Context0: Zero-Label Self-Evolution for Context-Aware Clinical Dialogue

Clinical dialogue is inherently context-dependent: an appropriate response must account for patient-specific constraints and healthcare settings, recognize when consequential information is missing, and avoid unnecessary clarification when the available context is sufficient. Developing these behaviors requires training experiences that capture not only medical knowledge, but also when and how that knowledge should be applied. We introduce **Context0**, a zero-label self-evolution framework that develops context-aware clinical dialogue capabilities without a pre-existing clinical dialogue dataset or example-level human annotations. Starting from zero curated training examples, a Teacher constructs context-sensitive dialogue tasks, while a Doctor learns from case-specific rubric feedback. A medical knowledge graph provides knowledge anchors for task construction, and feedback from the evolving Doctor guides the Teacher toward new learning opportunities. Alternating Teacher and Doctor updates jointly evolve the training curriculum and the response policy. On the Global Health subset of HealthBench, Context0 achieves a context-awareness score of 62.3%, exceeding GPT-5 by 3.2 percentage points in our evaluation. It also achieves full-rubric scores of 57.9% and 62.7% on the unclear- and clear-context subsets, respectively. These results highlight the potential of zero-label self-evolution to develop context-aware medical assistants through self-generated curricula and feedback. We will release the code and curriculum at https://anonymous.4open.science/r/Context0-2878.