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Add nexus/adaptive_calibrator.py with the AdaptiveCalibrator class that provides online learning (EMA) for LLM inference cost prediction. Key features: - Per-model ModelCalibration state tracking ms/token and base overhead - EMA updates from observed (prompt_tokens, completion_tokens, actual_ms) - Confidence metric grows with sample count (1 - exp(-n/10)) - Seeded priors distinguish local Ollama models from Groq cloud models - Atomic JSON persistence to ~/.nexus/calibrator_state.json - reset() per-model or global; autosave on every record() - 23 unit tests covering convergence, persistence, edge cases Exported from nexus/__init__.py as AdaptiveCalibrator and CostPrediction. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>