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Reversible action cycles enable self-verification in long-horizon planning, cutting state returning drift by 44% and boosting accuracy nearly 4x.
Privacy-sensitive visual and textual information can be recovered from deep-layer LVLM hidden states, exposing significant risks in collaborative inference.
Redirecting failure into actionable corrections boosts robotic manipulation success rates by nearly 32% while using an order of magnitude fewer training samples.
