feat(evals): v2 lanes — monotonic-learning + provenance
monotonic-learning v2:
public/v2 — 5 domains × 3-4 probes × 20 cycles (377 ops)
domains: truth, light, wisdom, order, memory
max_regression=0.0, floor_score=1.0
holdouts/v2 — 4 distinct domains × 3-4 probes × 18 cycles (284 ops)
domains: creation, knowledge, reason, spirit
max_regression=0.0, floor_score=1.0
Demonstrates the structural claim (zero regression on prior domains
as new ones accumulate) at substantially deeper cycle count and
broader domain breadth than v1.
provenance v2:
public/v2 — 30 cases across pack_axiom, vault_recall, teaching, mixed
deeper priming (3-5 turns), mixed-kind cases combining
pack + vault + teaching sources in one probe
source_attribution=1.0, source_validity=1.0,
replay_determinism=1.0, input_sensitivity=1.0
holdouts/v2 — 20 cases on distinct vocabulary
all sub-metrics 1.0
Generator: scripts/generate_monotonic_cases.py extended with three
extra domain probe sets (order, memory, reason, spirit) and split
definitions for v2.
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