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Agents-K1 transforms how we extract and reason about scientific knowledge, achieving superior performance in multi-hop reasoning tasks compared to existing methods.
Compiling external knowledge into structured, reusable skills boosts agent performance, achieving over 98% success rates in complex tasks.
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.
Forget painstakingly training a single massive model – HeteroFusion lets you Frankenstein a super-model by intelligently merging the strengths of Llama, Qwen, and Mistral.
