A Dataset for Modeling Iterative Problem-Solving

arXiv:2609.00940 · cs.CL · Submitted 2026-09-01 · Read on arXiv

cs.CL

Submitted: 2026-09-01

Updated: 2026-10-07

Code: https://github.com/aims-foundations/dynamic-irt

Terminology

• AUC — 39× in this paper · explained in 2 episodes
• Accuracy — 24× in this paper · explained in 1 episode
• Adam — 13× in this paper · explained in 2 episodes
• Calibration — 7× in this paper · explained in 7 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Breadth-First Search — 6× in this paper
• Failure Modes — 6× in this paper · explained in 3 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• RAG — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Deep Knowledge Tracing — 3× in this paper
• Item Response Theory — 3× in this paper
• Archetype — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• Recurrent State Space Model — 2× in this paper
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Knowledge Tracing Models — 1× in this paper · explained in 2 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• ParaStudent: Closing the Sim2Real Gap in User Simulators for AI Tutor Evaluation
• Learning to Make MISTAKEs: Modeling Incorrect Student Thinking And Key Errors
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

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