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AI Models Lack Human-Level Language Understanding, Capabilities Emerge

AI Models Lack Human-Level Language Understanding, Capabilities Emerge

Sep 11, 2025
MachineLearningMastery.com

Powerful AI language models demonstrate remarkable abilities, including reasoning and coding, yet still lack the deep language comprehension of humans, relying on statistical pattern matching rather than true understanding, while smaller models sometimes outperform larger ones on specific tasks.

Foundation Models Research
Floating-Point Math Quirk and Batch Variability Affect LLM Outputs

Floating-Point Math Quirk and Batch Variability Affect LLM Outputs

Sep 10, 2025
Thinking Machines Lab

A quirk in floating-point arithmetic and batch variability in large language models (LLMs) can lead to inconsistent outputs, but achieving deterministic inference through batch-invariant kernels for key operations like RMSNorm, matrix multiplication, and attention resolves these issues.

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