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v2: calibrated metacognition as RL + inference-time budget + transfer eval
KinchiHeadline contribution - metacognitive_reward.py: auxiliary objective that rewards calibration (does actual <think> length match the predicted band?), difficulty awareness (long predictions on bugs, short on safe?), and coupling (every prediction tied to a real tool call?). Geometric structure is non-gameable; only optimum is honest, action-grounded difficulty assessment. - scripts/budget_processor.py: ThinkingBudgetProcessor (LogitsProcessor) that hard-caps <think> tokens per block and per episode at inference time. Closes the loop with the training-time signal: shape during GRPO -> learns to allocate; budget processor at inference -> degrades gracefully under arbitrary tightening. - transfer_eval.py + data/transfer_episodes.json: held-out non-CVE domain evaluation (5 PR-review episodes: race conditions, auth bypasses, tenant leaks, etc). Same allocation policy: F1 0.28 -> 1.00, thinking ratio 5.24x preserved. Demonstrates skill transfer. - scripts/generate_cali…51fd6a7