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LLMs without hardware feedback fail to deploy, but a new iterative optimization method achieves 250x compression with less than 3.3% accuracy loss in real-world applications.
Today's best LLMs fail spectacularly at long-horizon reasoning, achieving under 10% accuracy on a new benchmark designed to isolate this critical capability.
LLMs don't just change *how* we write, they subtly distort *what* we mean, leading to blander, less insightful, and potentially biased communication.
