reduce_current_q_stats() re-ran 10 kernels (mamba2_step, predictive_coding,
regime_dropout, ISV forward/gate/route, recursive_confidence, trade_plan,
plan_noise, risk_budget) every training step OUTSIDE the graph replay.
These ops already executed inside the graph — the logit buffers are current.
The redundant execution added ~4s of GPU compute per step (matching the
graph replay cost), doubling total epoch time.
Fix: compute expected_q directly from the existing logit buffers.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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