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Provable system identification for partially observed linear dynamics is now possible even with up to 50% adversarial data poisoning.
Explicitly training LLMs to verbalize confidence scores and signal reasoning-time uncertainty unlocks better calibration, failure detection, and control in retrieval-augmented generation.