v1.5.0

Contextual Optimization (LCE-M GP)

• Added contextual multi-task optimization built on BoTorch's LCEMGP (Feng et al., NeurIPS 2020) for both the single-objective (bo.py) and multi-objective (mobo.py) BoTorch backends.
• Context embeddings are definable: learned from data, supplied manually per context (any encoder, e.g. ViT-G/14 vectors), or computed from context images via open_clip (default ViT-bigG-14, the open_clip release of ViT-G/14) with content-hashed caching and optional L2 normalization.
• Warm-start parameter CSVs accept a Context column to transfer observations from other contexts (users, devices, environments); new observations are tagged with the current context.
• Run metrics (coverage, IsBest/IsPareto, hypervolume/best-objective traces) and the logged Iteration index are computed over current-context observations only; ObservationsPerEvaluation.csv gains a Context column.
• New BoForUnityManager inspector section with live validation and fail-fast startup checks; contextual mode is BoTorch-only (CABOP rejects it with a clear error).
• Worked around a BoTorch LCEMGP task-kernel dimensionality issue when context_emb_feature is provided.

CABOP Fixes

• Fixed a critical parameter-ordering bug: with multiple CABOP groups whose parameters interleave in declaration order, parameter values were silently assigned to the wrong parameter names (in Unity payloads, observation logs, and warm-start data). Vectors now always follow parameter declaration order.
• Fixed a spurious AssertionError when the acquisition optimizer landed exactly on a parameter bound (floating-point overshoot); proposals are now clamped to bounds.
• IsBest/IsPareto marker flags are now derived from full-precision scalarized values instead of the rounded CSV values, and no longer re-scan the whole CSV every iteration.
• Zero-configured costs and degenerate GP predictions no longer produce division-by-zero in the acquisition function.
• The CABOP runtime now tolerates malformed/unrelated protocol messages the same way as the BoTorch backends.

Runtime and Tooling

• The optimizer's Python process now runs with PYTHONDONTWRITEBYTECODE=1, keeping __pycache__ folders out of StreamingAssets.
• Fixed a pandas dtype issue when writing IsBest flags during bo.py sampling runs.
• Context image paths configured on Windows now also resolve on macOS/Linux.
• MainThreadDispatcher no longer holds its queue lock while running actions, and one failing action can no longer abort the rest of the frame's queue.
• Removed the dead Optimizer.UpdateParameter API (it referenced CSV data that was never loaded).
• Added example warm-start CSVs with a Context column (ExampleContextInitData*.csv); documented the optional open_clip_torch/pillow dependencies in requirements.txt.
• Added a manually triggered Release workflow that tests, rolls this changelog, bumps bundleVersion, and creates the GitHub release.

Tests

• Added unit tests for the context protocol and embedding pipeline, plus real-BoTorch integration tests running full contextual BO/MOBO loops (skipped automatically on CI environments without torch).
• Added CABOP backend tests, including a multi-group ordering regression test and bounds/zero-cost edge cases (skipped when scipy/scikit-learn/loguru are unavailable).
• Added Unity EditMode tests for final-design selection and objective key matching.
• Consolidated the duplicated torch/botorch test stubs into a shared tests/_stubs.py.
• Added a weekly/manual full-stack-tests CI job that runs the complete suite against the real pinned dependency stack (CPU torch).

Documentation

• New README section 8.13 on contextual optimization and context embeddings (incl. ViT-G/14 guidance), new troubleshooting entries, and a note on Iteration numbering semantics for warm-started and contextual runs.