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Looping language models isn't just for single agents anymore: Recursive Multi-Agent Systems (RecursiveMAS) show that agent collaboration itself can be scaled through recursion, yielding faster and more efficient problem-solving.
Current multimodal LLMs still struggle to integrate information and reason critically when assessed on real scientific papers, despite progress on isolated tasks.
Diffusion language models can achieve faster decoding and better accuracy by learning directly from the token reveal order suggested by a lightweight autoregressive teacher, without expensive distillation.
