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README.md

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+---
+license: apache-2.0
+language:
+  - en
+tags:
+  - rag
+  - code-retrieval
+  - verified-ai
+  - constitutional-halt
+  - bft-consensus
+  - aevion
+size_categories:
+  - 10K<n<100K
+task_categories:
+  - question-answering
+  - document-retrieval
+---
+
+# Aevion Codebase RAG Benchmark
+
+**Verified structured-retrieval benchmark** extracted from a real Python codebase (968 source files, 21,149 chunks) with cryptographically signed partition proofs.
+
+## What's in this dataset
+
+| File | Description |
+|------|-------------|
+| `codebase_corpus.jsonl` | 21,149 Python code chunks with 6-field structural metadata |
+| `codebase_queries.jsonl` | 300 enterprise query-decomposition pairs |
+| `partition_proofs.jsonl` | XGML-signed proof bundles per query |
+| `benchmark_results.csv` | Precision/recall/F1 per retrieval method (60 eval queries) |
+| `benchmark_summary.json` | Aggregate metrics and auto-tuning parameters |
+| `tuning_summary.json` | Grid-search results across 10K synthetic docs |
+
+## Corpus Schema
+
+Each chunk in `codebase_corpus.jsonl` has:
+
+```json
+{
+  "doc_id": "chunk_000000",
+  "text": "module.ClassName (path/to/file.py:20)",
+  "layer": "core",
+  "module": "verification",
+  "function_type": "class",
+  "keyword": "hash",
+  "complexity": "simple",
+  "has_docstring": "yes",
+  "source_path": "core/python/...",
+  "source_line": 20
+}
+```
+
+## Benchmark Results (60 eval queries)
+
+| Method | Precision | Recall | F1 | Exact Match |
+|--------|-----------|--------|----|-------------|
+| naive | 0.516 | 0.657 | 0.425 | 11.7% |
+| instructed | 1.000 | 0.385 | 0.463 | 23.3% |
+| verified_structural | 1.000 | 0.385 | 0.463 | 23.3% |
+| verified_consensus | 1.000 | 0.437 | 0.503 | 31.7% |
+| **verified_structural_ensemble** | **1.000** | **0.459** | **0.527** | **33.3%** |
+
+Key finding: Structural + ensemble retrieval achieves **100% precision** (zero irrelevant chunks) vs. 51.6% for naive keyword search.
+
+## Method
+
+1. **AST extraction**: Python files parsed with `ast` module → class/function/method chunks
+2. **6-field structural metadata**: layer, module, function_type, keyword, complexity, has_docstring
+3. **Constitutional Halt labeling**: VarianceHaltMonitor (σ > 2.5x threshold) as automatic quality gate
+4. **XGML proof bundles**: Ed25519-signed proof chain on every partition plan
+
+## Related
+
+- [Aevion Verifiable AI](https://github.com/aevionai/aevion-verifiable-ai) — source codebase
+- Patent US 63/896,282 — Variance Halt + Constitutional AI halts
+
+## License
+
+Apache 2.0 — freely use for research and commercial applications.