Longer contexts present a new attack surface for adversarial attacks. In search of a "fruit-fly" of long-context vulnerabilities, we study Many-shot Jailbreaking (MSJ; Figure 1), a simple yet effective and scalable jailbreak.

The application of reinforcement learning (RL) involving interactions with individuals has grown significantly in recent years.

In label-noise learning, the noise transition matrix reveals how an instance transitions from its clean label to its noisy label. Accurately estimating an instance's noise transition matrix is crucial for estimating its clean label.

This allows us to interpret the latent variables as object positions and lets us train the network without supervision.

A key question in this growing field is to provide rigorous conditions under which latent factors can be identified and thus, potentially learned.

In this paper, we propose DASpeech, a non-autoregressive direct S2ST model which realizes both fast and high-quality S2ST.

Much of the promise of these models relies on learned concepts being high quality .