v0.8.0 — Measured training results

Calibra's predictions (calibra benchmark) were always simulated — a heuristic outcome model plus linear GPU-hour scaling. That's useful for a first pass, but not evidence. This release adds the other half: a way to record what a design partner's real training runs actually cost and achieved, and to fold those measured numbers into the benchmark report wherever they're available, so a report is never presented as a validated result when parts of it are still predictions.

Added

• calibra experiment record — logs one training run's result (GPU-hours, wall-clock time, energy, eval success rate) against the design-partner protocol's full / random / calibra conditions at a given retention percentage. Training itself runs in the partner's own pipeline; this only records the outcome. Stored locally as JSON Lines at ~/.calibra/experiments.jsonl — never synced to any network endpoint. New module calibra/experiment_log.py.
• calibra experiment list / calibra experiment report — list recorded runs, or print the full retention-curve comparison for one experiment, including the Calibra-vs-random delta at each level and which (retention%, condition) pairs the protocol still expects but haven't been recorded yet. New CLI handler calibra/experiment.py.
• calibra benchmark --sweep — runs the full design-partner retention curve (default 10/25/50/75/100%, override with --fractions) in one shot instead of a single --keep fraction.
• calibra benchmark --experiment-id ID — substitutes real measured GPU-hours / eval success rate from calibra experiment record into the benchmark report wherever a matching condition and retention level has been logged, falling back to simulated values for anything not yet measured.
• Benchmark reports now carry a status: SIMULATED (nothing measured — a prediction), PARTIAL MEASUREMENT (some conditions measured, others still simulated — not safe to report as validated), or CASE STUDY / VALIDATED (full, random, and Calibra all backed by real recorded training runs). Every number is individually tagged (measured) or (simulated).
• Compute savings are now computed from GPU-hours rather than episode-count reduction, since the two can diverge once real measured numbers are mixed in.

See docs/commands.md's calibra benchmark and calibra experiment sections for the full partner workflow.

Fixed

• compute_trajectory_entropy force-cast actions to float32 before np.histogram, which could collapse distinct values (and raise "Too many bins for data range") on large-offset, sub-millimeter-variance action columns — exactly the near-duplicate-trajectory case the function is meant to detect. Now computed in float64. Reported via a Reddit user testing on real data.

Full Changelog: v0.7.1...v0.8.0