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Creative agents can now maintain context and evolve projects in real-time, transforming how we approach multimodal production.
SearchEyes achieves state-of-the-art performance in multimodal search by unifying training data, environments, and rewards into a cohesive simulated world.
UniCoder achieves state-of-the-art visual-to-code generation by transforming blind exploration into guided policy improvement, setting a new standard for the field.
DOPD reveals that intelligently routing supervision based on advantage gaps can significantly enhance capability transfer in distillation, outperforming conventional methods.
InterleaveThinker transforms standard image generators into powerful tools for interleaved generation, achieving state-of-the-art performance while enhancing reasoning capabilities.
OpenSearch-VL offers a fully transparent recipe for training state-of-the-art multimodal search agents, finally democratizing access to a capability previously locked behind closed doors.
LLMs can now build playable games from scratch, thanks to a new framework that teaches them to scaffold stable architectures and systematically debug integration errors, not just patch syntax.
Language models are increasingly doing their real work in the "invisible" latent space, not the tokens we see.
By tightly coupling reasoning, searching, and generation, Unify-Agent demonstrates that agent-based modeling can substantially improve world knowledge grounding in image synthesis, rivaling closed-source models.
Image generation takes a leap towards real-world knowledge by training an agent that actively searches for and integrates external information, substantially boosting performance on knowledge-intensive tasks.
