v1.0.0

Upgrade notes

• Use PolyStepOptimizer for neural networks and PolyStep for standalone objectives.
• Replace diagonal CMA with AdaptiveSubspace, tempered softmax with SoftmaxSolver,
  and auto_epsilon with ProgressiveEpsilon. Probe reuse has been removed.
• Checkpoints use format 6. Rebuild the optimizer from saved model weights when
  upgrading from 0.13; older optimizer checkpoints cannot be loaded.
• Some low-level exports and experiment helpers moved or were removed. The
  migration guide lists replacements and
  changes to evaluation, configuration, and callbacks.

Fixes

• Preserve live model weights, buffers, momentum, and trust-region state across
  evaluation, subspace updates, and checkpoint restoration.
• Handle nonfinite costs, tiny transport rows, integer losses, and half-precision
  reductions consistently. Sparse CUDA projections repeat in deterministic mode.
• Accept device strings in rotation sampling and float32 masks in half-precision
  CUDA attention.
• Correct baseline budgets, tied ranks, rollout accounting, and download handling.

Performance and examples

• Reuse unchanged forward work, update only affected weights, and size candidate
  chunks from measured activations. Speed up blockwise, factored, and adaptive steps.
• Reduce sparse projection storage and host synchronization; compile Sinkhorn loops.
• Refresh all 11 examples, their evaluation paths, and their documented settings.
  See performance for measurements and tradeoffs.
• Consolidate redundant tests and keep release validation focused on the built wheel.