ABI is an alpha research compiler for acquiring, labeling, segregating, packaging, and verifying bounded capabilities from open-weight teacher models. It produces immutable, provenance-bound artifacts for a separate host such as LayerCake.
ABI and LayerCake have deliberately separate jobs:
- ABI qualifies teachers, probes bounded capabilities, labels and segregates records, accounts for imported information, constructs artifacts, and verifies the evidence.
- LayerCake installs, composes, routes, and executes compatible capability artifacts. Its runtime and performance claims belong to that repository.
Whole-moonshot verdict: FULL_MOONSHOT_NOT_PROVEN.
ABI has strong bounded results, but it has not yet proved automatic extraction of a minimal fluent-English substrate from an arbitrary pretrained model. It also has not completed human evaluation, independent different-hardware reproduction, global minimality, or a universal superiority comparison against LoRA and distillation.
| Evidence line | What is established | What remains open |
|---|---|---|
| R7 public release | Bounded capability runtime/conformance across three named environments; public reconstruction passed | Human ratings and independent hardware |
| V1089 local campaign | Bounded machine evidence through Phase 7 and a same-machine Phase 8 rehearsal | One coherent public final artifact, Phase 5 clean replay, external review |
| R97 | Prospective two-hop reasoning transfer: 1,399/1,400 with causal controls and byte-identical replay | Broad English, arbitrary capabilities, independent reproduction |
| R21-R23 | Bounded label-separated supplied-content generation and prospective semantic replication | General English and autonomous discovery |
These lineages must not be combined into one certification. The controlling
public technical release remains
abi-final-validation-v2-repaired-r7-2026-08-30.
The current default branch is a later peer-review candidate.
Read the project status and claim ledger before citing a result.
ABI requires Python 3.10 or newer. Git LFS is required for a complete research checkout.
git clone https://github.com/Yoder23/abi.git
cd abi
git lfs install
git lfs pull
python -m venv .venv
python -m pip install -e ".[dev]"
python -m abi status --json
python -m abi self-check
python -m examples.segregate_capabilitiesThe example builds and validates a small in-memory English/domain segregation manifest. It does not download a teacher, train a model, or claim scientific quality.
Install optional dependencies only for the workflow you need:
python -m pip install -e ".[host]" # Torch host/runtime helpers
python -m pip install -e ".[extraction]" # Teacher extraction research
python -m pip install -e ".[human]" # Frozen human-rating workflowSee the getting-started guide for platform notes, the supported Python API, and verification commands.
The public alpha API in abi/__init__.py supports:
- immutable source-model manifests;
- bounded capability inventories and user selection plans;
- explicit English-core, specialist-domain, and quarantine records;
- nested teacher-information budgets and cost accounting;
- content-addressed acquisition bundles and verification; and
- deterministic release status and self-check commands.
The canonical flow is:
pinned teacher -> bounded probes -> labeled records -> selection/budget
-> immutable acquisition artifact -> external host conformance
An acquisition artifact is not itself a deployable model and does not silently include a teacher at inference. Host-side installation and execution are separate, explicitly measured steps.
| You want to... | Start here |
|---|---|
| Understand the project | Documentation index |
| Run the supported API | Getting started |
| Understand the code and evidence tree | Repository map |
| Review current claims | Project status -> claims |
| Reproduce R7 | R7 public validation |
| Conduct independent review | Independent review handoff |
| Run human scoring | Human-rating handoff |
| Run external hardware review | Phase 8 reproduction |
| Contribute | Contributing guide |
ABI does not currently establish:
- complete diagnosis of everything a teacher knows;
- fluent, minimal English extraction from arbitrary open-weight models;
- exhaustive or ontology-free domain discovery;
- native tensor transplantation between unrelated architectures;
- zero-loss foreign-teacher transfer;
- human-rated teacher parity;
- independent-hardware reproducibility; or
- general superiority over LoRA, distillation, or fine-tuning.
Within this repository, "lossless" is reserved for exact archive, manifest, tensor, package, or replay identity inside a declared compatibility boundary. It does not describe knowledge extraction from a foreign model.
Run the lightweight supported surface:
python -m pytest -q \
tests/test_public_release.py \
tests/test_capability_pipeline.py \
tests/test_capability_segregation.py
python -m ruff check abi/__init__.py abi/__main__.py \
tests/test_public_release.py examples/segregate_capabilities.py
python scripts/check_docs.pyThe complete default research suite has additional Git LFS and exact sibling LayerCake requirements. Follow the independent review handoff; do not interpret a missing historical payload as a pass.
When documents disagree, use this order:
- an immutable tag plus exact content hashes;
- frozen protocol and campaign contracts;
- raw observations and immutable packages;
- fail-closed verifier output;
- claim and status documents; and
- roadmap or explanatory prose.
Documentation cannot promote a scientific claim. Negative and superseded results remain in the repository for auditability.
ABI is Apache-2.0 licensed alpha research software. Historical experiment modules are not a stable production API and should not be exposed directly to untrusted network input. See SECURITY.md, LICENSE, and CONTRIBUTING.md.