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Robotic world models can now learn from diverse, yet confident, alternative futures, achieving state-of-the-art prediction quality and extending predictive horizons.
Treating historical behavior and current requests as homogeneous context units, UniCon achieves a 3.09% lift in revenue and 2.07% in CTR, outperforming traditional models.
Streaming video MLLMs perform far better when historical context is actively internalized into evolving latent tokens rather than queried as passive external visual buffers.
Cultural cues can mislead language models into choosing the wrong answers even when they select the correct normative framework, exposing a significant knowledge gap in AI systems.
Flipping just a few bits can inflate LLM output by over 5900%, revealing a critical vulnerability in Mixture-of-Experts architectures.
