SweetGold v1.2.0

SweetGold v1.2.0

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SweetGold v1.2.0 completes the current feature-development cycle with an
auditable strategy-decision workflow, hardware-portable ML execution, equal
English and Chinese project documentation, and a public launch site.

Highlights

• M15 runs a matched-seed Arena league, applies a declared balanced, yield,
  or safety objective, enforces survival and invalid-action constraints, and
  writes linked JSON and Markdown decision evidence.
• M16 adds explicit auto, cpu, mps, and cuda selection to ML commands,
  refuses silent accelerator fallback, and records hardware in run manifests.
• Accelerator-synchronized BC timing and throughput make hardware decisions
  evidence-based; M6 BC+PPO and M7 CTDE smoke workflows pass on Apple MPS.
• English and Chinese READMEs, handoff, product design, changelog, release notes,
  model cards, and operational documents now have equal entry status.
• The bilingual project site is continuously deployed to
  https://alanthssss.github.io/sweetgold/ through GitHub Pages.

M15 auditable decisions

python3 main.py arena-agent \
  --strategies assignment greedy scout \
  --objective balanced \
  --min-bee-survival 0.9 \
  --max-invalid-action-rate 0.01 \
  --episodes 10 --seed 42

The decision is deterministic for the same artifact and policy. An explicit
“no eligible strategy” result is preserved when every candidate violates a
constraint. Small leagues demonstrate the workflow and do not replace the M14
cross-distribution robustness audit.

M16 hardware portability

.venv-ml/bin/python main.py hardware --device auto
.venv-ml/bin/python main.py pipeline --config experiments/smoke.json --device mps

On the initial fixed 20-episode, three-epoch BC smoke workload, the Apple M1 Pro
CPU trained in 0.5774 seconds at 138,355 examples/s; MPS trained in 3.6764
seconds at 21,730 examples/s. This small workload did not amortize accelerator
dispatch, so AWS CUDA is deferred until profiling shows a suitable workload.

Models and compatibility

v1.2.0 promotes no new policy and publishes no new checkpoint. M14
hierarchical-return-ctde remains the latest promoted policy and continues to
use the verified models-v2
asset catalog. Existing model IDs, registry records, Arena artifacts, and CLI
commands remain compatible.

Verification

python3 main.py --version
python3 -m unittest discover -s tests -v
python3 main.py models download
python3 main.py models verify

The source release uses Apache-2.0. Separately distributed model assets retain
their declared Apache-2.0 terms and model-card limitations.

Project status

After v1.2.0, new feature development is paused. Maintenance covers critical
defects, security, compatibility, reproducibility, documentation, and release
operations. A future CUDA run or research milestone requires a declared need,
budget, acceptance gates, and untouched evaluation seeds where applicable.