In a multiplayer networked game, actions for players with higher latencies are received and (potentially) acted upon later than players with lower latencies, leading to unfairness, especially important in competitive games. Time delay is a latency compensation technique that can mitigate this unfairness by adding latency to players with lower latency so that all players experience the same latency. Although this provides equal latency to all players, it unnecessarily degrades the responsiveness for the lower-latency players when the players are not interacting.

Frametime spikes can disrupt gameplay in games, affecting both player performance and experience, but the effects of these spikes on navigation based tasks is not well-studied. This work investigates how frametime spikes impact players performing navigation-focused tasks in a platformer game. An open-source platformer game, SuperTux Classic, was modified to deliberately create spikes in frametimes when players performed certain actions, while recording performance and assessing quality of experience (QoE).

We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architecture. Trained with a joint AR-diffusion objective, Nemotron-Labs-Diffusion can switch modes to sustain high throughput across deployment settings and concurrency levels. Our study shows that (1) AR and diffusion objectives are complementary: diffusion improves lookahead planning, while AR provides left-to-right linguistic priors.