arXiv AI June 15, 2026 By Xiaoran Yan, Cheng Tang, Atsushi Shimada

Recovering Stranded Discrimination in Knowledge Tracing: Per-Item Bias Correction via Empirical-Bayes Shrinkage

arXiv:2606. 14123v1 Announce Type: cross Abstract: Deployed knowledge-tracing models are typically frozen after training, yet systematic per-item logit bias arises, from limited per-item expressivity in backbone architectures and from post-deployment shifts in item properties, degrading prediction quality.

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

safety

arXiv Machine Learning

Aug 4

The Label Defines the Timescale: Trait-State Limits of Temporal-Aggregate Learning

arXiv:2608. 01587v1 Announce Type: cross Abstract: Machine-learning benchmarks often pair a label that aggregates a long temporal horizon with input observed through one or a few short windows.

By Xizhe Zhang

benchmarks

arXiv Machine Learning

Jul 30

BayesAME: Bayesian Active Model Evaluation

arXiv:2607. 27023v1 Announce Type: new Abstract: Evaluating large generative models across benchmarks is time-consuming and computationally expensive.

By Paula Cordero Encinar, Taylan Cemgil, Arnaud Doucet, Virginia Aglietti, Silvia Chiappa

benchmarks

arXiv Machine Learning

Jul 21

BACON: Budgeted Human Calibration for Modeling and Evaluation with Multiple AI Judges

arXiv:2607. 16239v1 Announce Type: new Abstract: AI judges offer a scalable, low-cost alternative to human evaluation, but their outputs can be biased relative to human preferences and highly item-dependent, varying across judges, tasks, and domains.

By Lei Shi, Anlan Zhang, Rita Lyu, Zhengmian Hu, Tong Yu, David Arbour, Avi Feller, Saayan Mitra, Ritwik Sinha

rag safety

arXiv AI

Jul 29

Laplace-PSN-IRT: Uncertainty Quantification for Neural Item Response Theory Models of LLM Benchmarks

arXiv:2607. 25257v1 Announce Type: cross Abstract: Item Response Theory (IRT) has recently been proposed as a framework for evaluating large language model (LLM) benchmarks by separating a model's latent ability from the properties of individual benchmark items.

By Juan Francisco, Mandujano Reyes

llms benchmarks

arXiv Machine Learning

Jun 26

DualEval: Joint Model-Item Calibration for Unified LLM Evaluation

arXiv:2606. 26429v1 Announce Type: new Abstract: Current LLM evaluation relies on two complementary but often disconnected signals: static benchmarks with objective correctness labels and arena-style preference data that better reflect open-ended user interactions.

By Aaron J. Li, Hao Huang, Youngmin Park, Yitong Ma, Wei-Lin Chiang, Li Chen, Cho-Jui Hsieh, Bin Yu, Ion Stoica

llms benchmarks

arXiv Machine Learning

Jun 17

When Dynamics Models Read the Wrong Time Steps: Label-Free Event Credit Re-Anchoring for Robust Global Readouts

arXiv:2606. 17572v1 Announce Type: new Abstract: Learned dynamics models often answer global physical questions, such as fault severity or impact stiffness, by pooling a per-step feature sequence into one readout vector.

By Yifan Wang

Related stories

The Label Defines the Timescale: Trait-State Limits of Temporal-Aggregate Learning

BayesAME: Bayesian Active Model Evaluation

BACON: Budgeted Human Calibration for Modeling and Evaluation with Multiple AI Judges

Laplace-PSN-IRT: Uncertainty Quantification for Neural Item Response Theory Models of LLM Benchmarks

DualEval: Joint Model-Item Calibration for Unified LLM Evaluation

When Dynamics Models Read the Wrong Time Steps: Label-Free Event Credit Re-Anchoring for Robust Global Readouts