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Reinforcement learning can transform how models autonomously verify scientific accuracy, achieving near-human levels of reasoning in error detection.
Today's best vision-language models are surprisingly bad at reading scientific figures, failing to match expert-level reasoning on a new benchmark of experimental images.
LLMs can now generate research roadmaps that are 8% better and 84% faster than human experts, thanks to a novel multi-agent system.
Retrieval improvements don't always boost reasoning in RAG systems, but NeocorRAG's evidence chains can fix that, achieving SOTA with 20% fewer tokens.
