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This work proposes Ecdysis, which aggregates failure evidence across task instances before promoting recurring failure patterns into persistent harness evolution, biasing evolution toward repairs that are more likely to generalize beyond individual model behaviors.
This work proposes $O^2-CritiCuRL, a novel curriculum reinforcement learning framework that introduces critical-step awareness through an iterative offline-online paradigm, and employs a progressive step-level reinforcement learning strategy, where truncated chains guide the model to infer missing steps and refine its...
An exploratory study involving over 30,000 real-world agent interaction records and 45 stand-up comedians reveals practical safety concerns in LLM-based content humorization, and proposes a prompt injection attack that exploits latent risks in humor-based defenses.
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