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Achieving trillion-parameter performance with just 35 billion parameters by scaling agent horizons reveals a new frontier in model efficiency.
Agents-K1 transforms how we extract and reason about scientific knowledge, achieving superior performance in multi-hop reasoning tasks compared to existing methods.
MLEvolve not only breaks the barriers of information isolation in MLE agents but also achieves state-of-the-art performance in algorithm discovery within half the standard runtime.
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.
