arXiv Machine Learning August 5, 2026 By Carson J. Cook, Ahmed J. Zerouali, Anthony Schmidt, Reginald Ziedzor, Paul Lin, Luke G. Eglington

UNVaMP: Neural Knowledge Tracing with Variational Regularization of Latent Knowledge Dynamics

Read the original on arXiv Machine Learning →

arXiv:2608. 03811v1 Announce Type: new Abstract: We introduce the Unified Neural Variational Measurement of Proficiency (UNVaMP) architecture, a knowledge tracing method that integrates observed student-item interactions with internal memory to produce evolving latent representations of student knowledge.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 29

Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction

arXiv:2606. 28186v1 Announce Type: cross Abstract: Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction.

By Chenguang Wang, Ming Li, Xinyue Zeng, Zhuochun Li, Hong Jiao, Tianyi Zhou, Dawei Zhou

llms fine-tuning benchmarks safety

arXiv Machine Learning

2d ago

Activation-Conditioned Self-Distillation

arXiv:2609.38342v1 Announce Type: new Abstract: On-policy self-distillation uses a model as its own teacher to provide dense supervision for reasoning, often through reference-solution conditioning....

By Zhexi Lu, Subhajit Chaudhury, Tejaswini Pedapati, Keerthiram Murugesan, Lei Yu

efficiency benchmarks

arXiv Machine Learning

Aug 13

Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates

arXiv:2608. 11435v1 Announce Type: new Abstract: Forward and inverse modeling of parametric dynamical systems requires surrogate models that are not only accurate for state prediction, but also informative for parameter calibration.

By Qiyao Zhou, Xujia Zhu, Pierre Joli, Yu Cong, Sibo Cheng

benchmarks

arXiv AI

Aug 19

KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn

KnowSim introduces an evaluation framework that uses a user simulator with explicit knowledge states to assess how well large language models calibrate information to users. The simulator represents knowledge as a graph of Information Units with prerequisite relationships and updates these states based on learning theory. KnowSim computes Knowledge Gain, Delivery Calibration, and Cognitive Overload metrics, and its rankings align with human judgments, outperforming baseline simulators and revealing model performance differences across user knowledge levels.

By Yoonjoo Lee, Hyoungwook Jin, Tae Soo Kim, Shaoyang Zhang, Philippe Laban, Q. Vera Liao

llms

arXiv AI

Jul 16

Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing

arXiv:2607. 13103v1 Announce Type: cross Abstract: Knowledge tracing (KT) aims to predict students' future performance by modeling their evolving knowledge states from historical interactions.

By Duantengchuan Li, Yingqian Bi, Jinsong Chen, Rui Zhang, Mingwen Tong

llms benchmarks safety

arXiv AI

Aug 7

Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors

arXiv:2608. 06300v1 Announce Type: new Abstract: Automatic speaking assessment systems are increasingly deployed in high-stakes settings to mark second language (L2) learners' speaking tests, making it critical to show that their scores depend on speaking proficiency rather than irrelevant speaker attributes such as first language (L1) or age.

By Arya Labroo, Mengjie Qian, Kate Knill

llms rag diffusion multimodal safety

One email a morning, machine-written

Related stories

Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction

Activation-Conditioned Self-Distillation

Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates

KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn

Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing

Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors