In the field of computational advertising, the integration of ads into the outputs of large language models (LLMs) presents an opportunity to support these services without compromising content integrity.

Advanced image editing techniques, particularly inpainting, are essential for seam-lessly removing unwanted elements while preserving visual integrity.

In this paper, we present a novel framework which accomplishes this by exploiting latent embedding spaces to define an objective function for an LLM in an iterative RL-driven process. As an example, consider the challenge of assisting content creators in generating valuable content within a recommender ecosystem (e.g., Y ouTube, Reddit, Spotify) [Boutilier et al., 2024].

In addition, we probe the biases of agent trust and differences in agent trust towards other LLM agents and humans.

OpenAI's new GPT-5 Instant and GPT-5 Thinking models show a 30% reduction in political bias compared to GPT-4o, according to an internal report obtained by Fox News Digital.

In this work, we propose a novel calibration method that can be used to combat hallucinations.