v0.3.1

Bug fixes

• llm.provider was silently dropped (#472): LLMConfig propagates shared settings to each per-model LLMModelConfig via a shared_config dict, but that dict omitted provider. Every model kept provider=None, so LLMEnsemble routed them all to the OpenAI backend regardless of the config — a config with provider: claude_code crashed with a missing-OPENAI_API_KEY error, making examples/claude_code_quickstart unusable. An explicit per-model provider still takes precedence.

Features

• run_evolution() accepts target_score and checkpoint_path (#457): both already existed on controller.run() but were not exposed, so library users could not resume from a checkpoint or stop at a target score.

New example

• examples/background_blur: evolving a hot function behind a hard quality gate — the score is a speedup, but fidelity is pass/fail, so a fast-but-wrong candidate scores zero. Deterministic and numpy-only (no webcam, GPU, model or dataset). Demonstrates cascade evaluation (cheap smoke → quality gate → benchmark, so timing is only spent on candidates that are already correct), artifacts that tell the model why it was rejected, and a (complexity, ssim) MAP-Elites grid.

  It documents two traps worth knowing about:

  • The obvious quality gate is exploitable. Grading mean and worst-frame SSIM lets a "blur frame 0's background and reuse it forever" candidate score 47x while leaving a visible person-shaped ghost — it damages one region and the frame average hides it. Grading the worst 16×16 region catches it. The cheats are encoded as tests so they must lose.
  • Timing-as-fitness needs care. Caching a single baseline measurement lets a transiently loaded machine inflate every later speedup.

Full changelog: v0.3.0...v0.3.1