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Contextual safety filtering (CSF), a training-free filter that grounds natural-language safety rules in safe and unsafe reference trajectories produced by the generator, is introduced.
A reweighting equivalence is derived between the proposed information-bottleneck objectives for action-conditioned InfoNCE and action prediction, motivating InfoNCE as a proxy for layer quality and providing the most consistent positive association with policy success among four evaluated proxies.
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