v1.6.0

Meta-BO backend (MetaTAF, TAF-EHVI)

• New MetaTAF optimizer backend (meta_mobo.py / meta_mobo_runtime.py): multi-objective Meta-Bayesian optimization that blends the current user's BoTorch qLogNEHVI model with hypervolume-improvement terms from population models built from prior participants' runs (Transfer Acquisition Function lifted to Pareto/hypervolume optimization, following Wistuba et al. 2018 and Liao et al. CHI 2024, including their population-weight decay γ(t)). Population models that disagree with the current user's observed Pareto rankings are automatically down-weighted; with zero valid sources the acquisition degenerates exactly to plain multi-objective BO.
• The multi-objective optimizers (mobo_botorch, mobo_taf, acquisition/taf_mo_ehvi) live in the openbo package (MIT, © Yi-Chi Liao; fork with correctness fixes and the new multi-objective code) — an optional dependency installed only for this backend; the runtime fails fast with the install command when missing. Not added to the auto-installed requirements.txt (needs git; documented in README 8.14 and docs/meta-taf-student-guide.md).
• New offline tool meta_train.py converts completed runs' ObservationsPerEvaluation.csv into population-model artifacts (per-objective GP fits, normalized into the canonical frame, self-checked by reloading through the runtime's own loader).
• Every artifact carries the study frame (parameter/objective names, bounds, minimize flags); the runtime skips sources whose frame differs from the live study, field by field — transferring across mismatched bounds or a flipped minimize flag would silently import a rescaled or inverted response surface.
• New outputs per run: MetaWeightsPerEvaluation.csv (target weight, decay factor, and per-source weights per iteration) and MetaSourcesUsed/ (audit copy of the exact population models the run used).
• Unity integration: OptimizerBackend.MetaTAF, inspector section (source dir, weight mode TafR/TafM, rho, target weight, warmup, decay d1/d2), init-message fields, and fail-fast validation (multi-objective only; Warm Start and Contextual Optimization are rejected with actionable errors).
• Hardening: the backend isolates PyTorch's JIT extension cache per process (TORCH_EXTENSIONS_DIR), preventing an infinite startup hang caused by stale lock files after a killed run.

Shared frame modules

• Added bo_normalize.py: the canonical frame (parameters in [0,1]^d, objectives in [-1,1] maximization with the minimize-flag sign flip) extracted into one shared, dependency-light module. bo.py and mobo.py now delegate to it instead of each carrying their own copy; behaviour is unchanged and the objective format is an explicit argument rather than a module global.
• Added meta_fingerprint.py: canonical frame fingerprint + field-by-field difference reporting (used by meta_train.py and the MetaTAF runtime).

Fixed

• The contextual-optimization guards in PythonStarter, SocketNetwork, and the inspector now check for "backend is not BoTorch" instead of "backend is CABOP", so newly added backends cannot silently bypass the LCE-M restriction.

Tests

• 48 new tests (193 total): shared-frame parity against mobo.py's live transform, frame-compatibility detection, and a full protocol/CSV test of the MetaTAF runtime against a stubbed openbo (CI-safe: numpy+pandas only). The contextual/embedding suites are unchanged and pass.

Documentation

• README 8.14 (Meta-BO overview + troubleshooting rows) and docs/meta-taf-student-guide.md (student walkthrough: population-model workflow, settings, logged outputs, study-design guidance, citations).