SCOPE-AD: Sequential cost-aware ordinal-belief planning with energy-based models for diagnostic agents

arXiv:2610.01278 · cs.AI, cs.CL · Submitted 2026-10-01 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-10-01

Updated: 2026-10-01

Comments: 5 pages,2 figures

Code: https://github.com/chijiaozima/SCOPE-AD

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 11× in this paper · explained in 2 episodes
• Energy-Based Model — 7× in this paper
• Distillation — 6× in this paper · explained in 2 episodes
• ACTMED — 5× in this paper · explained in 1 episode
• Ablation — 4× in this paper · explained in 1 episode
• Calibration — 3× in this paper · explained in 7 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Action Masking — 1× in this paper · explained in 1 episode
• Active Test Selection — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes

Sources

• Sequential Diagnosis with Language Models
• Timely Clinical Diagnosis through Active Test Selection
• Deep Reinforcement Learning for Cost-Effective Medical Diagnosis

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