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A novel decision-aware machine learning framework was deployed nationwide in Sierra Leone to allocate essential medicines, achieving a 19% increase in consumption and covering 2 million women and children under five.
Introduces CICL, a decision-aware context layer that selects and compresses evidence for tool-using LLM agents by treating context as a decision-time intervention, using counterfactual-inspired scoring and typed memory cards under a token budget. Experiments on SWE-bench and RepoBench show concrete gains in retrieval accuracy and action criticality.