An LLM-based framework recommends drill-down paths in visual analytics by approximating a greedy algorithm, interpreting user intent, and managing exploration branches to reduce cognitive load.
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LLM reaches >=0.95 accuracy on 60 number theory problems with optimal hints; LightGBM classifier empirically supports Dirichlet conductor conjecture via zero features at 93.9% test accuracy for small q.
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Intelligent Drill-Down: Large Language Model-Driven Drill-Down Technique for Human-AI Collaborative Visual Exploration
An LLM-based framework recommends drill-down paths in visual analytics by approximating a greedy algorithm, interpreting user intent, and managing exploration branches to reduce cognitive load.
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Artificial Intelligence in Number Theory: LLMs for Algorithm Generation and Ensemble Methods for Conjecture Verification
LLM reaches >=0.95 accuracy on 60 number theory problems with optimal hints; LightGBM classifier empirically supports Dirichlet conductor conjecture via zero features at 93.9% test accuracy for small q.