# BTL-3 Compact native release validation

Date: 2026-07-19

## Candidate

- Public model: **BTL-3 Compact**.
- Model lineage: Qwen3.6-27B plus BTL RL0013 and the frozen behavior repair.
- Scope: text-only coding, tool use and agent behavior.
- Representation: full 64-layer AVQ2/UniSVQ decoder, two measured INT4
  demotions, selected BF16 islands, packed vocabulary matrices, rank-32 head
  correction and a small behavior adapter.
- Source payload bytes before GGUF packing: 8,572,070,080.
- Source weight bytes: 8,551,772,952.
- Portable GGUF bytes: 8,392,369,600.
- Portable GGUF SHA-256:
  `2ddf9527620a17a2a6739d184a7096c45712092e6589128792ec6254e94dc30c`.

The package is complete enough to instantiate and generate without downloading
or loading the BF16 Qwen checkpoint.

## Native runtime proof

The standalone loader meta-initializes the Qwen3.6 text architecture and
installs only the package's small state, packed decoder tensors, packed
embedding, packed head, rank-32 output correction and behavior LoRA.

The H100 smoke test observed:

- no surviving dense compatible decoder matrices;
- exact AVQ2 CUDA-kernel parity with the unpacked reference;
- INT4 maximum absolute kernel error of `3.0517578125e-05`;
- standalone model peak CUDA allocation of 8,552,500,736 bytes;
- successful autoregressive generation.

The source payload was subsequently exported into the portable GGUF without
reconstructing dense weights. The exporter byte-verified all 2,416 payloads
and reported no unsupported tensors or native runtime gaps. The exact GGUF
then passed native llama.cpp generation on Apple Metal and NVIDIA CUDA.
MLX, WebGPU, phone execution, and stock-engine compatibility remain unverified.

## Fresh sealed gate

Benchmark ID: `btl-fresh-tool-gate-2026-07-19-v1`

Cases SHA-256:
`d656a7862e16e64ed3a359ba1de10f7eafefad77f6cf5a8264d60287e1890a45`

The gate was authored after compression and behavior-repair choices were
frozen. It contains 100 scored turns:

- 20 single calls;
- 20 parallel calls;
- 20 sequential calls;
- 20 parallel-multiple calls;
- 20 abstention decisions.

All tool families are first-party and use a new `lumenharbor_*` namespace.
Mechanical QA found no schema errors, duplicate IDs, parse errors or exact tool
name overlap with repository training/evaluation data.

This is a private synthetic contract-retention gate. It is not a public coding
benchmark and must not be presented as a frontier benchmark score.

## Full-precision teacher

The frozen RL0013 teacher scored:

| Category | Correct | Total |
|---|---:|---:|
| Single | 20 | 20 |
| Parallel | 20 | 20 |
| Sequential | 20 | 20 |
| Parallel-multiple | 10 | 20 |
| Abstention | 20 | 20 |
| Overall | 90 | 100 |

The release metric is conditional retention on these 90 teacher-correct turns,
reported separately for every category and overall. Absolute student accuracy
is also retained in the raw result.

## Standalone result

The standalone package scored:

| Category | Student correct | Total | Teacher-correct retained | Retention |
|---|---:|---:|---:|---:|
| Single | 20 | 20 | 20 / 20 | 100% |
| Parallel | 20 | 20 | 20 / 20 | 100% |
| Sequential | 20 | 20 | 20 / 20 | 100% |
| Parallel-multiple | 3 | 20 | 3 / 10 | 30% |
| Abstention | 20 | 20 | 20 / 20 | 100% |
| **Overall** | **83** | **100** | **83 / 90** | **92.2%** |

All generations stopped. The measured malformed rate was 7%, entirely within
the difficult parallel-multiple family.

The seven teacher-correct/student-wrong parallel-multiple cases were not parser
false negatives. The package emitted a fluent abstention instead of making the
three requested independent calls. This is a real over-abstention failure under
novel multi-call schemas.

### Decision

The package passes a 90% **overall** conditional-retention rule. It fails a 90%
**per-category** rule because parallel-multiple retention is 30%.

Do not alter compression based on this sealed result. Any behavior repair aimed
at these cases creates a new candidate and requires a newly authored, untouched
release gate.

## Release boundary

This result validates that the exact text-only source payload is physically
standalone and retains more than 90% overall on the fresh CUDA gate. The GGUF
export preserved those payload bytes exactly. It does not support a claim of
uniformly preserved behavior or phone deployment.

The release includes a distributable macOS native runtime and exact-artifact
throughput measurements on Apple M2 and RTX PRO 6000. Other GPU packages,
stock Ollama/LM Studio execution, mobile runtimes, and a public compact-specific
coding benchmark remain separate gates.