ABI

ABI is a research codebase for acquiring, labeling, packaging, validating, and eventually minimizing capabilities extracted from open-weight teacher models. It is designed to produce immutable capability artifacts that a separate LayerCake runtime can install, compose, route, and execute.

The repositories have deliberately separate responsibilities:

• ABI: teacher qualification, probing, labeling, segregation, provenance, information accounting, artifact construction, and verification.
• LayerCake: hosting, installation, transfer between compatible hosts, routing, orchestration, and inference performance.

Current release status

The latest technical validation is R7:

https://github.com/Yoder23/abi/releases/tag/abi-final-validation-v2-repaired-r7-2026-08-30

R7 passes a bounded capability-runtime/conformance proof across LayerCake v25, Qwen2.5-0.5B, and Pythia-160M using four immutable packages: English, Python, civics, and chemistry.

This is not a claim that ABI already extracts a fluent minimal English core from any teacher. Human ratings, different-hardware reproduction, registered minimum-information certification, teacher-extraction quality, and comparison with LoRA/distillation remain open.

The additive R8 native-neural-transfer campaign is a Level 0 negative result. Its strongest package-only recipient interface produced AFTER−BASE +0.001953 across 1,024 paired Pythia rows (95% bootstrap CI -0.019531 to +0.024414). Canonical extraction was exact, but recipient behavior did not transfer. The held-out secret was never revealed, and R8 does not change the R7 release. The final public verifier rejected 7/7 hostile mutations and ignored one forged scientific boolean without changing the NO verdict (8/8 expected outcomes).

R9 then tested the backend/compiler hypothesis directly with an intentionally capability-specific Pythia neural backend. V1 failed to fit its own training rows. The preregistered v2 repair exposed both embedding and final recipient states and trained a 1,039,880-parameter backend for 10,000 steps. It still recomputed at only 12.85% training accuracy and 12.5% unseen-depth AFTER accuracy, versus 7.32% BASE; ZERO also reached 12.5%, failing causality. All 8,192 evaluation and 288 training observations replayed live, and 5/5 hostile controls were rejected. R9 therefore closes this recipient-state GRU branch without opening the universal-backend experiment or changing R7.

R10 then connected the synthetic R8 extraction to a clean package slot and three frozen recipients. Its runtime-owned copy/paste component passed: four 2,040-byte AFTER packages produced 1.0 accuracy after paste and restore across 61,440 Pythia, Qwen2, and T5 rows; removal exactly matched BASE, all negative controls were at most 0.15625, recipient parameters were unchanged, and a fresh live run reproduced every row byte-for-byte. The overall R10 contract is still negative because the source model's native decoder reached only 0.59375 to 0.65234375 against the registered 0.99 source gate. See docs/R10_COPY_PASTE_RESULT.md. This proves a bounded canonical-runtime component, not LayerCake integration, lossless source behavior, English/domain extraction, or native neural transplantation.

R7 at a glance

Evidence	Result
Public archive	844,018,841 bytes
Archive SHA-256	fc50f423986149b5d4670ec9e28698540f64be96034efa26e5704c4469921e88
Archive members	1,193, all exact
Physical certification environments	3/3
Reachable inventory rows	301,543
Locked matrix	5,043/5,043 rows
Live causality	3,072 rows, 24 distinct processes
Live isolation	2,100 rows, zero target successes
Fail-closed controls	19/19 pre-public and 19/19 blind replay
Public reconstruction tests	17/17
Blind tar-prefix controls	12/12
Blind verdict	PASS, bounded scope

The blind reconstruction succeeded with a 368-character extracted path while Windows long paths were disabled.

Install

ABI targets Python 3.10. For the lightweight package and CLI:

python -m pip install -e .
abi status
abi self-check

Teacher extraction and research workflows require the dependencies in requirements.txt. Human-rating helpers use the optional human extra:

python -m pip install -e ".[human]"

Public R7 verification

Download public_release_assets_r7.json from the R7 GitHub Release. It binds the definitive archive and four capability packages by byte length, SHA-256, content address, release tag, and commit.

From a clean checkout of the public tag:

python -m abi_v2.public_reconstruction \
  --manifest /path/to/public_release_assets_r7.json \
  --tag-clone /path/to/clean/tag-clone \
  --workspace /path/to/new/workspace \
  --output /path/to/public-reconstruction-receipt.json

The external different-hardware workflow is under external_reproduction/. Its command sequence is:

abi-reproduce verify
abi-reproduce certify-hosts
abi-reproduce capability-matrix
abi-reproduce causality
abi-reproduce isolation
abi-reproduce performance
abi-reproduce hostile-audit
abi-reproduce report

Executing this sequence on the development laptop is a rehearsal, not an independent reproduction.

Human review

The frozen packet contains 7,000 judgments for each of three independent raters. The gate remains 0/21,000 until real raters complete and attest their forms:

abi human-rate --rater R1
abi human-rate --rater R2
abi human-rate --rater R3

See docs/PHASE2_HUMAN_RATING_HANDOFF_V1.md for the exact handoff.

Published artifacts

Capability	Bytes	SHA-256
English	253,216,208	acb787b3ffa0153c57d88cd37ba81c3f00b370d4ca4937e659cd4c775851f25d
Python	448,404	f1defaef2771ced336a332572a2d2f0e1e542399c877d182c48a6cd2e199231d
Civics	495,919	634ce66958859ec36dc1fbdf5ef34d6d2a9949d10cf2348a68c245d8c325d604
Chemistry	510,981	f9c9b2668fda5ef6b92844c1b7097fbdf8ff0daaae51f5b86f72d4a49000abeb

Claim boundary

R7 supports only the bounded capability-runtime/conformance result described above. It does not establish:

• arbitrary or universal model compatibility;
• tensor transplantation between unrelated architectures;
• complete diagnosis or extraction of teacher knowledge;
• fluent teacher-quality English generation;
• superiority over LoRA, distillation, or fine-tuning;
• human-rated quality;
• independent hardware reproducibility;
• global information minimality; or
• completion of the full ABI moonshot.

See docs/ABI_TECHNICAL_CLAIMS.md, docs/ABI_FINAL_RESULTS.md, and the review_packet/ directory before citing results.

Repository map

• abi/ — acquisition, labeling, artifact, and public CLI code.
• abi_v2/ — canonical runtime/conformance, certification, strict verification, public reconstruction, and external reproduction tooling.
• tests/ — supported automated checks.
• results/abi_final_validation_v2/ — immutable R3-R7 validation lineage.
• results/abi_moonshot/packages/ — published specialist packages.
• external_reproduction/ — independent-operator workflow and environment lock.
• review_packet/ — ordered technical and external-review handoff.
• docs/ — architecture, claims, results, and review instructions.

Historical experimental evidence remains in Git for auditability. Generated caches, model weights, temporary public reconstructions, and bulk reproducible intermediates are intentionally excluded from the production tree.

License and research status

This repository is a research release. Consult LICENSE and NOTICE before redistribution. Scientific claims are governed by immutable evidence and the claim ceiling above, not by roadmap language.