

benchmarks

arXiv Machine Learning

Jul 7

Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification

arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.

By Nicolas Sournac, Ahmed Baha Ben Jmaa, Bertrand Braeckeveldt

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arXiv Machine Learning

Aug 12

Invertible Logits Transformation for Accuracy-Preserving Post-Hoc Uncertainty Calibration

arXiv:2608. 10372v1 Announce Type: new Abstract: Post-hoc calibration aligns a classifier's predicted confidences with its empirical accuracy without retraining.

By Lening Zhao, Qipeng Zhan, Li Shen

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When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs

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