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An approach that does not scale to wide floors where robots must operate multiple machines with various procedures, where human operators can flexibly follow manuals that specify ordered steps, preconditions is suggested.
Comparisons support complementary benefits of feature-wise gated tactile fusion and training-only predictive supervision of TacSushi, a tactile-grounded, Cosmos3-based world-action policy that learns from recorded future consequences while acting on current observations.
Vibe Patenting, an end-to-end patent-drafting testbed for AI-agent evaluation, validates the judge against independent evaluation by a professional patent attorney and finds meaningful but strongly metric-dependent agreement and systematic calibration differences.
CAPTAIN (Context-Augmented Perplexity-based Threat Activity log detectIoN), a perplexity-based detector that leverages general, pre-trained language models with minimal, domain-agnostic preprocessing, enabling robust scoring of long, minimally processed log entries, is proposed.
CALM (Controller-Aware Language Models), a post-training framework that explicitly places controllers in the training loop, is introduced, showing that controller-aware post-training improves generalization across inference-time workflows beyond single-controller optimization.
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