Initial implementation of HAG (Hopfield-Augmented Generation)HEADmaster

Core Hopfield retrieval module with energy-based convergence guarantees,
memory bank, FAISS baseline retriever, evaluation metrics, and end-to-end
pipeline. All 45 tests passing on CPU with synthetic data.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

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+# HAG: Hopfield-Augmented Generation
+
+A retrieval-augmented generation system that replaces standard vector similarity search (FAISS top-k) with **iterative Modern Hopfield Network retrieval**.
+
+## Quick Start
+
+```bash
+pip install -e ".[dev]"
+pytest
+```
+
+## Core Idea
+
+Standard RAG retrieves passages once via FAISS similarity. HAG iteratively refines the query in embedding space using Modern Continuous Hopfield Network dynamics:
+
+```
+q_{t+1} = M * softmax(beta * M^T * q_t)
+```
+
+This enables implicit multi-hop reasoning through energy minimization with convergence guarantees.
+
+## Project Structure
+
+- `hag/hopfield.py` — Core Hopfield retrieval module
+- `hag/memory_bank.py` — Passage embedding storage
+- `hag/retriever_hopfield.py` — Hopfield-based retriever
+- `hag/retriever_faiss.py` — FAISS baseline retriever
+- `hag/pipeline.py` — End-to-end RAG/HAG pipeline
+- `hag/metrics.py` — Evaluation metrics (EM, F1, retrieval recall)
+- `hag/energy.py` — Energy analysis utilities
+- `scripts/` — Evaluation and analysis scripts
+- `tests/` — Comprehensive test suite
+
+## Running Tests
+
+```bash
+pytest tests/ -v
+```
+
+All tests run on CPU with synthetic data — no GPU or model downloads required.