BehaviorBench reconstructs 2,000 real wallets into 141k belief and 1.4M trade prediction tasks to test if personalization from history improves model performance over non-personalized baselines.
arXiv preprint arXiv:2502.14642 (2025)
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LEADS is an LLM-agent framework that discovers hybrid models for cardiac EP digital twins by treating domain knowledge as an action space, outperforming human-designed and other LLM-based hybrids on synthetic and real data.
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BehaviorBench: Modeling Real-World User Decisions from Behavioral Traces
BehaviorBench reconstructs 2,000 real wallets into 141k belief and 1.4M trade prediction tasks to test if personalization from history improves model performance over non-personalized baselines.
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Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure
LEADS is an LLM-agent framework that discovers hybrid models for cardiac EP digital twins by treating domain knowledge as an action space, outperforming human-designed and other LLM-based hybrids on synthetic and real data.