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Predictions alone can mislead; understanding how they reshape decision-making is crucial for effective automated systems.
Generalist agents need to remember distinct information to navigate conflicting optimal actions across environments, challenging the notion that current state observations are sufficient.
Healthcare LLM benchmarks can be misleading because they fail to capture critical assumptions about how users interact with models and how those interactions translate to real-world outcomes.
