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Imperial College London
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Visual features can be the game-changer in recommendation systems, but they鈥檙e often overlooked鈥擱EVEAL flips the script by making them a focal point.
LLMs can drive significant gains in ad systems, not just as rankers or retrievers, but as complementary predictors that forecast likely advertisers from user profiles.
Seedance 2.0 leapfrogs existing models by unifying multi-modal inputs (text, image, audio, video) into a single architecture for generating high-quality, longer-duration audio-video content.
LLMs still fail to demonstrate expert-level proficiency, achieving only ~66% success on a new benchmark of real-world professional tasks spanning finance, healthcare, and law.
Recommendation systems can now systematically debias engagement signals across user, content, and model dimensions using a lightweight, in-model approach, leading to more accurate value models and stable ecosystem dynamics.
Image generation models can now reason about spatial relationships with significantly improved accuracy thanks to a novel reinforcement learning framework that iteratively refines images based on spatial consistency checks.
