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Princigram achieves a new standard in scientific diagram generation, ensuring physical accuracy through a structured reasoning framework that traditional models lack.
Intern-S2-Preview-397B not only excels in multimodal scientific reasoning but also enhances biological instruction performance without altering its foundational architecture.
Current LLMs only achieve 27.3% accuracy in reasoning about scientific lineage, revealing a critical gap in their compositional capabilities.
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.
