Feature/demand assessor#49
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- DemandAssessor: neural IRT model predicting P(response=1 | subject, item_features) via MLP over concatenated subject embeddings and item feature vectors - Full demand annotation pipeline (DemandAnnotator, GeminiClient, RubricsCatalog, AnnotationCache) implementing the 18-dimension ADeLe rubric scoring system - Unit tests for DemandAssessor (24 tests, synthetic data, no pretrained model needed) - Unit and live end-to-end tests for annotation pipeline - Fix deferred import in LLMJudge to avoid crash when transformers not installed Closes aims-foundations#41
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…y client agnostic (use environment vars to set client and key
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For Issue #41