335 citations · 373 across the 50 of their papers we have counts for
8 papers · 1 filter
GRASP: GRanularity-Aware Search Policy for Agentic RAG
Varun Gandhi, Jaewook Lee, Shantanu Todmal +4
Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers.…
Gumbel Machine: Counterfactual Student Writing Generation via Gumbel Noise Steering
Hunter McNichols, Alexander Scarlatos, Mihai Dascalu +2
An effective method of teaching across disciplines is to provide examples of high-quality work. However, an example may be significantly different from a student's current work, ma…
Mathematics Teachers Interactions with a Multi-Agent System for Personalized Problem Generation
Candace Walkington, Theodora Beauchamp, Fareya Ikram +4
Large language models can increasingly adapt educational tasks to learners characteristics. In the present study, we examine a multi-agent teacher-in-the-loop system for personaliz…
RADAR: Reasoning-Ability and Difficulty-Aware Routing for Reasoning LLMs
Nigel Fernandez, Branislav Kveton, Ryan A. Rossi +2
Reasoning language models have demonstrated remarkable performance on many challenging tasks in math, science, and coding. Choosing the right reasoning model for practical deployme…
Reasoning and Sampling-Augmented MCQ Difficulty Prediction via LLMs
Wanyong Feng, Peter Tran, Stephen Sireci +1
The difficulty of multiple-choice questions (MCQs) is a crucial factor for educational assessments. Predicting MCQ difficulty is challenging since it requires understanding both th…
From Text to Visuals: Using LLMs to Generate Math Diagrams with Vector Graphics
Jaewook Lee, Jeongah Lee, Wanyong Feng +1
Advances in large language models (LLMs) offer new possibilities for enhancing math education by automating support for both teachers and students. While prior work has focused on…