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This paper investigates how jailbreak attempts are encoded in the internal representations of large language models by analyzing token-level predictive entropy trajectories across layers using the logit lens. It finds that entropy dynamics at intermediate layers are more discriminative than aggregate statistics, providing a training-free detection method consistent across multiple models.
This paper investigates when chain-of-thought reasoning is beneficial for LLMs, showing that early-stage entropy dynamics reliably indicate reasoning utility, and introduces EDRM, a lightweight, training-free framework that adaptively selects inference strategies to achieve significant token savings while maintaining or improving accuracy.