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LLMs exhibit a troubling tendency to prioritize code generation over genuine architectural understanding, revealing a critical gap in their evaluation metrics.
Ditch the one-size-fits-all code intelligence: modeling individual developer behaviors inside the IDE boosts Q&A accuracy by 33.8%.
Image editing can boost multimodal LLM reasoning by 5 points, but only if the editor is trained to understand the reasoning process itself.
Diffusion models can now reason their way through complex spatial tasks with near-perfect accuracy, thanks to a new framework that unlocks chain-of-thought reasoning within the latent space.
