v1.0.1

Fixed

• Isotonic calibrator: pool tied covariates before PAVA. src/aura/calibration.py
  did not pre-pool equal confidence values, so on duplicate covariates the fit was not
  L2-optimal (e.g. returned 0.5 instead of 0.333 on a [0,1,0] tie). Now pre-pools
  ties before the pool-adjacent-violators pass. The paper's published ECE/AUC are
  unchanged — real-data ties shift ECE by < 1e-6, below the reported 4-dp
  precision, and the figure reproduce-checks still pass bit-exactly. Regression test added.

Added

• Public API surface exported from aura.__init__: IsotonicConfidenceCalibrator,
  conformal_prune_certificate, certified_lod_plan, and related symbols.
• CLI error boundaries: main() wraps dispatch, emitting aura: error: … with exit 1
  on bad input (AURA_DEBUG=1 re-raises); glTF .bin buffer round-trip decode test.
• matplotlib/imageio/pillow moved to the dev extra; CI installs .[dev].

Changed

• Certified LOD — Bonferroni over the non-trivial levels only (tighter, still valid).
  The certified LOD ladder (src/aura/lod.py) previously split α over all K levels
  (α' = α/K = 0.025), but the full-keep (f = 1.00) level is a deterministic
  statement (ε = 0, nothing pruned, no interval) that consumes no error budget. By the
  union bound only the R = K−1 = 3 non-trivial random levels need correcting, so
  α' = α/R = α/3 ≈ 0.0333. This is strictly tighter — the family-wise 1−α = 0.9
  guarantee is unchanged, but every non-trivial ε_k shrinks slightly (e.g. Truck@10%
  0.3342 → 0.3340, Room@10% 0.4432 → 0.4429). Regenerated outputs/lod_certified.json
  through experiments/lod_certified_eval.py; all 16 bounds still hold (12 non-trivial
  • 4 trivial) on the disjoint eval halves, zero violations. Propagated to the paper
    (Table 6, Fig 9, family-wise paragraph), README, docs/P4_CERTIFIED_LOD.md, REPRODUCE.md,
    the certified_lod publication gate (now checks α' = α/R), and the LOD unit tests.