| --- |
| tags: |
| - autoencoder |
| - formal-logic |
| - experimental |
| - ipfs-datasets-py |
| --- |
| # security-ir: experimental 384D checkpoint |
|
|
| **Held-out exact IR reconstruction: 0/2. Native structural validity: 2/2.** These two authored paraphrase examples share semantic targets with training; they are not independent real-world benchmark tasks. The model generates plausible but semantically incorrect candidates and must not authorize actions. |
|
|
| This is an opt-in development artifact. It is **not the default symbolic compiler**, |
| not a verified formalizer, and not a security repair policy. All qualification, |
| admission and proof-authority flags remain false. No LLM/provider fallback runs. |
|
|
| ## Runtime |
|
|
| Input embeddings use `thenlper/gte-small` at commit |
| `17e1f347d17fe144873b1201da91788898c639cd` (384 dimensions). The embedding encoder |
| weights are not included. Source inference requires the corresponding verified |
| local encoder assets and rejects inputs above 512 tokens without truncation. |
| The installed `ipfs_datasets_py` implementation must match the source hashes in |
| the checkpoint; incompatible sources are rejected. This development runtime is |
| currently implemented in the matching workspace checkout, not claimed to be in |
| an existing PyPI release. No repository-supplied Python is executed on download. |
|
|
| ```python |
| from ipfs_datasets_py.logic.security_ir import open_autoencoder |
| model = open_autoencoder() # library catalog pins a complete Hub commit + SHA256 |
| report = model.infer([{"id": "sample", "source_text": source, "embedding": vector384}]) |
| # Or: model.infer_texts([source], snapshot_path=verified_gte_snapshot) |
| ``` |
|
|
| ## Training and scope |
|
|
| Security, Intent and UI/UX heads inherit all compatible nonlexical tensors from |
| a trained local Legal 384D head. New lexical rows use trained parent row means; |
| no random parameters remain in their initial transferred state. Each native head |
| used 12 authored training rows, 2 tuning rows, 500 epochs / 1000 optimizer updates; |
| selection used tuning loss only. Outputs are explicitly local typed fragments, |
| not complete verified programs or native documents. Security operator fragments |
| still require their enclosing program and source-reference context. Legal retains |
| its separate sparse core and learned formula head, with parser-derived features. |
|
|
| These checkpoints were **not trained on the complete CVEFixes or SkillCenter |
| corpora**. The separate full-feature structural-family experiment (15/9/8/6 routes) |
| does not measure this 384D source decoder and is not its training loss or coverage. |
| Weight/conditioning ablations demonstrate numerical dependence, not correct meaning. |
|
|
| ## Provenance and license |
|
|
| The manifest records the actual parent hashes, training manifests, embedding |
| assets and measured validation. Source screening found no private documents, |
| credentials, benchmark solutions or third-party corpus text in the new authored |
| fixture export. Original Legal files and the upstream Hugging Face datasets were |
| not overwritten. This release makes no new license grant for inherited weights. |
| The runtime source repository retains its AGPL-3.0 license; the new authored |
| fixture export separately declares Apache-2.0. A corpus license is not asserted |
| to be the weight license. |
|
|
| The checkpoint and evidence are immutable under `releases/20261002-384-development-v1/`. |
| Manifest SHA256: `d9fc241c7c6a039068681fab18b6a36fbcbd1752c2683ba1dc16423ce94aba2a`. |
|
|
|
|
| ## Additional verified development releases (2026-10-01) |
|
|
| The following opt-in structured profiles use a fixed typed JSON schema and learned scalar vocabulary. Their cards distinguish raw reconstruction, hybrid AST normalization, and compilation. They do not replace the symbolic formalizers or confer proof authority. |
|
|
| - [structured_native_v3](https://huggingface.co/Publicus/security-ir-autoencoder/tree/e451ce831c4fbc9079b7516e5d415dbab9e219ae/releases/20261001-structured-native-v3-experimental) |
| - [security_styles_v4_diagnostic](https://huggingface.co/Publicus/security-ir-autoencoder/tree/e451ce831c4fbc9079b7516e5d415dbab9e219ae/releases/20261001-security-styles-v4-diagnostic) |
|
|
| [Runtime code and exact release descriptors](https://github.com/endomorphosis/ipfs_datasets_py/blob/f4e28be225455add0f29259fa6ecf238d23c6316/docs/autoencoders/experimental_structured_checkpoints.md). All new package hashes and cold/offline numerical replay were verified. |
|
|
